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DTSTART;VALUE=DATE:20240319
DTEND;VALUE=DATE:20240323
DTSTAMP:20260419T064420
CREATED:20231129T180550Z
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SUMMARY:ONLINE COURSE - An Introduction to Spatial Eco-Phylogenetics and Comparative Methods (SECM01) This course will be delivered live
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nTuesday\, March 19th\, 2024\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE FORMAT\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCOURSE PROGRAM\nTIME ZONE – CET (Central European Time) – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				About This Course\n				\nIn this course we introduce phylogenetic analyses in a spatial context. Phylogenetic analyses often imply a high number of species for which phylogenetic information is unavailable\, hence we begin by providing an overview on modern techniques to incorporate phylogenetic uncertainty in the analyses (day 1). We then cover the most popular analyses in the spatial phylogenetics discipline (day 2)\, with particular focus on the canonical analysis of neo- and paleo-endemism (CANAPE). The second part of the course will be devoted to integrating phylogenetic information into models of geographic distribution of species (day 3)\, followed by an overview of recent advances to improve ecological forecasts using phylogenetic mixed models in a Bayesian framework (day 4).  \n\nBy the end of the course\, participants should: \n\nKnow how to expand incomplete phylogenies based on taxonomic information and customizing simulation parameters for optimal expansion.\nUnderstand the metrics and concepts used in spatial phylogenetics (i.e. phylogenetic alpha and beta diversity\, phylogenetic endemism)\, interpret them critically\, and assess pros and cons of analytical techniques.\nCalculate phylogenetic predictors that can be included as covariates in Species Distribution or Niche Models.\nUnderstand and implement the phylogenetic mixed model (PMM) and translate its predictions into a spatial context.\n\n			\n				\n				\n				\n				\n				Intended Audiences\n				This course is aimed at anyone who wishes to introduce into spatial phylogenetics and comparative analyses (in general and within a spatial context in particular) \n			\n				\n				\n				\n				\n				Course Details\n				Venue – Delivered remotelyAvailability – 30 placesDuration – 5 daysContact hours – Approx. 35 hoursECT’s – Equal to 3 ECT’sLanguage – English \n			\n				\n				\n				\n				\n				Teaching Format\n				The course will be hands-on and workshop based. Throughout each day\, there will be some introductory remarks for each new topic\, introducing and explaining key concepts. \nAll the sessions will be video recorded\, and made available immediately on a private video hosting website. Any materials\, such as slides\, data sets\, etc.\, will be shared with the attendees. \n			\n				\n				\n				\n				\n				Assumed quantative knowledge\n				\nWe will assume general familiarity with the very basics of statistics (e.g. summary statistics\, distributions). As this is an introductory course\, no phylogenetic background is required. \n\n			\n				\n				\n				\n				\n				Assumed computer background\n				We will assume general familiarity with R elementary operations (e.g. package sourcing\, data importing and exporting\, object indexing) and some familiarity with programming in R (writing code). \n			\n				\n				\n				\n				\n				Equipment and software requirements\n				\nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer). \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students. \n\n\n\n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\nPLEASE READ – CANCELLATION POLICY \n\n\n\n\nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \n\n\n\n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n\n\n			\n				\n				\n				\n				\n				\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE PROGRAMME\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Tuesday 19th\n				Classes from 8:00 to 13:00 CET \nDAY 1 \nExpansion of molecular trees using taxonomic information and fundamental metrics of phylogenetic structure \n\nSoftware for tree expansion exercises; randtip\, PhyloMaker\nAn overview of the fundamental metrics of phylogenetic structure. Null models.\n\n  \n			\n				\n				\n				\n				\n				Wednesday 20th\n				Classes from 8:00 to 13:00 CET \nDAY 2 \nSpatial Phylogenetics \n\nCanonical analysis of neo- and paleo- endemism. Metrics\, rationale\, workflow\, and implementation.\n\n			\n				\n				\n				\n				\n				Thursday 21st\n				Classes from 8:00 to 13:00 CET \nDAY 3 \nPhylogenetic Species Distribution Models \n\nPutting phylogenies in the geography: the imprints of evolutionary relationships in distribution models.\nCombining phylogenies with co-occurrence to infer spatial phylogenetic predictors.\nFitting\, evaluating and interpreting Phylogenetic-SDMs.\n\n			\n				\n				\n				\n				\n				Friday 22nd\n				Classes from 8:00 to 13:00 CET \nDAY 4 \nBeyond PGLS – Bayes and more \n\nMost common phylogenetic modelling approaches: PGLS\nPGLMM\nThe phylogenetic mixed model (PMM) in a Bayesian framework\n\n			\n			\n				\n				\n				\n				\n				Course Instructor\n \nDr. Rafael Molina Venegas \nThe scientific career of Rafael Molina Venegas revolves around three research lines pertaining to (1) the ecological and evolutionary mechanisms that jointly shape species assemblages at the community and macroecological scales\, (2) the development\, improvement\, and assessment of phylogenetic methods\, and (3) the links between biodiversity and human well-being. While these lines represent clearly differentiated research interests\, phylogenetics is a cross-cutting background for all of them. Considering that plants are his true passion in science\, he defines himself as a Phylogenetic Plant Ecologist. I personal page can be found here \nResearchGateGoogleScholar \n			\n				\n				\n				\n				\n				Course Instructor\n \nDr. Morales Castilla Ignacio \nDr. Morales-Castilla is a biogeographer and macroecologist interested in the spatial-temporal distribution of biodiversity. His research program aims to: (1) disentangle the relative roles of evolution and ecology as drivers of community structure\, (2) understand how different aspects of the species’ niches are evolutionarily conserved and\, (3) enhance models of biotic interactions and/or species distributions by integrating phylogenetic\, functional and geographic information. You can check his publication record at the links provided above. You can find hiss homepage here \nResearchGateGoogleScholarORCIDGitHub
URL:https://prstats.preprodw.com/course/an-introduction-to-spatial-eco-phylogenetics-and-comparative-methods-secm01/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20240312
DTEND;VALUE=DATE:20240315
DTSTAMP:20260419T064420
CREATED:20230915T125012Z
LAST-MODIFIED:20240118T153339Z
UID:10000437-1710201600-1710460799@prstats.preprodw.com
SUMMARY:CURSO ONLINE – Introdução a Modelos Mistos usando R e R Studio (IMMR08) Este curso será ministrado ao vivo
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Data do Evento \nTerça-feira\, 12th Março\, 2024\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				FORMATO DO CURSO\nEste é um ‘CURSO AO VIVO’ – o instructor ministrará as aulas e treinará os participantes através de aulas práticas por meio de uma conexão por video; uma boa conexão com a internet é essencial. \nPROGRAMA\nFUSO HORÁRIO – Horário de Brasília – porém\, todas as sessões serão gravadas e disponibilizadas online\, permitindo que participantes de outros fusos horários também acompanhem. \nPor favor\, envie um email para oliverhooker@prstatistics.com para maiores detalhes\, ou para discutir como Podemos acomodá-lo(a). \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				DETALHES DO CURSO\n				\nEste curso fornece uma introdução teórica e prática a modelos mistos\, também conhecidos como modelos multi-nível ou hierárquicos. Nosso foco primário será em modelos lineares mistos\, porém também cobriremos modelos lineares generalizados mistos. Também descreveremos abordagens Bayesianas para modelos mistos. Começaremos com modelos de efeitos aleatórios. Esses modelos mostram\, com clareza\, como os modelos mistos são\, na verdade\, “modelos de modelos”. Também\, modelos de efeitos aleatórios servem como uma base sólida para auxiliar o entendimento de modelos mistos. Nós também trataremos de conceitos importantes relacionados a shrinkage\, ou “redução/encolhimento” dos efeitos aleatórios\, e correlação intraclasse. Então\, cobriremos modelos lineares mistos\, com foco particular em modelos de intercepto e/ou coeficientes angulares aleatórios. Depois\, cobriremos modelos mistos para dados com estrutura aninhada ou cruzada\, bem como preditores de nível de grupo. Então\, trataremos de modelos lineares generalizados mistos e como utilizar efeitos aleatórios a nível observacional para acomodar superdispersão. Por fim\, cobriremos uma breve introdução à abordagem Bayesiana por meio do pacote brms. \n\n			\n				\n				\n				\n				\n				PÚBLICO ALVO\n				\nEste curso tem como público alvo qualquer pessoa que estiver interessada em utilizar R para ciência de dados ou estatística. R é amplamente utilizado em todas as áreas da pesquisa científica\, bem como nos setores público e privado. \n\n			\n				\n				\n				\n				\n				LOCAL\n				Ministrado remotamente.\n			\n				\n				\n				\n				\n				NFORMAÇÃO DO CURSO\n				Fuso horário – Horário de Brasília \nDisponibilidade – A definir \nDuração – 3 x 1/2 dias \nHoras de contato – Aprox. 12 horas \nCréditos – Equivalente a 1 crédito \nIdioma – Português\n			\n				\n				\n				\n				\n				FORMATO DE ENSINO\n				Este curso será um workshop prático. Para cada tópico\, haverá uma apresentação estilo aula\, isto é\, utilizando slides ou lousa eletrônica\, para introduzir conceitos-chaves e teoria. Então\, apresentaremos como realizar as variadas análises estatísticas utilizando o R. Todo o código que o instrutor fornecerá durante as sessões será disponibilizado em um repositório público do GitHub após as sessões. \nNo início de cada dia\, nos certificaremos de que todos estão confortáveis com o uso do Zoom e discutiremos os procedimentos para fazer perguntas e postar comentários. \nEmbora não seja estritamente necessário\, utilizar um monitor grande (ou preferivelmente um segundo monitor) tornará a experiencia de aprendizado melhor\, porque você poderá ver meu R Studio e seu próprio R Studio simultaneamente. \nTodas as sessões serão gravadas e disponibilizadas imediatamente em um link protegido por senha. \nTodos os materiais\, como slides\, conjuntos de dados\, etc.\, serão compartilhados via GitHub. \n			\n				\n				\n				\n				\n				CONHECIMENTO QUANTITATIVO NECESSÁRIO\n				\nUm entendimento básico de conceitos estatísticos chaves. Especificamente\, modelos de regressão linear\, significância estatística e testes de hipóteses. \n\n			\n				\n				\n				\n				\n				CONHECIMENTO COMPUTACIONAL NECESSÁRIO\n				Familiaridade com o R. Importar/exportar dados\, manipular data frames\, ajustar modelos estatísticos básicos e gerar gráficos simples. \n			\n				\n				\n				\n				\n				REQUERIMENTOS DE EQUIPAMENTO E SOFTWARE\n				\nUm computador com o R e R Studio instalados é necessário. R e R Studio são ambos gratuitos e disponíveis para PC\, Mac e Linux.Participantes devem poder instalar softwares adicionais em seus computadores durante o curso (por favor\, certifique-se de que você tem direitos de administrador em seu computador).Um monitor grande e uma segunda tela\, embora não seja absolutamente necessário\, melhorará a experiência de aprendizado. Participantes também são encorajados a manter suas câmeras ligadas para aumentar a interação entre o instrutor e os demais participantes. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer). \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students. \n\n\n\n\n\n\nFaça o download do R \n\n\nFaça o download do RStudio \n\n\nFaça o download do Zoom \n\n\n\n  \n\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				POR FAVOR\, LEIA – POLÍTICA DE CANCELAMENTO \nCancelamentos são aceitos até 28 dias antes da data de início do curso e estão sujeitos a uma taxa de cancelamento de 25%. Cancelamentos após esse período podem ser considerados\, contate oliverhooker@pr<span class=”s1″>statistics</span>.com. Falha em participar do curso resultará no custo completo do curso sendo cobrado. No evento improvável de o curso ser cancelado devido a imprevistos\, um reembolso completo das taxas do curso será creditado. \n			\n				\n				\n				\n				\n				Se você estiver incerto em relação à adequabilidade do curso\, por favor entre em contato por email para saber mais oliverhooker@prstatistics.com \n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PROGRAMA DO CURSO\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Terça-feira 12th\n				Aulas das 14:00 às 18:00 (Horário de Brasília) \nDIA 1 \n\nTópico 1: Modelos de efeitos aleatórios. A característica que define modelos mistos é o fato de que eles são “modelos de modelos”. Começaremos utilizando modelos binomiais de efeitos aleatórios para ilustrar esse conceito. Especificamente\, mostramos como modelos mistos são modelos da variabilidade em modelos de diferentes clusters\, ou grupos de dados. \n\n\nTópico 2: Modelo normal de efeitos aleatórios. Esses são modelos chaves para entender o modelo misto de uma maneira mais geral. Aqui\, cobriremos os conceitos estatísticos de shrinkage e de correlação intraclasse. \n\n			\n				\n				\n				\n				\n				Quarta-feira 13th\n				Aulas das 14:00 às 18:00 (Horário de Brasília) \nTópico 3: Modelo linear misto. Agora\, cobriremos os modelos lineares mistos. Lidamos\, especificamente\, com os casos de interceptos e/ou coeficientes angulares aleatórios. \nTópico 4: Modelos mistos para dados com estrutura aninhada. Aqui\, consideramos modelos para dados com estrutura aninhada\, isto é\, grupos de grupos. Como um exemplo\, aplicaremos modelos mistos a dados de estudantes dentro de classes dentro de escolas\, onde modelamos a variabilidade dos efeitos entre classes e entre escolas. \nTópico 5: Modelos mistos para dados com estrutura cruzada. Em alguns modelos mistos\, cada observação ocorre em múltiplos grupos\, que não estão aninhados. Por exemplo\, animais podem ser membros de diferentes grupos taxonômicos e em diferentes locais\, mas os grupos taxonômicos não são subconjuntos dos locais\, ou vice-versa. \n			\n				\n				\n				\n				\n				Quinta-feira 14th\n				Aulas das 14:00 às 18:00 (Horário de Brasília) \nTópico 6: Preditores a nível de grupo. Em alguns modelos mistos\, variáveis preditoras podem estar associadas a indivíduos ou a grupos. Nesta seção\, consideramos como lidar com essas duas situações. \nTópico 7: Modelos lineares generalizados mistos. Aqui\, estendemos o modelo linear misto para a família exponencial de distribuições e mostramos um exemplo usando o MLG misto Poisson. Também abordamos como acomodar superdispersão por meio de efeitos aleatórios a nível individual. \nTópico 8: Modelos mistos Bayesianos. Todos os modelos considerados podem ser ajustados utilizando a abordagem Bayesiana. Aqui\, fornecemos uma breve introdução a modelos Bayesianos e como ajustar os modelos mistos que consideramos durante o curso utilizando o pacote brms. \n			\n			\n				\n				\n				\n				\n				Instrutor do curso\n \nDr. Rafael De Andrade Moral \nRafael é Professor Associado de Estatística na Maynooth University\, Irlanda. Bacharel em Biologia e Doutor em Estatística pela Universidade de São Paulo\, Rafael tem interesse em ensino e pesquisa em modelagem estatística aplicada a ecologia\, manejo da fauna silvestre\, agricultura e ciências ambientais. Como diretor do grupo de pesquisa em ecologia teórica e estatística\, Rafael reúne uma comunidade de pesquisadores que utilizam ferramentas matemáticas e estatísticas para melhor compreenderem o mundo natural. Como uma estratégia de ensino alternativa\, Rafael vem produzindo vídeos musicais e paródias para promover a Estatística nas mídias sociais e na sala de aula. Sua página pessoal pode ser encontrada aqui. \nResearchGateGoogleScholarORCIDGitHub
URL:https://prstats.preprodw.com/course/introducao-a-modelos-mistos-usando-r-e-r-studio-immr08/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2023/09/Screenshot-2023-09-15-at-13.59.26.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240220
DTEND;VALUE=DATE:20240223
DTSTAMP:20260419T064420
CREATED:20230829T210013Z
LAST-MODIFIED:20240118T152938Z
UID:10000436-1708387200-1708646399@prstats.preprodw.com
SUMMARY:CURSO ONLINE – Introdução a Modelos Lineares Generalizados usando R e R Studio (IGLM07) Este curso será ministrado ao vivo
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Data do Evento \nTerça-feira\, 20th Fevereiro\, 2024\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				FORMATO DO CURSO\nEste é um ‘CURSO AO VIVO’ – o instructor ministrará as aulas e treinará os participantes através de aulas práticas por meio de uma conexão por video; uma boa conexão com a internet é essencial. \nPROGRAMA\nFUSO HORÁRIO – Horário de Brasília – porém\, todas as sessões serão gravadas e disponibilizadas online\, permitindo que participantes de outros fusos horários também acompanhem. \nPor favor\, envie um email para oliverhooker@prstatistics.com para maiores detalhes\, ou para discutir como Podemos acomodá-lo(a). \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				DETALHES DO CURSO\n				Este curso fornece uma introdução teórica e prática aos modelos lineares generalizados usando o R. Modelos lineares generalizados (MLGs) são generalizações de modelos de regressão linear para situações em que a variável resposta é\, por exemplo\, binária\, ou categórica\, ou de contagem\, etc. Os modelos específicos que apresentaremos incluem regressão logística binária\, binomial e categórica\, regressão Poisson e binomial negativa para variáveis de contagem. Também apresentaremos modelos de regressão de Poisson e binomial negativo inflacionados de zeros. Começaremos com uma breve recapitulação do modelo linear normal. Entender esse modelo é vital para um entendimento apropriado de como ele pode ser generalizado na teoria dos MLGs. Depois\, introduziremos o modelo de regressão logística binário amplamente utilizado\, que é um modelo de regressão para quando a variável resposta é binária. Depois\, apresentamos o caso da regressão logística binomial (duas categorias)\, e por fim multinomial\, para modelar respostas politômicas\, isto é\, que podem integrar mais de duas categorias. Depois apresentaremos a regressão Poisson\, que é amplamente utilizada para modelar variáveis respostas de contagem (isto é\, o número de vezes que algo aconteceu). Depois apresentaremos modelos de superdispersão\, que acomodam uma variabilidade maior do que a esperada pelos modelos de Poisson e binomial. Apresentaremos os modelos de quase-verossimilhança\, binomial negativo e beta-binomial\, para dados de contagens e proporções discretas\, respectivamente. Por fim\, apresentaremos modelos de Poisson e binomial negativo inflacionados de zeros\, para dados de contagem com um excesso de observações nulas.\n			\n				\n				\n				\n				\n				PÚBLICO ALVO\n				Este curso tem como público alvo qualquer pessoa que estiver interessada em utilizar R para ciência de dados ou estatística. R é amplamente utilizado em todas as áreas da pesquisa científica\, bem como nos setores público e privado.\n			\n				\n				\n				\n				\n				LOCAL\n				Ministrado remotamente.\n			\n				\n				\n				\n				\n				NFORMAÇÃO DO CURSO\n				Fuso horário – Horário de Brasília \nDisponibilidade – A definir \nDuração – 3 x 1/2 dias \nHoras de contato – Aprox. 12 horas \nCréditos – Equivalente a 1 crédito \nIdioma – Português\n			\n				\n				\n				\n				\n				FORMATO DE ENSINO\n				Este curso será um workshop prático. Para cada tópico\, haverá uma apresentação estilo aula\, isto é\, utilizando slides ou lousa eletrônica\, para introduzir conceitos-chaves e teoria. Então\, apresentaremos como realizar as variadas análises estatísticas utilizando o R. Todo o código que o instrutor fornecerá durante as sessões será disponibilizado em um repositório público do GitHub após as sessões. \nNo início de cada dia\, nos certificaremos de que todos estão confortáveis com o uso do Zoom e discutiremos os procedimentos para fazer perguntas e postar comentários. \nEmbora não seja estritamente necessário\, utilizar um monitor grande (ou preferivelmente um segundo monitor) tornará a experiencia de aprendizado melhor\, porque você poderá ver meu R Studio e seu próprio R Studio simultaneamente. \nTodas as sessões serão gravadas e disponibilizadas imediatamente em um link protegido por senha.  \nTodos os materiais\, como slides\, conjuntos de dados\, etc.\, serão compartilhados via GitHub.\n			\n				\n				\n				\n				\n				CONHECIMENTO QUANTITATIVO NECESSÁRIO\n				Um entendimento básico de conceitos estatísticos chaves. Especificamente\, modelos de regressão linear\, significância estatística e testes de hipóteses.\n			\n				\n				\n				\n				\n				CONHECIMENTO COMPUTACIONAL NECESSÁRIO\n				Familiaridade com o R. Importar/exportar dados\, manipular data frames\, ajustar modelos estatísticos básicos e gerar gráficos simples.\n			\n				\n				\n				\n				\n				REQUERIMENTOS DE EQUIPAMENTO E SOFTWARE\n				\nUm computador com o R e R Studio instalados é necessário. R e R Studio são ambos gratuitos e disponíveis para PC\, Mac e Linux.\nParticipantes devem poder instalar softwares adicionais em seus computadores durante o curso (por favor\, certifique-se de que você tem direitos de administrador em seu computador).\nUm monitor grande e uma segunda tela\, embora não seja absolutamente necessário\, melhorará a experiência de aprendizado. Participantes também são encorajados a manter suas câmeras ligadas para aumentar a interação entre o instrutor e os demais participantes. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer). \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students. \n\n\n\n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				POR FAVOR\, LEIA – POLÍTICA DE CANCELAMENTO \nCancelamentos são aceitos até 28 dias antes da data de início do curso e estão sujeitos a uma taxa de cancelamento de 25%. Cancelamentos após esse período podem ser considerados\, contate oliverhooker@pr<span class=”s1″>statistics</span>.com. Falha em participar do curso resultará no custo completo do curso sendo cobrado. No evento improvável de o curso ser cancelado devido a imprevistos\, um reembolso completo das taxas do curso será creditado. \n			\n				\n				\n				\n				\n				Se você estiver incerto em relação à adequabilidade do curso\, por favor entre em contato por email para saber mais oliverhooker@prstatistics.com \n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PROGRAMA DO CURSO\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Terça-feira 20th\n				Aulas das 14:00 às 18:00 (Horário de Brasília) \nDIA 1 \nTópico 1: O modelo linear geral. Começamos com uma recapitulação do modelo normal\, incluindo uso de variáveis preditoras. Embora esse modelo não seja o foco do curso\, é o pilar central no qual os modelos lineares generalizados estão baseados e\, portanto\, deve ser compreendido para que haja entendimento dos modelos lineares generalizados (MLGs). \nTópico 2: Regressão logística binária. Nosso primeiro MLG é o de regressão logística binária (ou Bernoulli)\, a ser utilizado para modelar respostas binárias. Apresentaremos o modelo teórico por trás da regressão logística\, implementaremos utilizando a função glm do R e mostraremos como interpretar os resultados\, calcular predições e comparar modelos encaixados. \nTópico 3: Regressão logística binomial. Aqui\, mostramos como a regressão logística para variáveis binarias pode ser estendida para lidar com dados que consistem de proporções discretas. Também apresentaremos funções de ligação alternativas ao logito\, como a probito e complemento log-log. \n			\n				\n				\n				\n				\n				Quarta-feira 21st\n				Aulas das 14:00 às 18:00 (Horário de Brasília) \nTópico 4: Regressão logística categórica. Também conhecida como regressão multinomial\, é utilizada pra modelar dados politômicos\, isto é\, dados que assumem mais do que duas categorias distintas. Assim como a regressão logística ordinal\, a regressão logística categórica também se baseia em uma extensão do caso de regressão logística binária. \nTópico 5: Regressão Poisson. A regressão Poisson é uma técnica amplamente utilizada para modelar dados de contagem\, isto é\, dados em que a variável resposta denota o número de vezes que um evento ocorreu. \n			\n				\n				\n				\n				\n				Quinta-feira 22nd\n				Aulas das 14:00 às 18:00 (Horário de Brasília) \nTópico 6: Modelos de superdispersão. A abordagem de quase-verossimilhança para os modelos de Poisson e binomial. Regressão binomial negativa. O modelo binomial negativo é\, assim como o modelo de regressão Poisson\, utilizado para dados de contagem\, mas é menos restritivo do que o modelo de Poisson\, especificamente por lidar com dados superdispersos. Regressão beta-binomial. O modelo beta-binomial é uma alternativa ao modelo binomial que acomoda superdispersão. \nTópico 7: Modelos inflacionados de zeros. Dados de contagens inflacionados de zeros apresentam um número excessivo de contagens nulas quando modelados utilizando um modelo de Poisson on binomial negativo. Os modelos de Poisson ou binomial negativo inflacionados de zeros são exemplos de modelos de variáveis latentes. \n			\n			\n				\n				\n				\n				\n				Instrutor do curso\n \nDr. Rafael De Andrade Moral \nRafael é Professor Associado de Estatística na Maynooth University\, Irlanda. Bacharel em Biologia e Doutor em Estatística pela Universidade de São Paulo\, Rafael tem interesse em ensino e pesquisa em modelagem estatística aplicada a ecologia\, manejo da fauna silvestre\, agricultura e ciências ambientais. Como diretor do grupo de pesquisa em ecologia teórica e estatística\, Rafael reúne uma comunidade de pesquisadores que utilizam ferramentas matemáticas e estatísticas para melhor compreenderem o mundo natural. Como uma estratégia de ensino alternativa\, Rafael vem produzindo vídeos musicais e paródias para promover a Estatística nas mídias sociais e na sala de aula. Sua página pessoal pode ser encontrada aqui. \nResearchGateGoogleScholarORCIDGitHub
URL:https://prstats.preprodw.com/course/curso-online-introducao-a-modelos-lineares-generalizados-usando-r-e-r-studio-iglm07/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/IGLM04R.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240220
DTEND;VALUE=DATE:20240223
DTSTAMP:20260419T064420
CREATED:20200804T125230Z
LAST-MODIFIED:20240222T142952Z
UID:10000313-1708387200-1708646399@prstats.preprodw.com
SUMMARY:ONLINE COURSE – Data visualization with ggplot2 using R and Rstudio (DVGG04) This course will be delivered live
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nTuesday\, March 26th\, 2023\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE FORMAT\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCOURSE PROGRAM\nTIME ZONE – Central Time Zone – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Details\n				During this course we provide a comprehensive introduction to data visualization in R using ggplot. We begin by providing a brief overview of the general principles data visualization\, and an overview of the general principles behind ggplot. We then proceed to cover the major types of plots for visualizing distributions of univariate data: histograms\, density plots\, barplots\, and Tukey boxplots. In all of these cases\, we will consider how to visualize multiple distributions simultaneously on the same plot using different colours and “facet” plots. We then turn to the visualization of bivariate data using scatterplots. Here\, we will explore how to apply linear and nonlinear smoothing functions to the data\, how to add marginal histograms to the scatterplot\, add labels to points\, and scale each point by the value of a third variable. We then cover some additional plot types that are often related but not identical to those major types covered during the beginning of the course: frequency polygons\, area plots\, line plots\, uncertainty plots\, violin plots\, and geospatial mapping. We then consider more fine grained control of the plot by changing axis scales\, axis labels\, axis tick points\, colour palettes\, and ggplot “themes”. Finally\, we consider how to make plots for presentations and publications. Here\, we will introduce how to insert plots into documents using RMarkdown\, and also how to create labelled grids of subplots of the kind seen in many published articles. \n			\n				\n				\n				\n				\n				Intended Audiences\n				This course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research\, and also widely throughout the public\, and private sector. \n  \n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely \n			\n				\n				\n				\n				\n				Course Information\n				Time zone – GMT+1 \nAvailability – TBC \nDuration – 2 days \nContact hours – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n			\n				\n				\n				\n				\n				Teaching Format\n				\n\nThis course will be largely practical\, hands-on\, and workshop based. For each topic\, there will first be some lecture style presentation\, i.e.\, using slides or blackboard\, to introduce and explain key concepts and theories. Then\, we will cover how to perform the various statistical analyses using R. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. For the breaks between sessions\, and between days\, optional exercises will be provided. Solutions to these exercises and brief discussions of them will take place after each break. \n\n\n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				None needed. \n			\n				\n				\n				\n				\n				Assumed computer background\n				Some familiarity with R. \n			\n				\n				\n				\n				\n				Equipment and software requirements\n				\n\nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer).  \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students. \n\n\n\n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n \n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \n			\n				\n				\n				\n				\n				If you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE PROGRAMME\n\nTuesday 26th \nClasses from 12:00 to 16:00 (Central Time Zone) \nDAY 1 \nTopic 1: What is data visualization. Data visualization is a means to explore and understand our data and should be a major part of any data analysis. Here\, we briefly discuss why data visualization is so important and what the major principles behind it are. \nTopic 2: Introducing ggplot. Though there are many options for visualization in R\, ggplot is simply the best. Here\, we briefly introduce the major principles behind how ggplot works\, namely how it is a layered grammar ofgraphics. \nWednesday 27th \nClasses from 12:00 to 16:00 (Central Time Zone) \nDAY 2 \n\nTopic 3: Visualizing univariate data. Here\, we cover a set of major tools for visualizing distributions over single variables: histograms\, density plots\, barplots\, Tukey boxplots. In each case\, we will explore how to plot multiple groups of data simultaneously using different colours and also using facet plots. \nTopic 4: Scatterplots. Scatterplots and their variants are used to visualize bivariate data. Here\, in addition to covering how to visualize multiple groups using colours and facets\, we will also cover how to provide marginal plots on the scatterplots\, labels to points\, and how to obtain linear and nonlinear smoothing of the plots. \nTopic 5: More plot types. Having already covered the most widely used general purpose plots on Day 1\, we now turn to cover a range of other major plot types: frequency polygons\, area plots\, line plots\, uncertainty plots\, violin plots\, and geospatial mapping. Each of these are important and widely used types of plots\, and knowing them will expand your repertoire. \n\nThursday 28th \nClasses from 12:00 to 16:00 (Central Time Zone) \nDAY 3 \nTopic 6: Fine control of plots. Thus far\, we will have mostly used the default for the plot styles and layouts. Here\, we will introduce how to modify things like the limits and scales on the axes\, the positions and nature of the axis ticks\, the colour palettes that are used\, and the different types of ggplot themes that are available. \nTopic 7: Plots for publications and presentations: Thus far\, we have primarily focused on data visualization as a means of interactively exploring data. Often\, however\, we also want to present our plots in\, for example\, published articles or in slide presentations. It is simple to save a plot in different file formats\, and then insert them into a document. However\, a much more efficient way of doing this is to use RMarkdown to run the R code and automatically insert the resulting figure into a\, for example\, Word document\, pdf document\, html page\, etc. In addition\, here we will also cover how to make labelled grids of subplots like those found in many scientific articles. \n\n  \n			\n				\n				\n				\n				\n				Course Instructor\n \nDr. Rafael De Andrade Moral \n\nRafael is an Associate Professor of Statistics at Maynooth University\, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo\, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology\, Wildlife Management\, Agriculture\, and Environmental Science. As director of the Theoretical and Statistical Ecology Group\, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy\, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here\n\nResearchGateGoogleScholarORCIDGitHub \n 
URL:https://prstats.preprodw.com/course/data-visualization-with-ggplot2-using-r-and-rstudio-dvgg04/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/DVGG02.png
GEO:55.378051;-3.435973
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240129
DTEND;VALUE=DATE:20240203
DTSTAMP:20260419T064420
CREATED:20230919T155938Z
LAST-MODIFIED:20231204T164859Z
UID:10000438-1706486400-1706918399@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Spatial and Spatial-Temporal Modelling Using R-INLA (SSTM01) This course will be delivered live
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nMonday\, January 29th\, 2024\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCourse Program\nTIME ZONE – UTC+2 – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Details\n				The aim of the course is to introduce you to Bayesian inference using the integrated nested Laplace approximation (INLA) method and its associated R-INLA package for the analysis of spatial and spatio-temporal data. This course will cover the basics on the INLA methodology as well as practical modelling of different types of spatial and spatio-temporaldata. \nBy the end of the course participants should: \n\nKnow the different types of spatial and spatio-temporal data available and how to work with them in R.\nKnow the different modelling approaches for spatial and spatio-temporal data.\nKnow how to visualize and produce maps of spatial and spatio-temporal data.\nBe able to fit models with the R-INLA package.\nKnow how to interpret the output from model fitting.\nBe confident with the use of INLA for data analysis.\nUnderstand the different models that can be fit with INLA to spatial and spatio-temporal data.\nKnow how to define the different parts of a model with INLA.\nHave the confidence to use INLA for their own projects.\n\n			\n				\n				\n				\n				\n				Intended Audiences\n				Academics and post-graduate students working on projects related to spatial and spatio-temporal data analysis and modelling and who want to add the INLA methodology for Bayesian inference to their toolbox. \nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a command-line language such as R. \nThe course is designed for intermediate-to-advanced R users interested in data analysis and modelling. Ideally\, they should have some background on probability\, statistics and data analysis. \n			\n				\n				\n				\n				\n				Venue\n				Venue – Delivered remotely\n			\n				\n				\n				\n				\n				Course Information\n				Time zone – Central European Standard Time (CEST) \nAvailability – 20 places \nDuration – 5 days \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n  \nPLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n			\n				\n				\n				\n				\n				Teaching Format\n				\n\nhe course will be a mixture of theoretical and practical sessions. Each concept will be first described and explained\, and next there will be a time to exercise the topics using provided data sets. Participants are also very welcome to bring their own data. \nAssumed quantitative knowledge \nThe course is designed for intermediate-to-advanced R users interested in Bayesian inference for data analysis and R beginners who have prior experience with Bayesian inference. \nAssumed computer background \nAttendees should already have experience with R and be familiar with data from different formats (csv\, tab\, etc.)\, create simple plots\, and manipulate data frames. Furthermore\, knowledge of how to fit generalized linear (mixed) models using typical R functions (such as glm and lme4) will be useful. \nEquipment and software requirements \nA laptop/personal computer with any operating system (Linux\, Windows\, MacOS) and with recent versions of R (https://cran.r-project.org) and RStudio (https://www.rstudio.com) installed; both are freely available as open-source software. You will be sent a list of packages prior to the course. It is essential that you come with all necessary software and packages already installed. \nhttps://cran.r-project.org/ \nDownload RStudio \nUNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com \n\n\n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				Although an introduction to the INLA method will be given\, attendants are expected to be familiar with Bayesian inference. This includes how to define simple Bayesian models and have a basic understanding of some typical methods to compute or approximate the prior distributions (such as models with conjugate priors\, MCMC methods\, etc.). \n			\n				\n				\n				\n				\n				Assumed computer background\n				Attendants are expected to be familiar with the R programming environment for data analysis. No previous background on handling of spatial and spatio-temporal data will be assumed. \n			\n				\n				\n				\n				\n				Equipment and software requirements\n				\nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n			\n				\n				\n				\n				\n				If you are unsure about course suitability\, please get in touch by email to find out more \noliverhooker@prstatistics.com \n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Programme\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Monday 29th\n				Classes from 14:00 to 21:00 CET \nDAY 1 \nLECTURE 1 – Intro to INLA \nPRACTICAL 1 – Intro to INLA \nLECTURE 2 – Model fitting with INLA \nPRACTICAL 2 – Model fitting with INLA \nLECTURE 3 – GLMM’s with INLA \nPRACTICAL 3 – GLMM’s with INLA \nQ and A and end of day summary \n			\n				\n				\n				\n				\n				Tuesday 30th\n				Classes from 14:00 to 21:00 CET \nDAY 2 \nLECTURE 4 – Spatial Data \nPRACTICAL 4 – Spatial Data \nLECTURE 5 – Spatio-Temporal Data \nPRACTICAL 5 – Spatio-Temporal Data \nLECTURE 6 – Advanced Visualisation \nPRACTICAL 6 – Advanced Visualisation \nQ and A and end of day summary \n			\n				\n				\n				\n				\n				Wednesday 31st\n				Classes from 14:00 to 21:00 CET \nDAY 3 \nLECTURE 7 – Spatial Models for Lattice Data \nPRACTICAL 7 – Spatial Models for Lattice Data \nLECTURE 8 – Spatial Models for Continuous Data \nPRACTICAL 8 – Spatial Models for Continuous Data \nLECTURE 9 – Spatial Models for Point Patterns \nPRACTICAL 9 – Spatial Models for Point Patterns \nQ and A and end of day summary \n			\n				\n				\n				\n				\n				Thursday 1st\n				Classes from 14:00 to 21:00 CET \nDAY 4 \nLECTURE 10 – Spatio-Temporal Models for Lattice Data \nPRACTICAL 10 – Spatio-Temporal Models for Lattice Data \nLECTURE 11 – Spatio-Temporal Models  for Continuous Data \nPRACTICAL 11 – Spatio-Temporal Models  for Continuous Data \nLECTURE 12 – Spatio-Temporal Models  for Point Patterns \nPRACTICAL 12 – Spatio-Temporal Models  for Point Patterns \nQ and A and end of day summary \n			\n				\n				\n				\n				\n				Friday 2nd\n				Classes from 14:00 to 21:00 CET \nDAY 5 \nCase studies\, own data and problem solving. \n			\n			\n				\n				\n				\n				\n				\n				\n					Dr Virgillio Gomez Rubio\n					\n					Virgilio has ample experience in Bayesian inference and statistical modeling as well as developing packages for the R programming language. His book Bayesian inference with INLA has been widely adopted for Bayesian modeling and it has been awarded the 2022 SEIO-BBVA Foundation Award in the category of Data Science and Big Data. You can find more information about him on here\n \n\nResearchgate\nGoogle Scholar\nORCID\nGitHub
URL:https://prstats.preprodw.com/course/spatial-and-spatial-temporal-modelling-using-r-inla-sstm01/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2023/09/Screenshot-2023-09-20-at-14.21.47.png
GEO:55.378051;-3.435973
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240109
DTEND;VALUE=DATE:20240112
DTSTAMP:20260419T064420
CREATED:20221130T161237Z
LAST-MODIFIED:20231204T171610Z
UID:10000421-1704758400-1705017599@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Quantitative analysis of infrared spectroscopy data for soil and plant sciences (SPEC02) This course will be delivered live
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nTuesday\, January 9th\, 2024\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE FORMAT\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTIME ZONE\nTIME ZONE – `Central European Standard Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				About This Course\n				This 3-day short course is aimed at providing an introduction to the analysis infrared spectroscopy data using the R programming language. Infrared spectroscopy is a high-throughput\, non-destructive\, and cheap sensing method that has a large range of applications in agricultural\, plant and environmental sciences. Theory underpinning the visible\, near and mid-infrared reflectance will be discussed\, as well as interpretation of the wavelengths corresponding to specific molecular vibrations and the pre-processing of the raw spectra (day 1). We will then cover chemometric methods for exploratory spectral analysis with principal component analysis. We will have the opportunity to detect outlier spectra as well as to select the samples for laboratory analysis using the spectral data (day 2).  Finally\, we will introduce methods for building accurate multivariate models. Multivariate models will be explained and tested\, including machine learning and conventional statistical algorithms. Sessions will be a blend of interactive demonstrations/practical and lectures\, where learners will have the opportunity to ask questions throughout. Prior to the course\, attendees will receive R script and datasets and a list of R packages to install. \nBy the end of the course\, participants should be able to: \n\nSelect the best pre-processing techniques for their own raw infrared spectral data.\nApply data exploration techniques and avoid the common pitfalls in tackling a data analysis of infrared spectral data.\nSelect the optimal sample size and the best sampling design to subset spectral data and send the samples for laboratory analysis.\nUnderstand and apply approaches for spectral data outlier detection.\nApply statistical multivariate modelling methods to infrared spectroscopy data and validate the model predictions.\n\n\n\n\n			\n				\n				\n				\n				\n				Intended Audiences\n				\nThis course is aimed at anyone who wishes to introduce into the analysis of visible\, near and mid-infrared spectral data for plant and soil sciences. It is particularly suited for:\n\nGraduate\, post-graduate or post-doctoral level researchers who wish to learn how to analyse their own infrared data in R.\nApplied researchers and analysts in the environmental or ecological sector with a role in handling and analysing infrared spectroscopy data.\n\n\n\n			\n				\n				\n				\n				\n				Course Details\n				Time zone – CET\n\nAvailability – TBC \nDuration – 3 days \nContact hours – Approx. 20 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English\n			\n				\n				\n				\n				\n				Teaching Format\n				This course will comprise a mixture of taught theory and practical examples. Data and analytical approaches will be presented in a lecture format to introduce key concepts. Statistical analyses will then be presented using R. All R script that the instructor uses during these sessions will be shared with participants\, and R script will be presented and explained.\n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				Understanding of basic concept of sensing in the infrared range of the electromagnetic spectrum and prior knowledge of basic statistical techniques (e.g. linear regression).\n			\n				\n				\n				\n				\n				Assumed computer background\n				Prior basic experience with performing statistical analyses using R and R Studio will be assumed\, but is not a requirement.\n			\n				\n				\n				\n				\n				Equipment and software requirements\n				\n\nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open-source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve the learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n\n\n  \n\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\nPLEASE READ – CANCELLATION POLICY \n\n\n\n\nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \n\n\n\n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n\n\n			\n				\n				\n				\n				\n				If you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE PROGRAMME\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Tuesday 9th\n				Classes from 09:00 to 17:00 CET \nDAY 1– Introduction to spectral inference in soil and plant sciences– Handling spectral data– Practical– The pre-processing of raw spectra– Practical– Exploratory spectral analysis– Practical \n			\n				\n				\n				\n				\n				Wednesday 10th\n				Classes from 09:00 – 17:00 CET \nDAY 2 \n– Spectral similarity analysis– The detection of outliers– Practical– Selecting the samples for laboratory analysis– Practical \n			\n				\n				\n				\n				\n				Thursday 11th \n				Classes from 09:00 – 17:00 CET \nDAY 3– Estimating properties from spectra -Multivariate statistical models– Practical– Validation of the predictions– Practical– Bring your own data! – OR large exercise estimating properties from raw spectra– Discussion & questions \n			\n			\n				\n				\n				\n				\n				\n				\n					Alexandre Wadoux\n					\n					Alexandre Wadoux is a Research Associate in digital soil mapping at the University of Sydney and recently moved to the French National Institute for Agronomic and Environmental Research in Montpellier (France) to work on his Marie-Curie Fellowship. He has an undergraduate degree from the University of Angers in France\, a MSc in soil science from the University of Tubingen in Germany\, a Master in epistemology of sciences from the University of Nantes in France and a PhD in applied geostatistics from Wageningen University in the Netherlands. He has made contributions to several quantitative aspects of soil and environmental science through the development of methods for spatial sampling\, mapping and assessment using geostatistics\, statistical learning algorithms and spectroscopy. He is the author of the book “Soil Spectral Inference with R” published in 2021 with Springer.
URL:https://prstats.preprodw.com/course/quantitative-analysis-of-infrared-spectroscopy-data-for-soil-and-plant-sciences-spec02/
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2022/11/featured-spec01.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240109
DTEND;VALUE=DATE:20240112
DTSTAMP:20260419T064420
CREATED:20220614T145529Z
LAST-MODIFIED:20231223T114438Z
UID:10000412-1704758400-1705017599@prstats.preprodw.com
SUMMARY:ONLINE COURSE – Model selection and model simplification (MSMS04) This course will be delivered live
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nTuesday\, January 9th\, 2024\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE FORMAT\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCOURSE PROGRAM\nTIME ZONE – Central Time Zone – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n​\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				About This Course\n				This three day course covers the important and general topics of statistical model building\, model evaluation\, model selection\, model comparison\, model simplification\, and model averaging. These topics are vitally important to almost every type of statistical analysis\, yet these topics are often poorly or incompletely understood. We begin by considering the fundamental issue of how to measure model fit and a model’s predictive performance\, and discuss a wide range of other major model fit measurement concepts like likelihood\, log likelihood\, deviance\, residual sums of squares etc. We then turn to nested model comparison\, particularly in general and generalized linear models\, and their mixed effects counterparts. We then consider the key concept of out-of-sample predictive performance\, and discuss over-fitting or how excellent fits to the observed data can lead to very poor generalization performance. As part of this discussion of out-of-sample generalization\, we introduce leave-one-out cross-validation and Akaike Information Criterion (AIC). We then cover general concepts and methods related to variable selection\, including stepwise regression\, ridge regression\, Lasso\, and elastic nets. Following this\, we turn to model averaging\, which is an arguably always preferable alternative to model selection. Finally\, we cover Bayesian methods of model comparison. Here\, we describe how Bayesian methods allow us to easily compare completely distinct statistical models using a common metric. We also describe how Bayesian methods allow us to fit all the candidate models of potential interest\, including cases were traditional methods fail. \n			\n				\n				\n				\n				\n				Intended Audiences\n				This course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research\, and also widely throughout the public\, and private sector. \n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely. \n			\n				\n				\n				\n				\n				Course Details\n				Time zone – GMT+1 \nAvailability – TBC \nDuration – 3 x 1/2 days \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n			\n				\n				\n				\n				\n				Teaching Format\n				\n			\n				\n				\n				\n				\n				Assumed computer background\n				\n			\n				\n				\n				\n				\n				Equipment and software requirements\n				\nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer). \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students. \n\n\n\n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \n			\n				\n				\n				\n				\n				\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n  \n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE PROGRAMME\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Tuesday 9th\n				Classes from 12:00 to 16:00 (Central Time Zone) \nDAY 1 \nTopic 1: Measuring model fit. In order to introduce the general topic of model evaluation\, selection\, comparison\, etc.\, it is necessary to understand the fundamental issue of how we measure model fit. Here\, the concept of conditional probability of the observed data\, or of future data\, is of vital importance. This is intimately related\, though distinct\, to concept of likelihood and the likelihood function\, which is in turn related to the concept of the log likelihood or deviance of a model. Here\, we also show how these concepts are related to concepts of residual sums of squares\, root mean square error (rmse)\, and deviance residuals. \nTopic 2: Nested model comparison. In this section\, we cover how to do nested model comparison in general linear models\, generalized linear models\, and their mixed effects (multilevel) counterparts. First\, we precisely define what is meant by a nested model. Then we show how nested model comparison can be accomplished in general linear models with F tests\, which we will also discuss in relation to R^2 and adjusted R^2. In generalized linear models\, and mixed effects models\, we can accomplish nested model comparison using deviance based chi-square tests via Wilks’s theorem. \n			\n				\n				\n				\n				\n				Wednesday 10th\n				Classes from 12:00 to 16:00 (Central Time Zone) \nDAY 2 \nTopic 3: Out of sample predictive performance: cross validation and information criteria. In the previous sections\, the focus was largely on how well a model fits or predicts the observed data. For reasons that will be discussed in this section\, related to the concept of overfitting\, this can be a misleading and possibly even meaningless means of model evaluation. Here\, we describe how to measure out of sample predictive performance\, which measures how well a model can generalize to new data. This is arguably the gold-standard for evaluating any statistical models. A practical means to measure out of sample predictive performance is cross-validation\, especially leave-one-out cross-validation. Leave-one-out cross-validation can\, in relatively simple models\, be approximated by Akaike Information Criterion (AIC)\, which can be exceptionally simple to calculate. We will discuss how to interpret AIC values\, and describe other related information criteria\, some of which will be used in more detail in later sections. \nTopic 4: Variable selection. Variable selection is a type of nested model comparison. It is also one of the most widely used model selection methods\, and variable selection of some kind is almost always done routinely in all data analysis. Although we will also have discussed variable selection as part of Topic 2 above\, we discuss the topic in more detail here. In particular\, we cover stepwise regression (and its limitations)\, all subsets methods\, ridge regression\, Lasso\, and elastic nets. \n			\n				\n				\n				\n				\n				Thursday 11th\n				Classes from 12:00 to 16:00 (Central Time Zone) \nDAY 3 \nTopic 5: Model averaging. Rather than selecting one model from a set of candidates\, it is arguably always better perform model averaging\, using all the candidates models\, weighted by the predictive performance. We show how to perform model average using information criteria. \nTopic 6: Bayesian model comparison methods. Bayesian methods afford much greater flexibility and extensibility for model building than traditional methods. They also allow us to easily directly compare completely unrelated statistical models of the same data using information criteria such as WAIC and LOOIC. Here\, we will also discuss how Bayesian methods allow us to fit all models of potential interest to us\, including cases where model fitting is computationally intractable using traditional methods (e.g.\, where optimization convergence fails). This allows us therefore to consider all models of potential interest\, rather than just focusing on a limited subset where the traditional fitting algorithms succeed. \n  \n			\n			\n				\n				\n				\n				\n				Course Instructor\n \nDr. Rafael De Andrade Moral \n\nRafael is an Associate Professor of Statistics at Maynooth University\, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo\, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology\, Wildlife Management\, Agriculture\, and Environmental Science. As director of the Theoretical and Statistical Ecology Group\, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy\, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here\n\nResearchGateGoogleScholarORCIDGitHub
URL:https://prstats.preprodw.com/course/online-course-model-selection-and-model-simplification-msms04/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/MSMS03.png
GEO:53.1423672;-7.6920536
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20231211
DTEND;VALUE=DATE:20231215
DTSTAMP:20260419T064420
CREATED:20231121T142647Z
LAST-MODIFIED:20231204T170316Z
UID:10000334-1702252800-1702598399@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Data wrangling using R and Rstudio (DWRS03) This course will be delivered live
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nMonday\, December 11th\, 2023\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE FORMAT\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCOURSE PROGRAM\nTIME ZONE – Central Time Zone – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Details\n				During this course we provide a comprehensive practical introduction to data wrangling using R. In particular\, we focus on tools provided by R’s tidyverse\, including dplyr\, tidyr\, purrr\, etc. Data wrangling is the art of taking raw and messy data and formatting and cleaning it so that data analysis and visualization etc may be performed on it. Done poorly\, it can be time consuming\, laborious\, and error-prone. Fortunately\, the tools provided by R’s tidyverse allow us to do data wrangling in a fast\, efficient\, and high-level manner\, which can have dramatic consequences for ease and speed with which we analyse data. We start with how to read data of different types into R\, we then cover in detail all the dplyr tools such as select\, filter\, mutate\, etc. Here\, we will also cover the pipe operator (%>%) to create data wrangling pipelines that take raw messy data on the one end and return cleaned tidy data on the other. We then cover how to perform descriptive or summary statistics on our data using dplyr’s summarize and group_by functions. We then turn to combining and merging data. Here\, we will consider how to concatenate data frames\, including concatenating all data files in a folder\, as well as cover the powerful SQL like join operations that allow us to merge information in different data frames. The final topic we will consider is how to “pivot” data from a “wide” to “long” format and back using tidyr’s pivot_longer and pivot_wider. \n			\n				\n				\n				\n				\n				Intended Audiences\n				This course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research\, and also widely throughout the public\, and private sector.\n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely \n			\n				\n				\n				\n				\n				Course Information\n				Time zone – GMT+1 \nAvailability – TBC \nDuration – 3 x 1/2 days \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n			\n				\n				\n				\n				\n				Teaching Format\n				\n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				Coming soon.. \n			\n				\n				\n				\n				\n				Assumed computer background\n				Minimal prior experience with R and RStudio is required. Attendees should be familiar with some basic R syntax and commands\, how to write code in the RStudio console and script editor\, how to load up data from files\, etc. \n			\n				\n				\n				\n				\n				Equipment and software requirements\n				\nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer). \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students. \n\n\n\n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n			\n				\n				\n				\n				\n				If you are unsure about course suitability\, please get in touch by email to find out more \noliverhooker@prstatistics.com \n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE PROGRAMME\n			\n				\n				\n				\n				\n				\n\n\nClasses from 12:00 to 16:00 (Central Time Zone) \nDAY 1 \nTopic 1: Reading in data. We will begin by reading in data into R using tools such as readr and readxl. Almost all types of data can be read into R\, and here we will consider many of the main types\, such as csv\, xlsx\, sav\, etc. Here\, we will also consider how to contol how data are parsed\, e.g.\, so that they are read as dates\, numbers\, strings\, etc. \nTopic 2: Wrangling with dplyr. For the remainder of Day 1\, we will next cover the very powerful dplyr R package. This package supplies a number of so-called “verbs” — select\, rename\, slice\, filter\, mutate\, arrange\, etc. — each of which focuses on a key data manipulation tools\, such as selecting or changing variables. All of these verbs can be chained together using “pipes” (represented by %>%). Together\, these create powerful data wrangling pipelines that take raw data as input and return cleaned data as output. Here\, we will also learn about the key concept of “tidy data”\, which is roughly where each row of a data frame is an observation and each column is a variable. \nClasses from 12:00 to 16:00 (Central Time Zone) \nDAY 2 \nTopic 2 continued: \nTopic 3: Summarizing data. The summarize and group_by tools in dplyr can be used with great effect to summarize data using descriptive statistics. \nClasses from 12:00 to 16:00 (Central Time Zone) \nDAY 3 \nTopic 4: Merging and joining data frames. There are multiple ways to combine data frames\, with the simplest being “bind” operations\, which are effectively horizontal or vertical concatenations. Much more powerful are the SQL like “join” operations. Here\, we will consider the inner_join\, left_join\, right_join\, full_join operations. In this section\, we will also consider how to use purrr to read in and automatically merge large sets of files. \nTopic 5: Pivoting data. Sometimes we need to change data frames from “long” to “wide” formats. The R package tidyr provides the tools pivot_longer and pivot_wider for doing this. \n\n\n\n			\n				\n				\n				\n				\n				Course Instructor\n \n\n\n\n\nDr. Rafael De Andrade Moral \n\n\nRafael is an Associate Professor of Statistics at Maynooth University\, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo\, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology\, Wildlife Management\, Agriculture\, and Environmental Science. As director of the Theoretical and Statistical Ecology Group\, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy\, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here \n\n\n  \nResearchGateGoogleScholarORCIDGitHub \n\n\n\n\n			\n			\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Let’s connectLorem ipsum dolor sit amet\, consectetuer adipiscing elit.\n				\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n					\n					\n						General Info\n						info@website.com\n					\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n					\n					\n						Twitter\n						@website.com\n					\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n					\n					\n						Facebook\n						website.com\n					\n				\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Copyright  PR Statistics  2022  |  Privacy Policy  |  Disclaimer  |  Site Map
URL:https://prstats.preprodw.com/course/data-wrangling-using-r-and-rstudio-dwrs03-2/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/DWRS02R.png
GEO:55.378051;-3.435973
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20231204
DTEND;VALUE=DATE:20231209
DTSTAMP:20260419T064421
CREATED:20230718T154854Z
LAST-MODIFIED:20231204T165620Z
UID:10000430-1701648000-1702079999@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Machine Learning with R (Intermediate - Advanced) (MLIA01) This course will be delivered live
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nMonday\, August 28th\, 2023\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCourse Program\nTIME ZONE – GMT – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Details\n				This intensive 4-day course provides an in-depth exploration of machine learning using the popular open-source statistical software\, R. Participants are assumed to have a basic working knowledge of regression and supervised learning techniques and so will gain a further understanding of various intermediate and advanced machine learning algorithms\, how they work\, and how to implement them using R’s ecosystem of packages. Real-world data sets will be used to offer hands-on experience and help participants understand the practical applications of the covered concepts. \nBy the end of this course\, students should be able to: \n\nUnderstand and implement advanced supervised learning techniques such as CNNs\, RNNs\, Transformer Models\, and Bayesian Machine Learning methods.\nUnderstand and implement advanced unsupervised learning techniques including various clustering\, dimension reduction\, and anomaly detection methods.\nApply these techniques to real-world datasets and interpret the results.\nUnderstand the underlying methods and assumptions/drawbacks of these techniques.\n\n			\n				\n				\n				\n				\n				Intended Audiences\n				Academics and post-graduate students working on projects where advanced machine learning and predictive modelling will be useful. \n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely \n			\n				\n				\n				\n				\n				Course Information\n				Availability – TBC \nDuration – 4 days \nContact hours – Approx. 28 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n			\n				\n				\n				\n				\n				Teaching Format\n				Each day will consist of 2-3 lectures with regular discussion and Q&A sessions. In the afternoons we will cover guided practicals (tutors and students running code and explaining results through worked examples and case studies) and self-guided exercise sheets. Students are welcome to bring their own data and discuss it with the tutors. \n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				A basic understanding of statistical concepts such as linear and logistic regression models. Basic machine learning techniques such as Random Forests\, Gradient Boosting\, k-NN\, SVMs. \n			\n				\n				\n				\n				\n				Assumed computer background\n				Good familiarity with R. Ability to import/export data\, manipulate data frames\, fit basic machine learning models (list above) and generate simple exploratory and diagnostic plots. \n			\n				\n				\n				\n				\n				Equipment and software requirements\n				\nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer). \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students. \n\n\n\n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n			\n				\n				\n				\n				\n				If you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com\n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE PROGRAMME\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Monday 4th\n				Classes from 09:30 to 17:30 GMT+1 \nDAY 1 \nDeep Dive into Supervised Learning \nWe begin with an introduction to Deep Learning in which we cover the basic concepts and its difference from traditional machine learning. We then extend to Convolutional Neural Networks (CNNs)\, exploring their architecture\, their use in image and video processing\, and their role in object detection and recognition. Finally we cover time series models through Recurrent Neural Networks (RNNs) and their application in sequential data analysis and natural language processing. \nIn the afternoon sessions we implement CNNs and RNNs using real data sets \nR Packages used: keras\, tensorflow \n			\n				\n				\n				\n				\n				Tuesday 5th\n				Classes from 09:30 to 17:30 GMT+1 \nDAY 2 \nAdvanced Supervised Learning Techniques \nOn day 2 we cover Transformer models and Bayesian machine learning techniques. We start by understanding the transformer architecture\, its self-attention mechanism\, and its use in natural language processing tasks. We then cover the basics of Bayesian inference and explore its use in classification and regression tasks\, and compare it to traditional machine learning methods. \nIn the afternoon sessions the students can choose whether they explore either the Transformer or Bayesian methods further by following and extending some example R scripts. \nR Packages: keras\, tensorflow\, rstan\, brms\, BART \n			\n				\n				\n				\n				\n				Thursday 7th\n				Classes from 09:30 to 17:30 GMT+1 \nDAY 3 \nUnsupervised Learning – Clustering and Dimension Reduction \nThe third day will focus on advanced clustering techniques and dimension reduction. We start by exploring clustering techniques including hierarchical clustering\, DBSCAN\, and their use in segmentation. We then cover dimension reduction techniques; starting with PCA and extending to t-SNE and UMAP. We explain how these techniques work and explore their use in visualisation of data sets with high dimensions. \nIn the afternoon session students will explore the use of these techniques through real-world data sets. \nR Packages: cluster\, dbscan\, factoextra\, Rtsne\, umap \n			\n				\n				\n				\n				\n				Friday 8th\n				Classes from 09:30 to 17:30 GMT+1 \nDAY 4 \nUnsupervised Learning – Anomaly Detection and Course Wrap-up \nOn the final day we will focus on anomaly detection techniques and bringing together the topics covered throughout the course. We start with various anomaly detection techniques and demonstrate their use in e.g. fraud detection\, network security\, and health monitoring. We then provide a discussion session where we review the content of the course and talk about future steps in Machine Learning. \nIn the afternoon students have the opportunity to work on their own data sets and ask questions of the course instructor. \nR Packages: anomalize\, forecast\, e1071 \n			\n			\n				\n				\n				\n				\n				\n				\n					Dr. Andrew Parnell\n					\n					Andrew Parnell is the Hamilton Professor of Statistics in the Hamilton Institute at Maynooth University. His research is in statistics and machine learning for large structured data sets in a variety of application areas. He has co-authored over 90 peer-reviewed papers in journals such as Science\, Nature Communications\, and Proceedings of the National Academy of Sciences\, and has methodological publications in journals such as Statistics and Computing\, Journal of Computational and Graphical Statistics\, The Annals of Applied Statistics\, and Journal of the Royal Statistical Society: Series C. He has many years experience in teaching Bayesian statistics\, time series modelling\, and statistical machine learning to students at every level from undergraduate to PhD. He enjoys collaborating with other scientists in areas as diverse as climate change\, 3D printing\, and bioinformatics. \nResearch GateGoogle ScholarORCIDLinkedInGitHub
URL:https://prstats.preprodw.com/course/machine-learning-with-r-intermediate-advanced-mlia01/
LOCATION:Delivered remotely (Ireland)\, Western European Time\, Ireland
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2023/07/hunter-harritt-Ype9sdOPdYc-unsplash-scaled.jpg
GEO:55.378051;-3.435973
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20231002
DTEND;VALUE=DATE:20231007
DTSTAMP:20260419T064421
CREATED:20230721T124055Z
LAST-MODIFIED:20230919T143549Z
UID:10000431-1696204800-1696636799@prstats.preprodw.com
SUMMARY:ONLINE COURSE – The Practice of RADseq: Population Genomics Analysis with Stacks (RADS02) This course will be delivered live
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nMonday\, October 2nd\, 2023\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE FORMAT\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTIME ZONE\nTIME ZONE – Central Standard Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				About This Course\n				This course is aimed at introducing researchers to the theory and practice of using reduced representation libraries – such as RAD sequencing – to preform population genomic analysis in non-model organisms. The course will center on running the software pipeline Stacks\, focusing on how the characteristics of the underlying molecular libraries result in weak or robust analytical results. Sessions will be live online\, consisting of a blend of lectures\, interactive demonstrations\, and lab practicals\, where participants will have the opportunity to ask questions throughout. Computation will be done on the Amazon AWS Cloud. \nBy the end of the course\, participants should be able to: \n\nNavigate the UNIX file system\, execute commands\, and interact with bioinformatic data files;\nUnderstand how to perform a de novo analysis – without a reference genome – including parameter optimization;\nUnderstand how PCR duplicates and other molecular library characteristics affect analysis;\nComplete a reference genome-based analysis;\nTake the outputs from Stacks to complete a Structure analysis (de novo)\, a genome scan based on FST(reference-based)\, and a private allele analysis.\n\n			\n				\n				\n				\n				\n				Intended Audiences\n				Graduate students\, post-doctoral researchers\, or professionals who wish to learn how to analyze genomic RAD-based data. \n			\n				\n				\n				\n				\n				Venue\n				Delivered Remotely \n			\n				\n				\n				\n				\n				Course Details\n				Availability – TBC \nDuration – 5 days \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n			\n				\n				\n				\n				\n				Teaching Format\n				Data and analytical approaches will be presented in a lecture format to introduce key concepts. In the beginning\, participants will work interactively with the instructor to understand fundamentals. Once completed\, the course will shift into a lab practical format\, where the instructor introduces the lab\, then free time is given for participants to complete the lab with the instructor present to answer questions. At the end of each practical\, the instructor will go over the key ideas and results. \n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				Basic understanding of evolution (mutation\, drift\, selection\, migration\, HWE) and population-genomic concepts (e.g.\, FST\, population structure) is assumed. \n			\n				\n				\n				\n				\n				Assumed computer background\n				No computational background knowledge is assumed\, however\, experience in UNIX and/or bioinformatics analysis will enable participants to move at a faster pace. \n			\n				\n				\n				\n				\n				Equipment and software requirements\n				Students will need a laptop or desktop with a fast and reliable internet connection. The computer can run any operating system including MacOS\, Windows\, or Linux\, as we will connect\, via the terminal\, to our AWS instance on the Amazon Cloud. \n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n \n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\nPLEASE READ – CANCELLATION POLICY \n\n\nCancellations/refunds are accepted as long as the course materials have not been accessed\,. \n\n\nThere is a 20% cancellation fee to cover administration and possible bank fess. \n\n\nIf you need to discuss cancelling please contact oliverhooker@prstatistics.com. \n\n			\n				\n				\n				\n				\n				If you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE PROGRAMME\n\nDay 1: 09:00 – 16:00 (Central Standard Time\, i.e.\, Chicago) \n\nInstructor and participant introductions\nLecture: Exploring the Genetics of Non-Model Organisms with RAD-seq\nIntroduction to the Cloud\nIntroduction to UNIX\, Part 1\n\nDay 2: 09:00 – 16:00 \n\nShort Lecture: Illumina error model\, FastQ files\, data quality control\, and sample multiplexing\nCleaning and demultiplexing RAD-seq data\nIntroduction to UNIX\, Part 2\nParticipant two-minute lightning talks\n\nDay 3: 09:00 – 16:00 \n\nShort Lecture: Parameter optimization and de novo assembly of RAD tags\nUnderstanding the de novo assembly algorithm\nHow to optimize assembly parameters in a de novo assembly\nDe novo assembly of RAD tags without a genome for a STRUCTURE Analysis\n\nDay 4: 09:00 – 16:00 \n\nReferenced-aligned RAD tags for genome scanning and identifying signatures of selection\nHow to perform an integrated analysis – applying de novo data to a related reference genome\n\nDay 5: 09:00 – 16:00 \n\nShort Lecture: Understanding DNA quality\, molecular library integrity\, and PCR duplicates\nExamining the effects of PCR duplicates in a bird dataset\nPerforming a private allele analysis in a hybrid zone\nOpen lab\, time for questions on participant provided data sets.\n\n\n  \n			\n				\n				\n				\n				\n				Course Instructor\n \nDr. Julian Catchen is an Associate Professor at the University of Illinois at Urbana-Champaign where he runs a population genomics lab that focuses on how the evolution of the genome affects underlying genomic architecture. He is the primary author of Stacks and has been involved in RADseq analysis since 2009\, working on projects in a variety of fishes\, birds\, and insects while applying a diversity of genomic analyses including defining population structure\, conducting genome scans\, private allele analysis\, and phylogenetics. \nLab Website: https://catchenlab.life.illinois.edu/Google Scholar: https://scholar.google.com/citations?user=YKnVJaAAAAAJ&hl=enResearch Gate: https://www.researchgate.net/profile/Julian-CatchenORCID: https://orcid.org/0000-0002-4798-660X
URL:https://prstats.preprodw.com/course/the-practice-of-radseq-population-genomics-analysis-with-stacks-rads02/
LOCATION:Delivered remotely (Portugal)\, Portugal
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2023/07/Screenshot-2023-07-21-at-13.30.55.png
GEO:39.399872;-8.224454
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230911
DTEND;VALUE=DATE:20230916
DTSTAMP:20260419T064421
CREATED:20230515T125812Z
LAST-MODIFIED:20240403T160610Z
UID:10000425-1694390400-1694822399@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Advanced Ecological Niche Modelling (ENM/SDM) Using R (ANMR02) Deadline to register 28th August
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nMonday\, September 11th\, 2023\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – UTC – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				About This Course\n				Have you built an Ecological Niche Model? If yes\, you have already encountered challenges on data preparation\, or have struggled with issues in models fitting and accuracy. This course will teach you how to overcome these challenges and improve the accuracy of your ecological niche models. By the end of 5-day practical course\, you will have the capacity to filter records and select your variables with variance inflation factor; to test effect of Maxent regularization parameter in models performance; to validate models performance and accuracy; to perform MESS analysis\, null models\, and mechanistic models\, as well as to build your “virtual species”. \nEcological niche\, species distribution\, habitat distribution\, or climatic envelope models are different names for mechanistic and correlative models\, which are empirical or mathematical approaches to the ecological niche of a species. These methods relate different types of ecogeographical variables (environmental\, topographical\, human) to species physiological data or geographical locations\, in order to identify the factors limiting and defining the species&#39; niche. ENMs have become popular because of their efficiency in the design and implementation of conservation management. \nBy the end of 5-day practical course you will have the capacity to \n\nfilter records and select your variables with variance inflation factor;\ntest the effect of Maxent regularization parameter in models performance;\nvalidate models performance and accuracy;\nperform MESS analysis\, null models\, and mechanistic models\, as well as to build your “virtual species”.\n\nStudents will learn to use functions implemented in the packages “usdm”; “dismo”; “ENMEval”; “SDMvspecies”; “spThin”; and “NicheMapper” among others. \n			\n				\n				\n				\n				\n				Intended Audiences\n				This course is orientated to PhD and MSc students\, as well as other students and researchers working on biogeography\, spatial ecology\, or related disciplines\, with experience in ecological niche models. \n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely\n			\n				\n				\n				\n				\n				Course Details\n				Availability – 24 places \nDuration – 5 days \nContact hours – Approx. 35 hours \nECT’s – Equal to 3ECT’s \nLanguage – English\n			\n				\n				\n				\n				\n				Teaching Format\n				The course will be mainly practical\, with some theoretical lectures. All modelling processes and calculations will be performed with R\, the free software environment for statistical computing and graphics (http://www.r-project.org/). Students will learn to use functions implemented in the packages “usdm”; “dismo”; “ENMEval”; “SDMvspecies”; “spThin”; and “NicheMapper” among others. \n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				A basic understanding of ecological niche models and biogeography in general is required\, thus we will assume the attendees know how to run an ecological niche model. \n			\n				\n				\n				\n				\n				Assumed computer background\n				Solid knowledge in Geographical Information Systems and R statistical package is necessary. It is also essential to have experience in ecological niche models. We will focus exclusively on advanced methods. If you need an introductory course on ecological niche models\, please consider attending our basic course on PRStatistics (www.prstats.org). \n			\n				\n				\n				\n				\n				Equipment and software requirements\n				\nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n			\n				\n				\n				\n				\n				\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE PROGRAMME\n  \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Monday 11th\n				Classes from 09:30 to 17:30 \nDay One: \n\nENM guide: how to model\nENM R packages.\nSources of environmental variables using geodata package.\nGetting species records with geodata package.\n\n			\n				\n				\n				\n				\n				Tuesday 12th\n				Classes from 09:30 to 17:30 \nDay Two: \n\nVariable selection with variance inflation factor (VIF) and usdm packages.\nChoosing the correct study area.\nFiltering records using usdm/spThin packages.\nChoosing pseudo-absences with Biomod2 package.\n			\n				\n				\n				\n				\n				Wednesday 13th\n				Classes from 09:30 to 17:30 \nDay Three: \n\nSplit records in training and test with ENMeval package.\nTest effect of Maxent regularization parameter.\nComparing correlative models with AIC\, with ENMeval package.\n\n			\n				\n				\n				\n				\n				Thursday 14th\n				Classes from 09:30 to 17:30 \nDay Four: \n MESS practice with Biomod2 package. \n Validate models null models. \n VirtualSpecies virtualspecies packages. \n			\n				\n				\n				\n				\n				Friday 15th\n				Classes from 09:30 to 17:30 \nDay Five: \n\nMechanistic model NicheMapper packages.\n\n			\n			\n				\n				\n				\n				\n				\n				\n					Dr. Neftali Sillero\n					\n					Neftalí Sillero works in the analysis and identification of biodiversity spatial patterns\, from species to populations and individuals. For this\, he uses four powerful tools to better understand how space influence biodiversity: Geographical Information Systems\, Remote Sensing\, Ecological Niche Modelling\, and Spatial Statistics. His main areas of research are: application of new technologies on species’ distributions atlases\, ecological modelling of species’ ranges\, identification of biogeographical regions and species’ chorotypes\, mapping and modelling road-kill hotspots\, and spatial analyses of home ranges. \nHe has more than 10 years’ experience working in ecological niche models. He has authored >70 peer reviewed publications and he is since 2007 Chairman of the Mapping Committee of the Societas Herpetologica Europaea\, where he is the PI of the NA2RE project (www.na2re.ismai.pt)\, the New Atlas of Amphibians and Reptiles of Europe \nPersonal website \nWork Webpage \nResearchGate \nGoogleScholar \n					\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Teaches\n				\nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n			\n				\n				\n				\n				\n				Teaches\n				\nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n			\n			\n				\n				\n				\n				\n				\n				\n					Dr. Salvador Arenas-Castro\n					\n					Dr. Salvador Arenas-Castro is a broad-spectrum ecologist with interesting in differentintegrative perspective of the fundamental ecology\, macroecology and biogeographywith their both application and relationship to climate and land management. He is alsoexploring other research sources in agroecology\, forestry\, spatial ecology\, andecoinformatics\, all addressed by explicitly considering the spatial component ofecological processes\, mainly applying spatially explicit modelling approaches\, GIS andremote sensing techniques. Please check his webpage for further information:https://salvadorarenascastro.wordpress.com \nGoogle Scholar: https://scholar.google.com/citations?user=UAYiB5UAAAAJ&hl=es&oi=aoResearchGate: https://www.researchgate.net/profile/Salvador-Arenas-Castro
URL:https://prstats.preprodw.com/course/advanced-ecological-niche-modelling-enm-sdm-using-r-anmr02/
LOCATION:Delivered remotely (Portugal)\, Portugal
CATEGORIES:Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2018/07/ANMR011.jpg
GEO:39.399872;-8.224454
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230522
DTEND;VALUE=DATE:20230527
DTSTAMP:20260419T064421
CREATED:20230117T164447Z
LAST-MODIFIED:20230920T131830Z
UID:10000315-1684713600-1685145599@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Introduction to Bayesian modelling with INLA (BMIN02) This course will be delivered live
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nMonday\, May 22nd\, 2023\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCourse Program\nTIME ZONE – UTC+2 – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Details\n				The aim of the course is to introduce you to Bayesian inference using the integrated nested Laplace approximation (INLA) method and its associated R-INLA package. This course will cover the basics on the INLA methodology as well as practical modelling of different types of data. \nBy the end of the course participants should: \n\nUnderstand the basics of Bayesian inference.\nUnderstand how the INLA method works and its main differences with MCMC methods.\nBe able to fit models with the R-INLA package.\nKnow how to interpret the output from model fitting.\nBe confident with the use of INLA for data analysis.\nUnderstand the different models that can be fit with INLA.\nKnow how to define the different parts of a model with INLA.\nBe able to develop new latent effects not implemented in the R-INLA package.\nKnow how to define new priors not included in the R-INLA package.\nHave the confidence to use INLA for their own projects.\n\n			\n				\n				\n				\n				\n				Intended Audiences\n				Academics and post-graduate students working on projects related to data analysis and modelling and who want to add the INLA methodology for Bayesian inference to their toolbox. \nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a command-line language such as R. \nThe course is designed for intermediate-to-advanced R users interested in data analysis and modelling. Ideally\, they should have some background on probability\, statistics and data analysis. \n			\n				\n				\n				\n				\n				Venue\n				Venue – Delivered remotely \n			\n				\n				\n				\n				\n				Course Information\n				Time zone – Central European Standard Time (CEST) \nAvailability – 20 places \nDuration – 5 days \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n  \nPLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \n			\n				\n				\n				\n				\n				Teaching Format\n				\n\nhe course will be a mixture of theoretical and practical sessions. Each concept will be first described and explained\, and next there will be a time to exercise the topics using provided data sets. Participants are also very welcome to bring their own data. \nAssumed quantitative knowledge \nThe course is designed for intermediate-to-advanced R users interested in Bayesian inference for data analysis and R beginners who have prior experience with Bayesian inference. \nAssumed computer background \nAttendees should already have experience with R and be familiar with data from different formats (csv\, tab\, etc.)\, create simple plots\, and manipulate data frames. Furthermore\, knowledge of how to fit generalized linear (mixed) models using typical R functions (such as glm and lme4) will be useful. \nEquipment and software requirements \nA laptop/personal computer with any operating system (Linux\, Windows\, MacOS) and with recent versions of R (https://cran.r-project.org) and RStudio (https://www.rstudio.com) installed; both are freely available as open-source software. You will be sent a list of packages prior to the course. It is essential that you come with all necessary software and packages already installed. \nhttps://cran.r-project.org/ \n\nDownload RStudio \n\nUNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com \n\n\n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				Although an introduction to the INLA method will be given\, attendants are expected to be familiar with Bayesian inference. This includes how to define simple Bayesian models and have a basic understanding of some typical methods to compute or approximate the prior distributions (such as models with conjugate priors\, MCMC methods\, etc.). \n			\n				\n				\n				\n				\n				Assumed computer background\n				Attendants are expected to be familiar with the R programming environment for data analysis. No previous background on handling of spatial and spatio-temporal data will be assumed. \n			\n				\n				\n				\n				\n				Equipment and software requirements\n				Attendees will need to install/update R/RStudio and various additional R packages. \nThis can be done on Macs\, Windows\, and Linux. \nR – https://cran.r-project.org/ \nRStudio – https://www.rstudio.com/products/rstudio/download/ \n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n \n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \n			\n				\n				\n				\n				\n				If you are unsure about course suitability\, please get in touch by email to find out more \ninfo@clovertraining.co.uk \n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Monday 22nd\n				Classes from 14:00 to 21:00 \nIntroduction to the courseKey concepts related to Bayesian inferenceModels with conjugate priorsIntroduction to Bayesian hierarchical modelsComputational methods for Bayesian inferenceIntroduction to the INLA methodologyFitting generalized linear models with INLA and the R-INLA packageUnderstanding and manipulating the output from model fitting with R-INLA \n			\n				\n				\n				\n				\n				Tuesday 23rd\n				Classes from 10:00 – 17:00Fitting generalized linear mixed models with R-INLATypes of latent effects in R-INLAModels with i.i.d. latent effectsFitting multilevel models with R-INLAModels with correlated latent effectsFitting time series models with R-INLA \n			\n				\n				\n				\n				\n				Wednesday 24th\n				Classes from 14:00 – 21:00Priors in R-INLASetting priors in R-INLAIntroduction to Penalized Complexity priors (PC-priors)Defining new priors in R-INLASpatially correlated random effectsFitting spatial models with R-INLAVisualizing the output from spatial models and mapping \n			\n				\n				\n				\n				\n				Thursday 25th\n				Classes from 14:00 – 21:00Advanced features in R-INLAComputing linear combinations of the latent effectsFitting models with several likelihoodsModels with shared termsAdding linear constraints to the latent effectsImplementing new latent models in R-INLAImputation and missing covariates in R-INLA \n			\n				\n				\n				\n				\n				Friday 26th\n				Classes from 14:00 to 21:00 \nCase studies and own data. \n			\n			\n				\n				\n				\n				\n				Course Instructor\n			\n				\n				\n				\n				\n				\n				\n					Dr Virgillio Gomez Rubio\n					Works at: Universdad de Castilla~La Mancha \n					Virgilio has ample experience in Bayesian inference and statistical modeling as well as developing packages for the R programming language. His book Bayesian inference withINLA has been widely adopted for Bayesian modeling and it has been awarded the 2022 SEIO-BBVA Foundation Award in the category of Data Science and Big Data. You can find more information about him on his website.\n\n\nResearchgate: https://www.researchgate.net/profile/Virgilio-Gomez-Rubio?ev=hdr_xprf\nGoogle Scholar: https://scholar.google.es/citations?user=OggVQCkAAAAJ&hl=es\nORCID: https://orcid.org/0000-0002-4791-3072\nGitHub: https://github.com/becarioprecario\nHomepage: https://becarioprecario.github.io
URL:https://prstats.preprodw.com/course/introduction-to-bayesian-modelling-with-inla-bmin02/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2023/02/book_cover.jpg
GEO:55.378051;-3.435973
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230327
DTEND;VALUE=DATE:20230331
DTSTAMP:20260419T064421
CREATED:20221114T174423Z
LAST-MODIFIED:20230221T185051Z
UID:10000419-1679875200-1680220799@prstats.preprodw.com
SUMMARY:ONLINE COURSE – A Non Mathematical Introduction To Ordination Methods Using R (ORDM01) Registration deadline 27th February  - This course will be delivered live
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nMonday\, March 27th\, 2023\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE FORMAT\nThis is a ‘LIVE COURSE’ – the instructors will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTIME ZONE\nTIME ZONE – EST – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				About This Course\n				This course will introduce you to the main methods of both constrained and unconstrained ordination without entering into the mathematical details of these methods. The following methods will be studied: principal component analysis; correspondence analysis and its detrended version; principal coordinates analysis; metric and non-metric multidimensional scaling; redundancy analysis\, canonical correspondence analysis; gradient analysis using siteXspecies data.\nBy the end of the course\, participants should be able to:\n\nUnderstand how each method works and the assumptions inherent in each;\nChoose the most appropriate method relative to their data and goals;\nCarry out the analyses in the R statistical environment\nInterpret their results\n\n			\n				\n				\n				\n				\n				Intended Audiences\n				\nGraduate or post-doctoral level researchers who wish to learn how to perform ordination techniques in R;\nApplied researchers and analysts in the environmental/ecological sector with a role in handling and analysing data\n\n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely \n			\n				\n				\n				\n				\n				Course Details\n				Time Zone – EST \nAvailability – TBC \nDuration – 4 days \nContact hours – Approx. 30 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n			\n				\n				\n				\n				\n				Teaching Format\n				This course will comprise a mixture of taught theory and practical examples. Data and analytical approaches will be presented in a lecture format to introduce key concepts. Statistical analyses will then be presented using R. All R script that the instructor uses during these sessions will be shared with participants\, and R script will be presented and explained. \nIdeally\, participants will be able to use a computer screen that is sufficiently large to enable them to view my shared RStudio and their own RStudio simultaneously. \n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				I assume that participants have a basic knowledge of general statistical concepts and of linear models. \n			\n				\n				\n				\n				\n				Assumed computer background\n				Experience with performing statistical analyses using R and R Studio will be assumed. \n			\n				\n				\n				\n				\n				Equipment and software requirements\n				A computer with the most recent version of R and RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. \nA full list of required packages will be made available to participants prior to the course. \nIdeally\, participants will be able to use a computer screen that is sufficiently large to enable them to view my shared RStudio and their own RStudio simultaneously. \n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n			\n				\n				\n				\n				\n				\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE PROGRAMME\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Monday 27th\n				Day 1 09:00 – 16:00
 \nPrincipal components analysis (PCA) \n\nA graphical explanation of how PCA works\nData preparation and basic assumptions\nDealing with proportions\nUsing a covariance matrix or a correlation matrix?\nSteps in fitting a PCA\nEvaluating the importance of each axis\nRelating variables to the axes\nRelating observations to the axes\nChoosing which axes to use\nGraphical visualizations using biplots\n\nCorrespondence analysis (CA) & detrended correspondence analysis (DCA) \nDirect gradient analysis in ecology\nCA as a form of direct gradient analysis\nSteps in fitting a CA\nBias due to the “arch effect” and its correction by detrending\nAn empirical example\nInterpreting the output and graphical presentation\n\n			\n				\n				\n				\n				\n				Tuesday 28th\n				Day 2  09:00 – 16:00
\nPrincipal coordinates analysis (PCoA) \n\nDistance and dissimilarity measures\nMeasures for nominal categorical\, binary\, ordinal and quantitative variables\nGower’s distance\nPCA and CA as special cases of multidimensional scaling\nA graphical explanation of how PCoA works\nSteps in fitting a CA\nPerforming PCoA in R\n\nMetric (MDS) and non-metric multidimensional scaling (NMDS) \nWhat is multidimensional scaling and how does it work?\nWhat is non-metric multidimensional scaling?\nPerforming NMDS in R.\nGraphical methods for evaluating and interpreting NMDS results\nProcrustes analysis\nAn empirical example\n			\n				\n				\n				\n				\n				Wednesday 29th\n				Day 3 09:00 – 16:00 \nConstrained ordinations \nExploratory vs. inferential statistical methods\nRedundancy analysis (RDA)\nObtaining output from rda()\nHypothesis testing with rda()\nPartial RDA\nCanonical correspondence analysis (CCA)\nPartial CCA\nHypothesis testing with CCA\nDistance-based redundancy analysis (db-RDA)\nEmpirical example\n			\n				\n				\n				\n				\n				Thursday 30th\n				Day 4 09:00 – 12h00 \nGradient analysis using siteXspecies data\nSimulating environmental gradients\nUsing simulations to compare ordination methods\nThe “horseshoe” or “arch” effect\nFlexible shortest path adjustments\nRecommendations for siteXspecies ordinations\nImplications for constrained ordination methods\n			\n			\n				\n				\n				\n				\n				Course Instructor\n			\n				\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				Prof. John (Bill) Shipley\nBill Shipley is an experienced researcher and  teacher in plant ecology and statistical ecology. He has published four scientific monographs and over 170 peer-reviewed papers.
URL:https://prstats.preprodw.com/course/a-non-mathematical-introduction-to-ordination-methods-using-r-ordm01/
LOCATION:Delivered remotely (Canada)
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/11/Picture-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230313
DTEND;VALUE=DATE:20230318
DTSTAMP:20260419T064421
CREATED:20250203T111240Z
LAST-MODIFIED:20250203T111246Z
UID:10000469-1678665600-1679097599@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Phylogenetic Species Distribution Modelling using R (PSDM01) This course will be delivered live
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nMonday\, May 12th\, 2025\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nPre Recorded\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				About This Course\n				In this three-day course\, we introduce species distribution models (SDMs) and ways toincorporate phylogenetic information into single species models using R. We begin byproviding an overview on the use of SDMs as a central tool for ecologists and evolutionarybiologists\, review and implement common SDM approaches and introduce hybrid models\,which use the information in functional traits to complement the models. We then justifythe rationale for using phylogenetic information in absence of functional trait data andshow how to incorporate phylogenetic information in SDMs (day 1). We review examplesof practical implementation of PSDMs to both present and future climate scenarios (day 2).Finally\, we overview more advanced approaches of incorporating phylogenies into models(the Bayesian Phylogenetic Mixed Model) and how to project model results into a spatialcontext (day 3). \n			\n				\n				\n				\n				\n				Intended Audiences\n				This course is aimed at anyone who wishes to improve/complement their use of SpeciesDistribution Models using phylogenies. \n			\n				\n				\n				\n				\n				Course Details\n				Venue – Delivered remotelyAvailability – 20 placesDuration – 3 daysContact hours – Approx. 18 hoursECT’s – Equal to 2 ECT’sLanguage – English \n			\n				\n				\n				\n				\n				Teaching Format\n				The course will be hands-on and workshop based. Throughout each day\, there will be some introductory remarks for each new topic\, introducing and explaining key concepts. \nThe course will take place online using Zoom. On each day\, the live video broadcasts willoccur between (UK local time) at:• 8:00am-10:00am• 11:00pm-13:00pm• 14:00pm-16:00pm \nAll sessions will be video recorded and made available to all attendees. \nAttendees in different time zones will be able to join into some of these live broadcasts\, even if all of them are not convenient times. \nBy joining any live sessions that are possible\, this will allow attendees to benefit Fromm asking questions and having discussions\, rather than just watching prerecorded sessions. \nAll the sessions will be video recorded\, and made available immediately on a private video hosting website. Any materials\, such as slides\, data sets\, etc.\, will be shared with the attendees. \n			\n				\n				\n				\n				\n				Assumed quantative knowledge\n				We will assume general familiarity with the very basics of statistics (e.g. summarystatistics\, distributions). \n			\n				\n				\n				\n				\n				Assumed computer background\n				We will assume general familiarity with R elementary operations (e.g. package sourcing\,data importing and exporting\, object indexing) and some familiarity with programming inR (writing code). \n			\n				\n				\n				\n				\n				Equipment and software requirements\n				\nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer). \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students. \n\n\n\n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\nUNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com \n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\nPLEASE READ – CANCELLATION POLICY \n\n\nCancellations/refunds are accepted as long as the course materials have not been accessed\,. \n\n\nThere is a 20% cancellation fee to cover administration and possible bank fess. \n\n\nIf you need to discuss cancelling please contact oliverhooker@prstatistics.com. \n\n			\n				\n				\n				\n				\n				\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE PROGRAMME\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Monday 12th\n				Classes from 8:00 to 16:00 \nIntroduction to species distribution models: rationale\, algorithms\, validation and applications. \n• Working with SDMs. Implementing SDMs in R.• Hybrid-SDMs or how to incorporate functional information into the models.• What to do in absence of functional trait data? The rationale for using the latent information in phylogenies instead.• The phylogenetic predictor. \n			\n				\n				\n				\n				\n				Tuesday 13th\n				Classes form 08:00 – 16:00 \nPutting phylogenies in the geography: how to combine phylogenies with speciesdistribution models in R. \n• Phylogenetic information can improve both present and future predictions ofspecies distributions.• Projecting phyloSDMs across space and time in R.• When and why phylogenies can and can’t improve models. \n			\n				\n				\n				\n				\n				Wednesday 14th\n				Classes form 08:00 – 16:00 \nPhylogenies also improve models for the temporal distribution of species. \n\nThe Bayesian Phylogenetic Mixed Model\nExamples of implementation of PMMs and extrapolating their predictions to thegeography in R.\n\n			\n			\n				\n				\n				\n				\n				Course Instructor\nDr. Morales Castilla Ignacio \n 
URL:https://prstats.preprodw.com/course/phylogenetic-species-distribution-modelling-using-r-psdm01/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom
CATEGORIES:Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/ECPH01R.png
GEO:53.1423672;-7.6920536
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230310
DTEND;VALUE=DATE:20230311
DTSTAMP:20260419T064421
CREATED:20230118T151219Z
LAST-MODIFIED:20230301T114349Z
UID:10000423-1678406400-1678492799@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Introduction to Multi’omics Data Analysis from Microbial Communities (MOMC01) This course will be delivered live
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nFriday\, March 10th\, 2023\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCourse Program\nTIME ZONE – UTC+2 \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Details\n				The aim of this one-day workshop is to provide a thorough introduction to computational\napproaches for the analysis of microbial community profiles with a focus on metagenomic\nsequencing data. We will explain how taxonomic and functional profiles are generated from\nraw sequencing data\, introduce different bioinformatic approaches to process sequencing\ndata\, followed by multivariate statistical analysis and different visualization techniques. The\ncourse will consist of a mixture of lectures and hands-on tutorials. The practical part of the\ncourse will focus on the analysis of publicly available multi-omics profiles. \nBy the end of the course participants should:\n1. Be familiar with different workflows involved in the analysis of large-scale multi-\nomics studies.\n2. Understand how to generate taxonomic\, functional and strain profiles from\nmetagenomic sequencing data.\n3. Be familiar with applying a multivariate statistical framework to generate hypotheses\nand account for confounding covariates.\n4. Be able to use exploratory data visualizations techniques and visualize results from\nthe statistical analysis using R.\n			\n				\n				\n				\n				\n				Intended Audiences\n				Academics\, post-graduate students and researchers working on projects related to microbial community studies\, who want to learn computational approaches for the analysis of high-dimensional sequencing data.\n			\n				\n				\n				\n				\n				Venue\n				Venue –  Delivered remotely\n			\n				\n				\n				\n				\n				Course Information\n				\nTime zone – GMT \nAvailability – 20 places \nDuration – 1 day \nContact hours – Approx. 7 hours \nECT’s – Equal to 1 ECT \nLanguage – English \n\n\n\n\n\n\n\n\n			\n				\n				\n				\n				\n				Teaching Format\n				\n\n\nThe course will be held virtually and consists of a mixture of theoretical and practical\nsessions. The concepts and tools will be first described and explained\, followed by a lab\nsession with hands-on experience of applying the tool to provided data sets. At the end of\nthe day there will be additional time for questions. \n\n\n\n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				Attendees are assumed to have a basic understanding of microbial community studies. The\ncourse will not cover data generation aspects (sample collection\, library preparation etc) but\nfocus on how to analyse sequencing data and taxonomic/functional microbial community\nprofiles.\n			\n				\n				\n				\n				\n				Assumed computer background\n				Familiarity with the command line interface (bash/shell) and R is an advantage. We will offer\nshort introductory labs for both to make the course more accessible to a wider audience.\nWe also encourage attendees to get familiar with zoom prior to the course.\n			\n				\n				\n				\n				\n				Equipment and software requirements\n				Attendees are expected to have their own laptop with a microphone and ideally a camera.\nWe encourage all attendees to keep their camera on during the lectures and tutorials. Zoom\nshould be installed prior to the course. For the tutorials will use R Studio Cloud which you\ncan access through your browser. The setup instructions for R Studio Cloud will be sent prior\nto the course start. \nRStudio – https://www.rstudio.com/products/rstudio/download/\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n			\n				\n				\n				\n				\n				If you are unsure about course suitability\, please get in touch by email to find out more \noliverhooker@prstatistics.com\n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE PROGRAMME\n\nFriday 10th – Classes from 09:00 to 18:00 \nLecture 1 – general Introduction \nLecture 2 – Introduction to microbial community analysis \nPractical 1 – Introduction to R and R notebook \nLecture 3 – Metagenomic data visualisation and exploratory analysis with cMD \nPractical 2 – Metagenomic data visualisation \nLecture 4 – Statistics for microbial multi-comics data\, methods for multi-comics integration \nPractical 3 – Multivariate analysis (Linear models and/or MaAsLin2 \nLecture 5 – Large-scale multi-omics studies \nConclusions – Discussion\, questions\, wrap up! \n\n			\n				\n				\n				\n				\n				Course Instructor\n  \nDr. Melanie Schirmer\nMelanie is a computational biologist studying the human microbiome and its role in health and disease as an Emmy Noether Group leader at the Technical University in Munich\, Germany. In many diseases\, such as chronic inflammatory bowel diseases and immune-related diseases\, an imbalance of the microbial communities\, that live in and on our bodies\, has been observed. The underlying reasons and consequences of this imbalance are largely unknown though. Previous studies have identified taxonomic changes of the microbiome and disease-associated bacterial species. However\, different strains of the same species can substantially differ in their functional capacities. Therefore\, it is crucial to investigate functional and metabolic differences of microbial strains\, in order to develop effective therapies and strategies to prevent these diseases. We are addressing these questions with computational analyses of multi-omics data in combination with experimental validation of the immunogenicity and inflammatory activity of the identified strains. Our research provides insights into the potential mechanisms of the human microbiome in autoimmune and inflammatory diseases. \nWorks at – Technical University of Munich \nTeaches – Introduction to Multi’omics Data Analysis of Microbial Communities (MOMC)
URL:https://prstats.preprodw.com/course/introduction-to-multiomics-data-analysis-from-microbial-communities-momc01/
LOCATION:Delivered remotely (USA east)\, Eastern Daylight Time\, MD\, United States
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2020/11/MOMC01.jpg
GEO:56.4906712;-4.2026458
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230130
DTEND;VALUE=DATE:20230204
DTSTAMP:20260419T064421
CREATED:20220608T132224Z
LAST-MODIFIED:20221220T091139Z
UID:10000410-1675036800-1675468799@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Introduction to Aquatic Acoustic Telemetry (IAAT02) This course will be delivered live
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nMonday\, January 30th\, 2023\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCourse Program\nTIME ZONE – UK Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Details\n				Acoustic telemetry is a popular method for monitoring the movements and behaviour of aquatic animals globally. Increasingly smaller tags along with improvements in battery technology have allowed for tagging a wide varietyof species and life stages\, enabling monitoring of individuals as small as salmon smolts and as large as whale sharks for periods from 30 days to 10 years. In addition\, with more and more tag sensor and environmentalmonitoring options available\, telemetry datasets are becoming richer\, allowing researchers to answer increasingly complex questions about why animals move where and when they do. Receiver technology also continues to evolve and increasingly allows for data to be collected at finer spatial and temporal scales than ever before. New technologies such as gliders and real-time detection systems allow broad geographic coverage and remote\, real-time access to animal movement data. Additional advancements in built-in acoustic array diagnostics permit increasingly detailed analyses of system performance over time\, resulting in more robust interpretation of animalmovement data. \nIn this course you will learn about the different types of Innovasea acoustic telemetry technologies and their applicability for use in different study environments and in answering different research questions. Exampleapplications that will be discussed include: monitoring fine-scale movements and behaviour around barriers and other structures\, migration survival studies around barriers\, monitoring spawning and other seasonal behaviours\,real-time monitoring\, home range and Marine Protected Area studies\, habitat selection\, species interactions\, and investigating causes of mortality. This section will include a deep dive into the logistics of fine-scale positioningstudies and will provide an overview of the different types of analyses that are commonly performed with positional data. \nA robust telemetry study design that accounts for the advantages and limitations of different equipment options is critical to ensure a successful study. During this course\, you will learn about important study design considerations such as appropriate hardware models and tag programming parameters for your study objectives\, tag attachment considerations\, and how to optimize your receiver placements through early and thorough testing. We will analyze an example range test dataset and discuss the implications of range test results on array design. We will also review considerations when designing moorings for your particular study environment. \nTo ensure successful execution of your telemetry study plan and the highest quality data\, it’s important to use telemetry best practices when preparing for and during tagging and deployment. We will review best practices for acoustic telemetry equipment maintenance and care\, pre-deployment testing\, tips for preparing equipment and data logs\, and how to test your array once deployed in the field. We will also look at how to monitor the performance of your telemetry array throughout the duration of your study\, reviewing what metrics are best used to determine whether the array is operating as planned so you can have confidence in the data being collected. Finally\, because interpretation of acoustic telemetry data and inferring animal behaviour from these data is often confounded by array performance questions\, this course will teach you techniques for assessing system performance to aid in the correct interpretation of animal detection data. Finally\, since telemetry datasets are growing larger all of the time\, data management is becoming increasingly challenging. During this course you will learn about data management best practices and tools to perform basic quality assurance\, basic visualizations\, and basic filtering of large datasets in preparation for statistical analyses. You will also have the opportunity to discuss your own telemetry studies with the experts during a Q&amp;A sessionon the final day of the course. Bring your data and your questions! \n			\n				\n				\n				\n				\n				Intended Audiences\n				This course is suitable for research postgraduates\, practicing academics\, or persons in industry or government who are working with acoustic telemetry\, or planning an acoustic telemetry study\, to monitor aquatic animal movement and behaviour.  This course focuses on applications of the technology and best practices for obtaining meaningful animal movement  data using acoustic telemetry.\n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely \n			\n				\n				\n				\n				\n				Course Information\n				Availability – 30 places \nDuration – 4 days \nContact hours – Approx. 16 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English \n			\n				\n				\n				\n				\n				Teaching Format\n				This course offers 16 hours of acoustic telemetry content over 4 days.  Each day consists of 2 hours lecture\, 1 hour break\, and another 2 hours lecture.  Lecture days are Monday\, Tuesday\, Thursday and Friday.  Friday’s lecture is shorter with remaining time dedicated to interactive participant Q&A and study design or data review.  The course will take place online. \n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				We will assume that you are familiar with basic statistical concepts\, linear models\, and statistical tests\, however statistics knowledge is not required to benefit from this course. \n			\n				\n				\n				\n				\n				Assumed computer background\n				Familiarity with data manipulation in Microsoft Excel and ability to import/export data into a data management / statistical package of your choice. \n			\n				\n				\n				\n				\n				Equipment and software requirements\n				Computer work is not required during this course however it will be possible for participants to follow along with software demonstrations during the lectures.  Software download links will be provided prior to the course.  Desktop software applications require a modern Windows operating system. \nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \nDownload Zoom \n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n \n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \n			\n				\n				\n				\n				\n				If you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE PROGRAMME\n\n\nMonday 30th – Classes from 12:30 to 17:30 \nIntroductionTypes of Telemetry TechnologyHow Does Acoustic Telemetry Work? \nFine-Scale Positioning Deep Dive \nAcoustic Telemetry Applications – Part IExample case studies include: fine-scale monitoring around barriers\, migration survival studies\, spawning behaviour\, real-time monitoring\, home range and MPA use\, habitat selection\, species interactions\, and investigation into causes of mortality. \nTuesday 31st – Classes from 12:30 to 17:30 \nAcoustic Telemetry Applications – Part IIExample case studies include: fine-scale monitoring around barriers\, migration survival studies\, spawning behaviour\, real-time monitoring\, home range and MPA use\, habitat selection\, species interactions\, and investigation into causes of mortality \nDesigning a Successful Acoustic Telemetry StudyDefining the Question and Data NeedsHardware SelectionTag ProgrammingTag Attachment MethodRange TestingReceiver PlacementReceiver Mooring Design \nThursday 2nd – Classes from 12:30 to 17:30 \nRunning Your Acoustic Telemetry StudyEquipment Maintenance &amp; CarePre-Deployment TestingPreparing for Tagging &amp; DeploymentTesting Your Deployment \nSystem Performance AssessmentIn the FieldPost-Study Performance Assessment \nFriday 3rd – Classes from 12:30 to 17:30 \n Data Management Data Quality Assurance Acoustic Telemetry Q &amp; A \n\n\n			\n				\n				\n				\n				\n				Course Instructor\nStephanie Smedbol\nDirector of Product Management &amp; Customer Success\, Innovasea Fish TrackingStephanie has been working with Innovasea for 12 years in a number of roles in R&amp;D\, Product\, Management\, and Customer Success.  Her primary areas of focus are ensuring that researchers get the most out of their telemetry equipment and data\, and improving telemetry products and services to enable new and better science. Stephanie’s areas of expertise include telemetry study design\, telemetry field work\, telemetry system performance analysis\, technical training\, and technical problem solving.​ Stephanie has a Bachelor ofScience in Biology from McGill University (Montreal\, Canada) and a Bachelor of Engineering (Electrical) from Dalhousie University in Nova Scotia\, Canada.​ \nColleen Burliuk\nResearch Biologist\, Innovasea Fish Tracking \nCourtney MacSween\nCustomer Engagement Coordinator\, Innovasea Fish Tracking
URL:https://prstats.preprodw.com/course/online-course-introduction-to-aquatic-acoustic-telemetry-iaat02/
LOCATION:Delivered remotely (Canada)
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/ATDA01.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20230116
DTEND;VALUE=DATE:20230307
DTSTAMP:20260419T064421
CREATED:20221017T154832Z
LAST-MODIFIED:20221017T162301Z
UID:10000417-1673827200-1678147199@prstats.preprodw.com
SUMMARY:ONLINE COURSE – Trait based ecology Using R: Theory and Practice (TBER01)  This course will be delivered live
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nMonday\, January 16th\, 2022\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – GMT – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				About This Course\n				This course introduces the participants to the main concepts and methods of trait-based ecology. While traits have been used in ecology for a long time\, an approach explicitly based on traits has been increasingly introduced to almost all aspects of ecological research in the last two decades. In particular\, since the early 2000s\, methodological developments have really flourished\, up to a point that it is hard to keep track of such developments. In this course\, we will combine lectures providing an overview of the main principles and methods of trait-based ecology with practices using the statistical software R\, so that participants will acquire a knowledge of available R packages and customized functions\, and how to use them in the context of trait-based analyses. The course will span methods taking both species-level and community-level perspectives that can be applied to a large variety of organisms. Additional practical aspects that will be covered include the choice of the “right” traits for a given study\, what to consider when using trait data from data bases\, and how to design and optimize your own trait sampling campaign. The course is largely based on the book recently published by Cambridge University Press “Handbook of Trait-Based Ecology: From Theory to R Tools” and the accompanying R material. The book is not required for course participation. \n  \n  \n			\n				\n				\n				\n				\n				Intended Audiences\n				Master and PhD students\, as well as post docs and established researchers new to the topic\, who are at the start of their own trajectory in trait-based ecological research. \n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely \n			\n				\n				\n				\n				\n				Course Details\n				Time zone – GMTAvailability – 30 places \nDuration – 8 days (4 hours per day\, one day per week\, for 8 weeks) \nContact hours – Approx. 32 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n  \n			\n				\n				\n				\n				\n				Teaching Format\n				The course will consist in 1 teaching block per week\, for 8 weeks. Each block will consist of approximately 3.5 hours of interactive live online sessions (at xx:xx GMT time)\, which will include theoretical lectures\, discussion\, and demonstrations of R code of selected packages and functions and approximately 4 hours of practical’s that each participant will do on their own schedule / time zone\, based on annotated self-explanatory R scripts. The instructor will be available for questions and help during Western European working hours and a bit beyond that\, depending on the participants’ time zones. Data sets and R codes for practicals will be provided\, so that participants can repeat and extend the methods demonstrated during the lectures\, at their own convenience. \n  \n  \n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				A basic knowledge of uni- and multivariate statistical analyses is assumed (correlation\, simple regression models\, unconstrained and constrained ordination\, e.g. PCA\, RDA). Without such knowledge the course can probably be followed for most parts\, but the practicals will be much less efficient for the student. \n			\n				\n				\n				\n				\n				Assumed computer background\n				Participants should have basic experience in working with the R statistical environment\, preferably in connection with the R studio interface. They should be familiar with importing data to R\, installing and loading packages\, and basic plot functions. \n			\n				\n				\n				\n				\n				Equipment and software requirements\n				\nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n  \n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n \n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \n			\n				\n				\n				\n				\n				\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE PROGRAMME\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Monday 16th January\n				Classes from 10:30 to 14:30\nInstructor Francesco Bello \nTheory\nIntroduction\, definitions\, response and effect\, functional groups\, trade-offs\, Gower distance. \nPractical\nPeople’s trait game\nGower distance\n			\n				\n				\n				\n				\n				Monday 23rd January\n				Classes from 10:30 to 14:30\nInstructor Francesco Bello \nTheory\nCommunity Weighted Mean (CWM) and Functional Diversity (FD) \nPractical\nCWM & FD\n			\n				\n				\n				\n				\n				Monday 30th January\n				Classes from 10:30 to 14:30\nInstructor Lars Götzenberger \nTheory\nResponse traits and environmental filtering \nPractical\nKleyer appendix\n			\n				\n				\n				\n				\n				Monday 6th February\n				Classes from 10:30 to 14:30\nInstructor Carlos Pérez Carmona \nTheory\nCommunity assembly \nPractical\nBasics of null-models\n			\n				\n				\n				\n				\n				Monday 13th February\n				Classes from 10:30 to 14:30\nInstructor Matty Berg & Carlos Pérez Carmona \nTheory\nIntraspecific trait variability \nPractical\nTrait overlap (trova)\, Trait variance\, CWM flex anova\n			\n				\n				\n				\n				\n				Monday 20th February\n				Classes from 10:30 to 14:30\nInstructor Lars Götzenberger \nTheory\nPhylogeny \nPractical\nConservatism\, Phylogenetic diversity\, PICs\n			\n				\n				\n				\n				\n				Monday 27th February\n				Classes from 10:30 to 14:30\nInstructor Marco Moretti & Francesco Bello \nTheory\nResponse & Effect traits \nPractical\nSelection/Complementarity\, Lautaret\, multitrophic\n			\n				\n				\n				\n				\n				Monday 6th March\n				Classes from 10:30 to 14:30\nInstructor Lars Götzenberger & Carlos Pérez Carmona \nTheory\nMissing traits\, databases\, sampling traits \nPractical\nDatabases extraction\, sampling game\, data imputation
URL:https://prstats.preprodw.com/course/online-course-trait-based-ecology-using-r-theory-and-practice-tber01-this-course-will-be-delivered-live/
LOCATION:Delivered remotely (Portugal)\, Portugal
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/10/Screenshot-2022-10-17-at-17.03.22.png
GEO:39.399872;-8.224454
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20221212
DTEND;VALUE=DATE:20221217
DTSTAMP:20260419T064421
CREATED:20220302T115332Z
LAST-MODIFIED:20221019T151411Z
UID:10000400-1670803200-1671235199@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Ecological niche modelling using R (ENMR04) This course will be delivered live
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nMonday\, December 12th\, 2022\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – Western European Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				About This Course\n				\nThe course will cover the base theory of ecological niche modelling and its main methodologies. By the end of this 5-day practical course\, attendees will have the capacity to perform ecological niche models and understand their results\, as well as to choose and apply the correct methodology depending on the aim of their type of study and data. \nEcological niche\, species distribution\, habitat distribution\, or climatic envelope models are different names for similar mechanistic or correlative models\, empirical or mathematical approaches to the ecological niche of a species\, where different types of ecogeographical variables (environmental\, topographical\, human) are related with a species physiological data or geographical locations\, in order to identify the factors limiting and defining the species’ niche. ENMs have become popular due to the need for efficiency in the design and implementation of conservation management. \nThe course will be mainly practical\, with some theoretical lectures. All modelling processes and calculations will be performed with R\, the free software environment for statistical computing and graphics (http://www.r-project.org/). Attendees will learn to use modelling algorithms like Maxent\, Bioclim\, Domain\, and logistic regressions\, and R packages for computing ENMs like Dismo and Biomod2. Also\, students will learn to compare different ecological niche models using the Ecospat package. \n  \n\n			\n				\n				\n				\n				\n				Intended Audiences\n				\nThis course is orientated to PhD and MSc students\, as well as persons in researcher or industry working on biogeography\, spatial ecology\, or related disciplines. \n\n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely \n			\n				\n				\n				\n				\n				Course Details\n				Time Zone – Western European Time \nAvailability – 24 Places \nDuration – 5 days \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n			\n				\n				\n				\n				\n				Teaching Format\n				Introductory lectures on the concepts and applications of ENM. Practical lectures on most used ENM methods. Presentations and round-table discussions about the analysis requirements of attendees (option for them to bring their own data). Data sets for computer practicals will be provided by the instructor\, but participants are welcome to bring their own data. \n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				Basic knowledge in Geographical Information Systems and spatial analyses.\n			\n				\n				\n				\n				\n				Assumed computer background\n				Familiarity with GIS software like QGIS. Ability to visualise shapefiles and raster files. Familiarity with R. Ability to import/export data\, manipulate data frames\, fit basic statistical models & generate simple exploratory and diagnostic plots.\n			\n				\n				\n				\n				\n				Equipment and software requirements\n				A laptop/personal computer with a working version or R and RStudio installed. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links \n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n			\n				\n				\n				\n				\n				\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n  \n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE PROGRAMME\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Monday 12th\n				Classes from 09:30 to 17:30 \nElementary concepts on Ecological Niche Modelling \nModule 1: Introduction to ENM theory. Definition of ecological niche model; introduction to species ecological niche theory\, types of ecological niches\, types of ENM\, diagram BAM\, ENMs as approximations to species’ niches. \nModule 2: Problems and limitations on ENM. Assumptions and uncertainties\, equilibrium concept\, niche conservatism\, autocorrelation and intensity\, sample size\, correlation of environmental variables\, size and form of study area\, thresholds\, model validation\, model projections. \nModule 3: Methods on ENM. Mechanistic and correlative models. Overlap Analysis\, Biomod\, Domain\, Habitat\, Distance of Mahalanobis\, ENFA\, GARP\, Maxent\, Logistic regression\, Generalised Linear Models\, Generalised Additive Models\, Generalised Boosted Regression Models\, Random Forest\, Support Vector Machines\, Artificial Neural Network. \nModule 4: Conceptual and practice steps to calculate ENM. How to make an ENM step-by-step. \nModule 5: Applications of ENM. Ecological niche identification\, Identification of contact zones\, Integration with genetical data\, Species expansions\, Species invasions\, Dispersion hypotheses\, Species conservation status\, Prediction of future conservation problems\, Projection to future and past climate change scenarios\, Modelling past species\, Modelling species richness\, Road-kills\, Diseases\, Windmills\, Location of protected areas. \n			\n				\n				\n				\n				\n				Tuesday 13th\n				Classes from 09:30 to 17:30 \nPrepare environmental variables and run ecological niche models with dismo package. \nModule 6: Preparing variables. Choosing environmental data sources\, Downloading variables\, Clipping variables\, Aggregating variables\, Checking pixel size\, Checking raster limits\, Checking NoData\, Correlating variables. \nModule 7: Dismo practice. How to run an ENM using the R package dismo. \n  \n			\n				\n				\n				\n				\n				Wednesday 14th\n				Classes from 09:30 to 17:30 \nRun ecological niche models with Biomod2 package and Maxent. \nModule 8: Biomod2 practice. How to run an ENM using the R package Biomod2. \nModule 9: Maxent practice. How to run an ENM using the R packages dismo and Biomod2 as well as Maxent software. \n			\n				\n				\n				\n				\n				Thursday 15th\n				Classes from 09:30 to 17:30 \nCompare ecological niche models with ecospat. \nModule 10: Ecospat practice. Compare statistically two different ecological niche models using the R package Ecospat. \nModule 11: Students’ talks. Attendees will have the opportunity to present their own data and analyse which is the best way to successfully obtain an ENM. \n  \n			\n				\n				\n				\n				\n				Friday 16th\n				Classes from 09:30 to 17:30 \nRun ecological niche models with your own data. \nModule 12: Final practical. In this practical\, the students will run ENM with their own data or with a new dataset\, applying all the methods showed during the previous days. \n  \n			\n			\n				\n				\n				\n				\n				\n				\n					Dr. Neftali Sillero\n					\n					Neftalí Sillero works in the analysis and identification of biodiversity spatial patterns\, from species to populations and individuals. For this\, he uses four powerful tools to better understand how space influence biodiversity: Geographical Information Systems\, Remote Sensing\, Ecological Niche Modelling\, and Spatial Statistics. His main areas of research are: application of new technologies on species’ distributions atlases\, ecological modelling of species’ ranges\, identification of biogeographical regions and species’ chorotypes\, mapping and modelling road-kill hotspots\, and spatial analyses of home ranges. \nHe has more than 10 years’ experience working in ecological niche models. He has authored >70 peer reviewed publications and he is since 2007 Chairman of the Mapping Committee of the Societas Herpetologica Europaea\, where he is the PI of the NA2RE project (www.na2re.ismai.pt)\, the New Atlas of Amphibians and Reptiles of Europe \nPersonal websiteWork WebpageResearchGateGoogleScholar \n					\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Teaches\n				\nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n			\n				\n				\n				\n				\n				Teaches\n				\nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)
URL:https://prstats.preprodw.com/course/ecological-niche-modelling-using-r-enmr04/
LOCATION:Delivered remotely (Portugal)\, Portugal
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2021/09/pr-stats-stock-image-64562101-xl-2015.jpeg
GEO:39.399872;-8.224454
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20221115
DTEND;VALUE=DATE:20221119
DTSTAMP:20260419T064421
CREATED:20220609T102652Z
LAST-MODIFIED:20221108T151103Z
UID:10000411-1668470400-1668815999@prstats.preprodw.com
SUMMARY:ONLINE COURSE – Time Series Data Analysis (TSDA02) This course will be delivered live
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nWednesday\, November 16th\, 2022\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – GMT+1 – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				About This Course\n				This course covers introductory modelling for the analysis of time series data. The main focus of the course is on data observed at regular (discrete) time points but later modules cover continuously-observed data. The methods are presented both at a theoretical level and also with practical examples where all code is available. The practical classes include instructions on how to use the popular forecast package. The second half of the course looks at Bayesian time series analysis which is extremely customisable to bespoke data analysis situations. \n			\n				\n				\n				\n				\n				Intended Audiences\n				\nResearch postgraduates\, practicing academics\, or other professionals from any field who would like to learn about time series analysis and how it can help them derive superior insight from their data. \n\n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely \n			\n				\n				\n				\n				\n				Course Details\n				Availability – 30 places \nDuration – 4 days \nContact hours – Approx. 28 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n			\n				\n				\n				\n				\n				Teaching Format\n				\nThe course will be divided into theoretical lectures to introduce and explain key concepts and theories. Afternoon practicals will be based on the topics covered in the morning lectures. \n\n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				A basic understanding of regression methods and generalised linear models. \nSome familiarity with R including the ability to import/export data\, manipulate data frames\, fit basic statistical models\, and generate simple exploratory and diagnostic plots. \n			\n				\n				\n				\n				\n				Assumed computer background\n				Attendees should already have experience with R and be able to read csv files\, create simple plots\, and manipulate data frames. \n			\n				\n				\n				\n				\n				Equipment and software requirements\n				EQUIPMENT AND SOFTWARE REQUIREMENTS\n\n\nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n \n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \n			\n				\n				\n				\n				\n				\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Programme\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Wedesday 16th\n				\n\n\n9:30-10:30\nIntroduction\, example data sets\n\n\n10:30-10:45\nCoffee break\n\n\n10:45-11:45\nRevision: likelihood and inference\n\n\n11:45-12:00\nBreak\n\n\n12:00-13:00\nRevision: linear regression and GLMs\n\n\n13:00-14:00\nLunch\n\n\n14:00-14:45\nTutor-guided practical: Loading data in R and running simple analysis\n\n\n14:45-15:00\nCoffee break\n\n\n15:00-17:00\nSelf-guided practical: Using R for linear regression and GLMs’\n\n\n\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Thursday 17th\n				\n\n\n9:30-10:30\nAuto-regressive models and random walks\n\n\n10:30-10:45\nCoffee break\n\n\n10:45-11:45\nMoving averages and ARMA\n\n\n11:45-12:00\nBreak\n\n\n12:00-13:00\nIntegrated models and ARIMA\n\n\n13:00-15:00\nLunch\n\n\n15:00-15:45\nTutor-guided practical: the forecast package in R\n\n\n15:45-16:00\nCoffee break\n\n\n16:00-17:00\nSelf-guided practical: Fitting ARIMA models with forecast\n\n\n\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Friday 18th\n				\n\n\n9:30-10:30\nIncluding covariates: ARIMAX models\n\n\n10:30-10:45\nCoffee break\n\n\n10:45-11:45\nCreating bespoke time series models using Bayes\n\n\n11:45-12:00\nBreak\n\n\n12:00-13:00\nModel choice and forecasting using Bayes\n\n\n13:00-14:00\nLunch\n\n\n14:00-14:45\nTutor-guided practical: a walkthrough example time series analysis\n\n\n14:45-15:00\nCoffee break\n\n\n15:00-17:00\nSelf-guided practical: finding the best time series model for your data set\n\n\n\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Tuesday 22nd\n				\n\n\n9:30-10:30\nModelling with seasonality and the frequency domain (slides)\n\n\n10:30-10:45\nCoffee break\n\n\n10:45-11:45\nStochastic volatility models and heteroskedasticity (slides)\n\n\n11:45-12:00\nBreak\n\n\n12:00-13:00\nFitting Bayesian time series models (slides)\n\n\n13:00-14:00\nLunch\n\n\n14:00-14:45\nTutor-guided practical: fitting time series models in JAGS and Stan (code)\n\n\n14:45-15:00\nCoffee break\n\n\n15:00-17:00\nSelf-guided practical: start analysing your own data set with Bayes (worksheet)\n\n\n\n 
URL:https://prstats.preprodw.com/course/online-course-time-series-data-analysis-tsda02/
LOCATION:Delivered remotely (Portugal)\, Portugal
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/TSDA01.png
GEO:39.399872;-8.224454
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20221018
DTEND;VALUE=DATE:20221021
DTSTAMP:20260419T064422
CREATED:20210724T164637Z
LAST-MODIFIED:20221018T101649Z
UID:10000345-1666051200-1666310399@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Bayesian Data Analysis (BADA02) This course will be delivered live
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nTuesday\, 18th October\, 2022\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – GMT – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Details\n				Bayesian methods are now increasingly widely in data analysis across most scientific research fields. Given that Bayesian methods differ conceptually and theoretically from their classical statistical counterparts that are traditionally taught in statistics courses\, many researchers do not have opportunities to learn the fundamentals of Bayesian methods\, which makes using Bayesian data analysis in practice more challenging. The aim of this course is to provide a solid introduction to Bayesian methods\, both theoretically and practically. We will begin by teaching the fundamental concepts of Bayesian inference and Bayesian modelling\, including how Bayesian methods differ from their classical statistics counterparts\, and show how to do Bayesian data analysis in practice in R. We then provide a solid introduction to Bayesian approaches to these topics using R and the brms package. We begin by covering Bayesian approaches to linear regression. We will then proceed to Bayesian approaches to generalized linear models\, including binary logistic regression\, ordinal logistic regression\, Poisson regression\, zero-inflated models\, etc. Finally\, we will cover Bayesian approaches to multilevel and mixed effects models. Throughout this course\, we will be using\, via the brms package\, Stan based Markov Chain Monte Carlo (MCMC) methods. \n			\n				\n				\n				\n				\n				Intended Audiences\n				This course is aimed at anyone who is interested to learn and apply Bayesian data analysis in any area of science\, including the social sciences\, life sciences\, physical sciences. No prior experience or familiarity with Bayesian statistics is required. \n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely \n			\n				\n				\n				\n				\n				Course Information\n				Availability – 30 places \nDuration – 3 days \nContact hours – Approx. 20 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English \n			\n				\n				\n				\n				\n				Teaching Format\n				This course will be largely practical\, hands-on\, and workshop based. For each topic\, there will first be some lecture style presentation\, i.e.\, using slides or blackboard\, to introduce and explain key concepts and theories. Then\, we will cover how to perform the various statistical analyses using R. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. For the breaks between sessions\, and between days\, optional exercises will be provided. Solutions to these exercises and brief discussions of them will take place after each break. \nThe course will take place online using Zoom. On each day\, the live video broadcasts will occur during UK local time at:• 10am-12pm• 1pm-3pm• 4pm-6pm \nAll sessions will be video recorded and made available to all attendees as soon as possible\, hopefully soon after each 2hr session. \nIf some sessions are not at a convenient time due to different time zones\, attendees are encouraged to join as many of the live broadcasts as possible. For example\, attendees from North America may be able to join the live sessions from 3pm-5pm and 6pm-8pm\, and then catch up with the 12pm-2pm recorded session once it is uploaded. By joining any live sessions that are possible will allow attendees to benefit from asking questions and having discussions\, rather than just watching prerecorded sessions. \nAt the start of the first day\, we will ensure that everyone is comfortable with how Zoom works\, and we’ll discuss the procedure for asking questions and raising comments. \nAlthough not strictly required\, using a large monitor or preferably even a second monitor will make the learning experience better\, as you will be able to see my RStudio and your own RStudio simultaneously. \nAll the sessions will be video recorded\, and made available immediately on a private video hosting website. Any materials\, such as slides\, data sets\, etc.\, will be shared via GitHub \n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				A basic understanding of statistical concepts. Specifically\, generalised linear regression models\, statistical significance\, hypothesis testing. \n			\n				\n				\n				\n				\n				Assumed computer background\n				Familiarity with R. Ability to import/export data\, manipulate data frames\, fit basic statistical models & generate simple exploratory and diagnostic plots. \n			\n				\n				\n				\n				\n				Equipment and software requirements\n				\nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n \n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \n			\n				\n				\n				\n				\n				\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE PROGRAMME\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Tuesday 18th\n				Classes from 10:00 to 18:00 \n• Topic 1: We will begin with a overview of what Bayesian data analysis is in essence and how it fits into statistics as it practiced generally. Our main point here will be that Bayesian data analysis is effectively an alternative school of statistics to the traditional approach\, which is referred to variously as the classical\, or sampling theory based\, or frequentist based approach\, rather than being a specialized or advanced statistics topic. However\, there is no real necessity to see these two general approaches as being mutually exclusive and in direct competition\, and a pragmatic blend of both approaches is entirely possible. \n• Topic 2: Introducing Bayes’ rule. Bayes’ rule can be described as a means to calculate the probability of causes from some known effects. As such\, it can be used as a means for performing statistical inference. In this section of the course\, we will work through some simple and intuitive calculations using Bayes’ rule. Ultimately\, all of Bayesian data analysis is based on an application of these methods to more complex statistical models\, and so understanding these simple cases of the application of Bayes’ rule can help provide a foundation for the more complex cases. \n• Topic 3: Bayesian inference in a simple statistical model. In this section\, we will work through a classic statistical inference problem\, namely inferring the number of red marbles in an urn of red and black marbles\, or equivalent problems. This problem is easy to analyse completely with just the use of R\, but yet allows us to delve into all the key concepts of all Bayesian statistics including the likelihood function\, prior distributions\, posterior distributions\, maximum a posteriori estimation\, high posterior density intervals\, posterior predictive intervals\, marginal likelihoods\, Bayes factors\, model evaluation of out-of-sample generalization. \n			\n				\n				\n				\n				\n				Wednesday 19th\n				Classes from 10:00 to 18:00 \n• Topic 4: Bayesian analysis of normal models. Statistical models based on linear and normal distribution are a mainstay of statistical analyses in general. They encompass models such as linear regression\, Pearson’s correlation\, t-tests\, ANOVA\, ANCOVA\, and so on. In this section\, we will describe how to do Bayesian analysis of normal linear models\, focusing on simple examples. One of the aims of this section is to identify some important and interesting parallels between Bayesian and classical or frequentist analyses. This shows how Bayesian and classical analyses can be seen as ultimately providing two different perspectives on the same problem. \n• Topic 5: The previous section provides a so-called analytical approach to linear and normal models. This is where we can calculate desired quantities and distributions by way of simple formulae. However\, analytical approaches to Bayesian analyses are only possible in a relatively restricted set of cases. On the other hand\, numerical methods\, specifically Markov Chain Monte Carlo (MCMC) methods can be applied to virtually any Bayesian model. In this section\, we will re-perform the analysis presented in the previous section but using MCMC methods. For this\, we will use the brms package in R that provides an exceptionally easy to use interface to Stan. \nTopic 6: Bayesian linear models. We begin by covering Bayesian linear regression. For this\, we will use the brm command from the brms package\, and we will compare and contrast the results with the standard lm command. By comparing and contrasting brm with lm we will see all the major similarities and differences between the Bayesian and classical approach to linear regression. We will\, for example\, see how Bayesian inference and model comparison works in practice and how it differs conceptually and practically from inference and model comparison in classical regression. As part of this coverage of linear models\, we will also use categorical predictor variables and explore varying intercept and varying slope linear models. \n  \n			\n				\n				\n				\n				\n				Thursday 20th\n				Classes from 10:00 to 18:00 \n• Topic 7: Extending Bayesian linear models. Classical normal linear models are based on strong assumptions that do not always hold in practice. For example\, they assume a normal distribution of the residuals\, and assume homogeneity of variance of this distribution across all values of the predictors. In Bayesian models\, these assumptions are easily relaxed. For example\, we will see how we can easily replace the normal distribution of the residuals with a t-distribution\, which will allow for a regression model that is robust to outliers. Likewise\, we can model the variance of the residuals as being dependent on values of predictor variables. \n• Topic 8: Bayesian generalized linear models. Generalized linear models include models such as logistic regression\, including multinomial and ordinal logistic regression\, Poisson regression\, negative binomial regression\, zero-inflated models\, and other models. Again\, for these analyses we will use the brms package and explore this wide range of models using real world data-sets. In our coverage of this topic\, we will see how powerful Bayesian methods are\, allowing us to easily extend our models in different ways in order to handle a variety of problems and to use assumptions that are most appropriate for the data being modelled. \n• Topic 9: Multilevel and mixed models. In this section\, we will cover the multilevel and mixed effects variants of the regression models\, i.e. linear\, logistic\, Poisson etc\, that we have covered so far. In general\, multilevel and mixed effects models arise whenever data are correlated due to membership of a group (or group of groups\, and so on). For this\, we use a wide range of real-world data-sets and problems\, and move between linear\, logistic\, etc.\, models are we explore these analyses. We will pay particular attention to considering when and how to use varying slope and varying intercept models\, and how to choose between maximal and minimal models. We will also see how Bayesian approaches to multilevel and mixed effects models can overcome some of the technical problems (e.g. lack of model convergence) that beset classical approaches. \n  \n  \n			\n			\n				\n				\n				\n				\n				\n				\n					Dr. Mark Andrews\n					Works at: Senior Lecturer\, Psychology Department\, Nottingham Trent University\, England \n					Teaches:\n\nFree Introduction To Statistics Using R And Rstudio (IRRS)\nIntroduction To Generalised Linear Models Using R And Rstudio (IGLM)\nIntroduction To Mixed Models Using R And Rstudio (Immr)\nNonlinear Regression Using Generalized Additive Models Using R (GAMR)\nIntroduction To Machine Learning And Deep Learning Using R (IMDL)\nModel Selection And Model Simplification (MSMS)\nData Visualization Using Gg Plot 2 (R And Rstudio) (DVGG)\nData Wrangling Using R And Rstudio (DWRS)\nReproducible Data Science Using Rmarkdown\, Git\, R Packages\, Docker\, Make & Drake\, And Other Tools (RDRP)\nIntroduction/Fundamentals Of Bayesian Data Analysis Statistics Using R (FBDA)\nBayesian Data Analysis (BADA)\nBayesian Approaches To Regression And Mixed Effects Models Using R And Brms (BARM)\nIntroduction To Stan For Bayesian Data Analysis (ISBD)\nIntroduction To Python (PYIN)\nIntroduction To Scientific\, Numerical\, And Data Analysis Programming In Python (PYSC)\nMachine Learning And Deep Learning Using Python (PYML)\nPython For Data Science\, Machine Learning\, And Scientific Computing (PDMS)\n\nMark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham\, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition\, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis\, particularly as applied to data from the social and behavioural sciences. Since 2015\, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences. \nResearchGate
URL:https://prstats.preprodw.com/course/bayesian-data-analysis-bada02/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/BADA01R.png
GEO:53.1423672;-7.6920536
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20221003
DTEND;VALUE=DATE:20221006
DTSTAMP:20260419T064422
CREATED:20220221T202517Z
LAST-MODIFIED:20220926T162226Z
UID:10000318-1664755200-1665014399@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Bioacoustics For Ecologists: Hardware\, Survey design And Data analysis (BIAC03) This course will be delivered live
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nTuesday\, September 20th\, 2022\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \nTime Zone\nTIME ZONE – GMT – Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				About this course\n				This course will introduce and explain the different applications for bioacoustics to answer ecological questions. Starting with a detailed overview of the correct and most efficient methods of data collecting in the field\, this course will then go on to show delegates cutting edge methods for analysing and interpreting different types of bioacoustic data. \nBy the end of this 5-day practical course\, attendees will have the capacity to set up and deploy recording devices\, download acoustic data\, how to analyse this data and report the results. \nBioacoustic methods are becoming increasingly recognised as a valuable approach for ecological surveying. Bioacoustics can be used to effectively replace some current techniques whilst increasing the quality of the data collected or can be used in unison to compliment them. They are particularly useful for developing long-term\, permanent datasets that can be independently reviewed\, particularly for rare species with low detectability\, or when working in difficult environments. \nThe course will provide a practical introduction to bioacoustics methods\, with a mix of lectures and practical workshops\, and some optional fieldwork. It will start with a basic introduction to sound and recording theory\, before developing hands-on skills in setting-up and deploying a range of acoustic and ultrasonic audio recorders. Workshops will then cover the download and analysis of audio data\, mainly using Kaleidoscope Pro and Audacity software. The processed audio data will then be analysed and presented using R\, the free software environment for statistical computing and graphics (http://www.r-project.org/). \nExample data sets will mostly cover applications for bat and bird surveys\, as well as the use of Acoustic Indices as biodiversity metrics. If you are working in different areas of ecology using bioacoustics please feel free to contact oliverhooker@prstatistics.com so we can advise if the learning outcomes are transferable to your field of research. \n			\n				\n				\n				\n				\n				Intended Audiences\n				This course is suitable for anyone working with bioacoustics from those in academia\, conservation biologists and persons in industry and government. \n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely \n			\n				\n				\n				\n				\n				Course Details\n				Time Zone – GMT \nAvailability – 15 places \nDuration – 3 days \nContact hours – Approx. 21 hours \nECT’s – Equal to 1.5 ECT’s \nLanguage – English \n			\n				\n				\n				\n				\n				Teaching Format\n				There will be morning lectures based on the modules outlined in the course timetable. In the afternoon there will be practicals based on the topics covered that morning. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \n \n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				A basic understanding of statistical concepts. Specifically\, generalised linear regression models\, statistical significance\, hypothesis testing. \n			\n				\n				\n				\n				\n				Assumed computer background\n				Familiarity with R. Ability to import/export data\, manipulate data frames\, fit basic statistical models & generate simple exploratory and diagnostic plots. \n			\n				\n				\n				\n				\n				Equipment and software requirements\n				\nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n \n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \n			\n				\n				\n				\n				\n				 \n\n\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n\n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Programme\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Tuesday 20th\n				Classes from 09:30 – 17:30 \nSESSION 1 – INTRO TO ACOUSTIC DATA (AND METADATA) \n1. Acoustic Data and Metadata – what does it look like?Data sources – survey methods/approaches\, recorder hardware\, file types etcMetadata recording and systemsCase study examples – terrestrial & freshwater (& marine) \n2. Introduction to spectrogramsVisualizing sound – understanding spectrograms\, identifying speciesBats – peakfreq\, IPI\, max\, min\, duration\, shape etc..Birds – Nathan Pieplow keys – time/frequency characteristics\, song/call shapesMeasuring parameters manually and programatically \n3. Introduction to audio software – for species ID and vocalizationsAnalysis tools for acoustic dataSoftware tools – Kaleidoscope\, Audacity\, R (others: Raven/Lite\, Batscan\, Batsound\, Batscope\, iBatsID\, Analook\, SonoChiro\, Sonobat\, Luscinia\, BirdNet\, MATLAB\, PAMGUARD\, etc)Viewing/listening/measuring\, recognizers\, clusteringManual and automated call detection and ID methodsLimitations and emerging opportunities in acoustic data analysis \n4. Workshop – sound editing\, measuring and management using Audacity \nSESSION 2 – ANALYSING BAT DATA USING KALEIDOSCOPE \n5. Workshop – Kaleidoscope bat ID processing (Paul H-L) \n			\n				\n				\n				\n				\n				Wednesday 21st\n				Classes from 09:30 – 17:30 \nSESSION 3 – ANALYSING ACOUSTIC DATA USING R) \n6. Workshop – R (Seewave/Soundecology) (creat/view/analyse spectrograms) \nSESSION 4 – INTERPRETING ACOUSTIC DATA \n7. Data collation\, analysis and interpretationMoving from sound to data to meaning (creating tidy data/metadata and using this)Data and recognizer quality – false positives/negatives and validating auto-IDs…Presence/absenceActivity levelsDistributionTemporal changesPopulation assessments/occupancyLocalizing calls with amplitude levels or microphone arraysIdentifying individualsMention of Soundscapes and Acoustic indices – more on this later \n8. Soundscapes and Acoustic indicesWhat different indicesPros and cons of eachUsing and comparing scores \n9. Example workflows from previous studiesCarlos capercaillie and TBH workBCT/CIEEM guidance on call assessmentOther published research and recommendations \n			\n				\n				\n				\n				\n				Thursday 22nd\n				Classes from 09:30 – 17:30 \nSESSION 5 –ACOUSTIC INDICES USING R/KALEIDOSCOPE \n10. Workshop – Kaleidoscope (analyse Acoustic Indices) \n11. Workshop – R (Seewave/Soundecology) (analyse Acoustic Indices) \nSESSION 6 –SPATIAL ACOUSTIC DATA AND COURSE ROUND-UP \n12. Workshop – presenting spatial data using Google Earth and REMtouch kml output – Google EarthCSV outputSpatial analysis with R \n13. Review and roundup/conclusions \n			\n			\n				\n				\n				\n				\n				Course Instructor\n \n*\nDr. Carlos Abrahams\nWorks at – Technical Director at Baker Consultants Ltd and Senior Lecturer at Nottingham Trent UniversityTeaches – Bioacoustics for ecologists: Hardware\, Survey design and Data analysis (BIAC) \nCarlos has been working in the practical fields of ecology and nature conservation for over 25 years. Starting his career in nature reserve and countryside management\, he has been an ecological consultant since 2001. Alongside managing a busy consultancy\, undertaking Environmental Impact Assessments for a range of clients\, he is also a part-time lecturer at Nottingham Trent University on the BSc Environmental Biology. Carlos has previously published research on wetland vegetation/management and amphibian habitat selection. However\, after many years of using static and handheld detectors for bat surveys\, he is currently engaged in studying the potential of bioacoustic methods for investigating bird populations\, especially for rare and declining species such as Capercaillie and Nightjar.
URL:https://prstats.preprodw.com/course/bioacoustics-for-ecologists-hardware-survey-design-and-data-analysis-biac03/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/BIAC02R.png
GEO:55.378051;-3.435973
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20220711
DTEND;VALUE=DATE:20220715
DTSTAMP:20260419T064422
CREATED:20200410T193910Z
LAST-MODIFIED:20221019T102457Z
UID:10000306-1657497600-1657843199@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Introduction to spatial analysis of ecological data using R (ISPE05) This course will delivered live
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nMonday\, July 11th\, 2022\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCourse Program\nTIME ZONE – UTC+2 – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				About this course\n				The aim of the course is to introduce you to a spatial data processing\, analysis\, and visualization capabilities of the R programming language. It will teach a range of techniques using a mixture of lectures\, computer exercises and case studies. \nBy the end of the course participants should: \n\nUnderstand the basic concepts of spatial data analysis\nKnow R’s spatial capabilities\nUnderstand how to import a range of spatial data sources into R\nBe confident with using R’s command-line interface (CLI) for spatial data processing\nBe able to perform a range of attribute operations (e.g. subsetting and joining)\, spatial operations (e.g. distance relations\, topological relations)\, and geometry operations (e.g. clipping\, aggregations)\nUnderstand coordinate reference systems (CRSs)\, be able to decide which CRS to use\, and how to reproject spatial data\nKnow how to visualize the results of a spatial analysis in the form of static and interactive maps\nHave the confidence to apply spatial analysis skills to their own projects\n\n			\n				\n				\n				\n				\n				Intended Audiences\n				Academics and post-graduate students working on projects related to spatial data and want access to a powerful (geo)statistical and visualization programming language. \nApplied researchers and analysts in public\, private or third-sector organizations who need the reproducibility\, speed and flexibility of a command-line language such as R. \nThe course is designed for intermediate-to-advanced R users interested in spatial data analysis and R beginners who have prior experience with geographic data. \n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely \n			\n				\n				\n				\n				\n				Course Details\n				Venue – Delivered remotely \nTime zone – Poland local time (UTC+2) \nAvailability – 20 places \nDuration – 4 days \nContact hours – Approx. 27 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English \n			\n				\n				\n				\n				\n				Teaching Format\n				\n\nThe course will be a mixture of theoretical and practical. Each concept will be first described and explained\, and next there will be a time to exercise the topics using provided data sets. Participants are also very welcome to bring their own data. \n\n\n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				The course is designed for intermediate-to-advanced R users interested in spatial data analysis and R beginners who have prior experience with geographic data. \n			\n				\n				\n				\n				\n				Assumed computer background\n				Attendees should already have experience with R and be able to read csv files\, create simple plots\, and manipulate data frames. \nHowever\, if you do not have R experience but already use GIS software and have a strong understanding of geographic data types\, and some programming experience\, the course may also be appropriate for you. \n			\n				\n				\n				\n				\n				Equipment and software requirements\n				\nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n \n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \n			\n				\n				\n				\n				\n				If you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE PROGRAMME\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Monday 11th\n				Classes from 09:00 – 17:00Introduction to the courseKey concepts related to spatial dataR’s spatial ecosystemReading data from spatial file formatsUnderstanding R’s spatial classesCreating static and interactive maps:Customizing mapsMaking facet maps \n			\n				\n				\n				\n				\n				Tuesday 12th\n				Classes from 09:00 – 17:00Attribute data operations:Vector attribute subsetting\, aggregation and joiningCreating new vector attributesRaster subsettingSummarizing raster objectsSpatial data operations:Spatial subsetting \n			\n				\n				\n				\n				\n				Wednesday 13th\n				Classes from 09:00 – 17:00Attribute data operations:Vector attribute subsetting\, aggregation and joiningCreating new vector attributesRaster subsettingSummarizing raster objectsSpatial data operations:Spatial subsetting \n			\n				\n				\n				\n				\n				Thursday 14th\n				Classes from 09:00 – 17:00Attribute data operations:Vector attribute subsetting\, aggregation and joiningCreating new vector attributesRaster subsettingSummarizing raster objectsSpatial data operations:Spatial subsetting \n			\n			\n				\n				\n				\n				\n				\n				\n					Jakub Nowosad\n					Works at: Adam Mickiewicz University \n					Jakub Nowosad is a computational geographer working at the intersection between geocomputation and the environmental sciences. His research is focused on developing and applying spatial methods to broaden understanding of processes and patterns in the environment. A vital part of his work is to create\, collaborate\, and improve geocomputational software. He is an active member of the #rspatial community and a co-author of the Geocomputation with R book. \nResearchGate\nGoogleScholar\nORCID\nLinkedIn\nGitHub\n					\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Teaches\n				\nIntroduction to spatial analysis of ecological data using R (ISPE)\nMaking beautiful and effective maps in R (MAPR\nAdapting to the recent changes in R spatial packages (sf\, terra\, PROJ library) (PROJ\n\n			\n				\n				\n				\n				\n				Teaches\n				\nIntroduction to spatial analysis of ecological data using R (ISPE)\nMaking beautiful and effective maps in R (MAPR\nAdapting to the recent changes in R spatial packages (sf\, terra\, PROJ library) (PROJ
URL:https://prstats.preprodw.com/course/introduction-to-spatial-analysis-of-ecological-data-using-r-ispe05/
LOCATION:Delivered remotely (Poland)\, Central European Summer Time\, Poland
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2020/06/ISPE01-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20220620
DTEND;VALUE=DATE:20220623
DTSTAMP:20260419T064422
CREATED:20220218T223151Z
LAST-MODIFIED:20220614T232250Z
UID:10000354-1655683200-1655942399@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Bayesian GLM's For Ecologists (BGFE01) This course will be delivered live
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nMonday\, June 20th 2022\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – GMT+1 – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Details\n				This short course is aimed at introducing researchers to analysing ecological and environmental data with Bayesian GLMs using R. Theory underpinning Bayesian inference will be discussed\, as well as analytical methods and statistical interpretation. Sessions will be a blend of interactive demonstrations and lectures\, where learners will have the opportunity to ask questions throughout. Prior to the course\, attendees will receive R script and datasets and a list of R packages to install. \nBy the end of the course\, participants should be able to: \n\nRecognise the distinction between frequentist and Bayesian approaches to model fitting\nApply data exploration techniques and avoid the common pitfalls in tackling a data analysis\nApply a 9-step protocol to fitting Bayesian GLMs\nUnderstand and apply alternative approaches to model selection\nApply statistical modelling methods to ecological data using Bayesian GLMs\n\n  \n			\n				\n				\n				\n				\n				Intended Audiences\n				Post graduate or post-doctoral level researchers who wish to learn how to manipulate and analyse ecological data using R \nApplied researchers and analysts in the environmental/ecological sector with a role in handling and analysing data \n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely \n			\n				\n				\n				\n				\n				Course Information\n				Availability – 30 places \nDuration – 3 days \nContact hours – Approx. 21 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English \n			\n				\n				\n				\n				\n				Teaching Format\n				This course will comprise a mixture of taught theory and practical examples. Data and analytical approaches will be presented in a lecture format to introduce key concepts. Statistical analyses will then be presented using R. All R script that the instructor uses during these sessions will be shared with participants\, and R script will be presented and explained. \nIdeally\, participants will be able to use a computer screen that is sufficiently large to enable them to view my shared RStudio and their own RStudio simultaneously. \n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				It will be assumed that participants will be familiar with general statistical concepts and fitting GLMs to ecological data. Participants will need experience of performing statistical analysis using R. \n			\n				\n				\n				\n				\n				Assumed computer background\n				Experience with performing statistical analyses using R and R Studio will be assumed. \n			\n				\n				\n				\n				\n				Equipment and software requirements\n				\nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n \n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \n			\n				\n				\n				\n				\n				If you are unsure about course suitability\, please get in touch by email to find out more \ninfo@clovertraining.co.uk \n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE PROGRAMME\nMonday  09:00 – 16:00 \nIntroduction to Bayesian inference \n\nDifference between Bayesian and frequentist approaches\nBayes’ theorem\nA frequentist or Bayesian framework: Which is better?\nFitting Bayesian GLMs\nSteps in fitting a Bayesian GLM\nPriors\nNon-informative priors\nWeakly informative priors\nInformative priors\nThe posterior distribution\nBayesian computational methods\nThe advantages of Bayesian inference\nCriticism of Bayesian inference\n\nData exploration \n\nSix-step data exploration protocol\nOutliers\nNormality and homogeneity of the dependent variable\nLots of zeros in the response variable\nMulticollinearity among covariates\nRelationships among dependent and independent variables\nIndependence of response variable\nResults of data exploration\n\nGaussian GLM with INLA  \n\nEuropean bitterling territoriality\nState the question\nSelection of a statistical model\nSpecification of priors\nModel fitting\nObtain the posterior distribution\nConduct model checks\nInterpret and present model output\nVisualise the results\nPresenting results\nConclusions\n\n  \nTuesday  09:00 – 16:00 \nPoisson GLM with INLA  \n\nStickleback lateral plate number\nState the question\nSelection of a statistical model\nSpecification of priors\nModel fitting\nObtain the posterior distribution\nConduct model checks\nInterpret and present model output\nVisualise the results\nPresenting results\nConclusions\n\nNegative binomial GLM with INLA  \n\nCoral abundance\nState the question\nSelection of a statistical model\nSpecification of priors\nModel fitting\nObtain the posterior distribution\nConduct model checks\nInterpret and present model output\nVisualise the results\nPresenting results\nConclusions\n\nBernoulli GLM with INLA  \n\nCuckoo parasitism of reed warbler nests\nState the question\nSelection of a statistical model\nSpecification of priors\nModel fitting\nObtain the posterior distribution\nConduct model checks\nInterpret and present model output\nVisualise the results\nPresenting results\nConclusions\n\n  \nWednesday 09:00 – 16:00 \nGamma GLM with INLA  \n\nStickleback lateral plate number\nState the question\nSelection of a statistical model\nSpecification of priors\nModel fitting\nObtain the posterior distribution\nConduct model checks\nInterpret and present model output\nVisualise the results\nPresenting results\nConclusions\n\nImplementing and assessing Bayesian GLMs \n\nPrior information\nPresenting results of Bayesian GLMs\nReviewing Bayesian GLMs\nMisuse of Bayesian GLMs\nConclusions\n\nDiscussion & questions \n			\n				\n				\n				\n				\n				Course Instructor\n			\n				\n				\n				\n				\n				\n				\n					Dr. Carl Smith\n					\n					Teaches:\n\nBayesian GLMs for Ecologists (BGFE01)
URL:https://prstats.preprodw.com/course/bayesian-glms-or-ecologists-bgfe01/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/BGFE01.png
GEO:55.378051;-3.435973
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20220530
DTEND;VALUE=DATE:20220604
DTSTAMP:20260419T064422
CREATED:20220218T222314Z
LAST-MODIFIED:20220512T151555Z
UID:10000311-1653868800-1654300799@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Statistics For Biodiversity And Conservation (SFBC01) This course will be delivered live
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nMonday\, May 30th 2022\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCourse Program\nTIME ZONE – GMT+1 – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				About This Course\n				The way statistics are used in biology\, and especially ecology\, is changing\, with a shift from statistical tests of significance to fitting statistical models to data to explain causation and draw inferences to wider situations. And a new enlightened Bayesian world of statistical inference is also emerging. \nAn understanding of statistical modelling is no longer a luxury\, and it is an expectation that postgraduates and post-doctoral researchers\, as well as ecological practitioners possess an understanding of this approach. This change has been unleashed by an explosion in computing power and the advent of powerful and flexible software\, such as R\, that permits users to wrangle\, analyse and visualise their data in novel ways. \nThis course is aimed at introducing researchers to analysing ecological and environmental data with GLMs using R. Study design will be discussed\, as well as data analysis and statistical interpretation. Sessions will be a blend of interactive demonstrations and lectures\, where learners will have the opportunity to ask questions throughout. Prior to the course\, you will receive R script and datasets and a list of R packages to install. \nBy the end of the course\, participants should be able to: \n\nApply data exploration techniques and avoid the common pitfalls in tackling a data analysis\nRecognise common problems associated with analysis of ecological data and how to address them\nUnderstand and apply alternative approaches to model selection\nApply statistical modelling methods to ecological data using GLMs\nRecognise the distinction between frequentist and Bayesian approaches to model fitting\n\n			\n				\n				\n				\n				\n				Intended Audiences\n				Post graduate or post-doctoral level researchers who wish to learn how to manipulate and analyse ecological data using R \nApplied researchers and analysts in the environmental/ecological sector with a role in handling and analysing data \n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely\n			\n				\n				\n				\n				\n				Course Details\n				Availability – 30 places \nDuration – 5 days \nContact hours – Approx. 35 hours \nECT’s – Equal to 3 ECT’s \nLanguage – English \n			\n				\n				\n				\n				\n				Teaching Format\n				 This course will comprise a mixture of taught theory and practical examples. Data and analytical approaches will be presented in a lecture format to introduce key concepts. Statistical analyses will then be presented using R. All R script that the instructor uses during these sessions will be shared with participants\, and R script will be presented and explained.  \nIdeally\, participants will be able to use a computer screen that is sufficiently large to enable them to view my shared RStudio and their own RStudio simultaneously. \n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				 It will be assumed that participants have a basic familiarity with general statistical concepts\, linear models\, and statistical inference. Participants may have limited experience of performing statistical analysis using R. \n			\n				\n				\n				\n				\n				Assumed computer background\n				Some experience with R and R Studio will be needed to run R script and install R packages\, though guidance will be provided on basic concepts. \n			\n				\n				\n				\n				\n				Equipment and software requirements\n				 A computer with the most recent version of R and RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers.  \nA full list of required packages will be made available to participants prior to the course.  \nIdeally\, participants will be able to use a computer screen that is sufficiently large to enable them to view my shared RStudio and their own RStudio simultaneously\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n			\n				\n				\n				\n				\n				\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n  \n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE PROGRAMME\n  \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Monday 30th\n				Classes from 09:00 to 17:00 \nIntroduction to R and RStudio \n\nGetting started with R and RStudio\nBasic points\nNavigating RStudio\nBasic settings in RStudio\nBasic principles in R\nSetting the working directory\nImporting data\nFunctions and packages in R\n\nData exploration \n\nSix-step data exploration protocol\nOutliers\nNormality and homogeneity of the dependent variable\nLots of zeros in the response variable\nMulticollinearity among covariates\nRelationships among dependent and independent variables\nIndependence of response variable\nResults of data exploration\n\nTesting differences between two groups \n\nEuropean hedgehogs\nOutliers\nNormality and homogeneity of the dependent variable\nZeros in the response variable\nMulticollinearity among covariates\nRelationships among dependent and independent variables\nIndependence of response variable\nResults of data exploration\nComparing two groups of normal unpaired data: unpaired t-test\nComparing two groups of normal paired data: the paired t-test\nComparing two groups of non-normal unpaired data: the Mann-Whitney test\nComparing two groups of non-normal paired data: the Wilcoxon test\nPresenting results\n\nTesting association between two continuous variables: correlation \n\nBarn owls\nOutliers\nNormality of the variables\nAn excess of zeros\nMulticollinearity among covariates\nRelationships between variables\nIndependence of variables\nResults of data exploration\nTesting association between two continuous normal variables: Pearson’s correlation\nTesting association between two continuous non-normal variables: Spearmann’s rank correlation\nTesting association between two continuous non-normal variables with small sample size and ties: Kendall’s Tau correlation\nPresenting the results\n\n			\n				\n				\n				\n				\n				Tuesday 31st\n				Classes from 09:00 to 17:00 \nModelling two continuous variables with linear regression \n\nNorthern pike length-fecundity relationship\nOutliers\nNormality and homogeneity of the variables\nAn excess of zeros\nMulticollinearity among covariates\nRelationship between variables\nIndependence of variables\nResults of data exploration\nBivariate linear regression\nModel validation\nHomogeneity of variance of the residuals\nNormality of residuals\nPlot of the linear regression model\nAbsence of influential observations\nConclusions from model validation\nData transformation\nRefit linear regression with transformed data\nModel re-validation\nHomogeneity of variance of the residuals\nNormality of residuals\nPlot of the linear regression model\nAbsence of influential observations\nModel presentation and interpretation\n\nGaussian General Linear Model (GLM) \n\nDiet of weatherfish in different seasons\nData exploration\nOutliers\nNormality and homogeneity of the variables\nLots of zeros in the response variable\nMulticollinearity among covariates\nRelationships among dependent and independent variables\nIndependence of response variable\nModel fitting\nModel validation\nHomogeneity of residual variance\nModel misfit\nNormality of residuals\nAbsence of influential observations\nModel presentation\n\n  \n			\n				\n				\n				\n				\n				Wednesday 1st\n				Classes from 09:00 to 17:00 \nModelling two continuous variables with linear regression \n\nNorthern pike length-fecundity relationship\nOutliers\nNormality and homogeneity of the variables\nAn excess of zeros\nMulticollinearity among covariates\nRelationship between variables\nIndependence of variables\nResults of data exploration\nBivariate linear regression\nModel validation\nHomogeneity of variance of the residuals\nNormality of residuals\nPlot of the linear regression model\nAbsence of influential observations\nConclusions from model validation\nData transformation\nRefit linear regression with transformed data\nModel re-validation\nHomogeneity of variance of the residuals\nNormality of residuals\nPlot of the linear regression model\nAbsence of influential observations\nModel presentation and interpretation\n\nGaussian General Linear Model (GLM) \n\nDiet of weatherfish in different seasons\nData exploration\nOutliers\nNormality and homogeneity of the variables\nLots of zeros in the response variable\nMulticollinearity among covariates\nRelationships among dependent and independent variables\nIndependence of response variable\nModel fitting\nModel validation\nHomogeneity of residual variance\nModel misfit\nNormality of residuals\nAbsence of influential observations\nModel presentation\n\n  \n			\n				\n				\n				\n				\n				Thursday 2nd\n				Classes from 09:00 to 17:00 \nPoisson Generalised Linear Model (GLM) \n\nAbundance of freshwater mussels\nData exploration\nOutliers\nLots of zeros in the response variable\nMulticollinearity among covariates\nRelationships among dependent and independent variables\nIndependence of response variable\nModel fitting\nModel validation\nOverdispersion\nModel misfit\nSimulating from the model\nModel presentation\n\nNegative binomial Generalised Linear Model (GLM) \n\nSpecies diversity of chironomids\nData exploration\nOutliers\nLots of zeros in the response variable\nMulticollinearity among covariates\nRelationships among dependent and independent variables\nModel fitting\nModel validation\nOverdispersion\nModel presentation\n\n  \n			\n				\n				\n				\n				\n				Friday 3rd\n				Classes from 09:00 to 17:00 \nGaussian Generalised Linear Mixed Model (GLMM) \n\nBody condition of European tree frogs\nData exploration\nOutliers\nNormality and homogeneity of the dependent variable\nLots of zeros in the response variable\nMulticollinearity among covariates\nRelationships among dependent and independent variables\nIndependence of response variable\nResults of data exploration\nModel fitting\nModel validation\nHomogeneity of residual variance\nModel misfit\nNormality of residuals\nAbsence of influential observations\nRefit model\nModel validation\nHomogeneity of residual variance\nModel misfit\nNormality of residuals\nAbsence of influential observations\nRefit model with random term\nModel validation\nHomogeneity of residual variance\nModel misfit\nNormality of residuals\nModel presentation\n\nBayesian inference \n\nIntroduction to Bayesian inference\nEuropean bitterling territoriality\nData exploration\nOutliers\nNormality and homogeneity of the dependent variable\nLots of zeros in the response variable\nIndependence of response variable\nModel fitting\nINLA\nPosterior (marginal) distributions\nComparison with frequentist Gaussian GLM\nModel validation\nHomogeneity of residual variance\nModel misfit\nNormality of residuals\nModel presentation\n\n  \n			\n			\n				\n				\n				\n				\n				Course Instructor\n			\n				\n				\n				\n				\n				\n				\n					Dr. Carl Smith\n					Senior Lecturer\, Psychology Department\, Nottingham Trent University \n					Teaches:\n\nStatistics for biodiversity and conservation (SFBC01)\nBayesian GLMs for Ecologists (BGFE01)\n\nMark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham\, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition\, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis\, particularly as applied to data from the social and behavioural sciences. Since 2015\, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences. \n 
URL:https://prstats.preprodw.com/course/statistics-for-biodiversity-and-conservation-sfbc01/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/SFBC01.png
GEO:55.378051;-3.435973
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20220518
DTEND;VALUE=DATE:20220520
DTSTAMP:20260419T064422
CREATED:20220303T115627Z
LAST-MODIFIED:20220316T135650Z
UID:10000366-1652832000-1653004799@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Machine Learning and Deep Learning Using Python (PYML03) This course will be delivered live
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nWednesday\, May 18th\, 2022\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE FORMAT\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTIME ZONE\nTIME ZONE – GMT – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				About This Course\n				Python is one of the most widely used and highly valued programming languages in the world\, and is especially widely used in machine learning and for deep learning. In this two day course\, we provide an introduction to machine learning and deep learning using Python. We begin by providing an overview of the machine learning and deep learning landscape\, and discuss the prominent role that Python has come to play in this area. We then turn to machine learning in practice\, and for this\, we will primarily using the widely used and acclaimed scikit-learn toolbox. We begin with binary and multiclass classification problems\, then look at decision trees and random forests\, then look at unsupervised learning methods\, all of which are major topics in machine learning. We then cover artificial neural networks and deep learning. For this\, we will using the PyTorch deep learning toolbox. Here\, we will cover the relatively easy to understand multilayer perceptron and then turn to convolutional neural networks. \n			\n				\n				\n				\n				\n				Intended Audiences\n				This course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research\, and also widely throughout the public\, and private sector.\n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely \n			\n				\n				\n				\n				\n				Course Details\n				Availability – TBC \nDuration – 2 days \nContact hours – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n			\n				\n				\n				\n				\n				Teaching Format\n				This course will be hands-on and workshop based. Throughout each day\, there will be some brief introductory remarks for each new topic\, introducing and explaining key concepts.The course will take place online using Zoom. On each day\, the live video broadcasts will occur between (UK local time) at:• 10am-12pm• 1pm-3pm• 4pm-6pm \nAll sessions will be video recorded and made available to all attendees as soon as possible\, hopefully soon after each 2hr session. Attendees in different time zones will be able to join in to some of these live broadcasts\, even if all of them are not convenient times. By joining any live sessions that are possible\, this will allow attendees to benefit from asking questions and having discussions\, rather than just watching prerecorded sessions. Although not strictly required\, using a large monitor or preferably even a second monitor will make the learning experience better. All the sessions will be video recorded\, and made available immediately on a private video hosting website. Any materials\, such as slides\, data sets\, etc.\, will be shared via GitHub. \n			\n				\n				\n				\n				\n				Assumed quantative knowledge\n				We will assume familiarity with some general statistical and mathematical concepts such as matrix algebra\, calculus\,probability distributions. However\, expertise with these concepts are not necessary. Anyone who has taken anyundergraduate (Bachelor’s) level course in mathematics\, or even advanced high school level\, can be assumed to havesufficient familiarity with these concepts. \n			\n				\n				\n				\n				\n				Assumed computer background\n				We assume familiarity with using Python\, general purpose programming in Python\, and numerical programming in Python. Note that both of these topics covered comprehensively in two preceding two-day courses\, which together will provide all the computing prerequisites for this course. \n			\n				\n				\n				\n				\n				Equipment and software requirements\n				Attendees of the course must use a computer with Python (version 3) installed. This can in fact be done entirely online for free using Google’s Colaboratory without needing to install any software on your own laptop or desktop. If you are new to Python\, this approach is highly recommended. You will be able to immediately starting learning Python without any installation or configuration of software. This entire course can be done using this approach. \nIf you prefer to install and use Python on your machine\, instructions on how to install and configure all the software needed for this course are provided here. We will also provide time during the workshops to ensure that all software is installed and configured properly. \n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\nPLEASE READ – CANCELLATION POLICY \n\n\nCancellations/refunds are accepted as long as the course materials have not been accessed\,. \n\n\nThere is a 20% cancellation fee to cover administration and possible bank fess. \n\n\nIf you need to discuss cancelling please contact oliverhooker@prstatistics.com. \n\n			\n				\n				\n				\n				\n				\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n  \n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE PROGRAMME\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Wednesday 18th\n				Classes from 10:00 to 18:00 \n• Topic 1: Machine learning and Deep Learning Landscape. Concepts like machine learning\, deep learning\, big data\, data science have become widely used and celebrated in the last 10 years. However\, their definitions are relatively nebulous\, and how they related to one another and to major fields like artificial intelligence and general statistics are not simple matters. In this introduction\, we briefly overview the field of machine learning and deep learning\, discussing its major characteristics and sub-topics\, and also discuss the prominence of Python in the area. \n• Topic 2: Classification problems. Classification problems is one of the bread and butter topics in machine learning\, and is usually the first topic covered in introductions to machine learning. Here\, we will cover image classification (itself a “hello world” type problem in machine learning)\, and particularly focus on logistic regression and support vector machines (SVMs). \n• Topic 3: Decision trees and random forests. Decision trees are a widely used machine learning method\, particularly for classification. Random forests are an ensemble learning extension of decision trees whereby large number of decision tree classifiers are aggregated\, which often leads to much improved performance. \n  \n			\n				\n				\n				\n				\n				Thursday 19th\n				Classes from 10:00 to 18:00 \n• Topic 4: Unsupervised machine learning. Unsupervised learning is essentially a method of finding patterns in unclassified data. Here\, we will look at two of the most widely used unsupervised techniques: k-means clustering and probabilistic mixture models. \n• Topic 5: Introducing artificial neural networks and deep learning with PyTorch. Python provides many popular libraries for artificial neural networks and deep learning. These include Keras and TensorFlow. Here\, we will use PyTorch\, which is a relatively new but increasingly high-level neural network model building and training language. These models often use graphical processing units (GPUs) for computing. Given that most personal computers don’t have adequate GPUs\, we will use Google’s Colaboratory https://colab.research.google.com/\,which is a free Python Jupyter notebook service from Google. \n• Topic 6: Multilayer perceptons. Multilayer perceptrons are very powerful\, yet relatively easy to understand\, artificial neural networks. They are also the simplest type of deep learning model. Here\, we will build and train a multilayer perceptron for a classification problem. \n• Topic 7: Convolutional neural networks. Convolutional neural networks (CNNs) have proved high successful at image classification\, primarily due to their translation invariance\, which is highly suitable for computational vision. Here\, we use PyTorch to build and train a CNN for image classification. \n			\n			\n				\n				\n				\n				\n				Course Instructor\n \nDr. Mark Andrews \nResearchGate \nGoogle Scholar \nMark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham\, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition\, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis\, particularly as applied to data from the social and behavioural sciences. Since 2015\, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences.
URL:https://prstats.preprodw.com/course/machine-learning-and-deep-learning-using-python-pyml03/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/PYML03R.png
GEO:53.1423672;-7.6920536
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20220504
DTEND;VALUE=DATE:20220506
DTSTAMP:20260419T064422
CREATED:20220218T162532Z
LAST-MODIFIED:20220316T135034Z
UID:10000348-1651622400-1651795199@prstats.preprodw.com
SUMMARY:ONLINE COURSE – Introduction To Scientific\, Numerical\, And Data Analysis Programming In Python (PYSC03) This course will be delivered live
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nWednesday\, May 4th\, 2022\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – GMT – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				About This Course\n				Python is one of the most widely used and highly valued programming languages in the world\, and is especially widely used in data science\, machine learning\, and in other scientific computing applications. In order to use Python confidently and competently for these applications\, it is necessary to have a solid foundation in the fundamentals of scientific\, numerical\, and data analysis programming Python. This two day course provides a general introduction to numerical programming in Python\, particularly using numpy\, data processing in Python using Pandas\, data analysis in Python using statsmodels and rpy2. We will also cover the major data visualization and graphics tools in Python\, particularly matplotlib\, seaborn\, and ggplot. Finally\, we will cover some other major scientific Python tools\, such as for symbolic mathematics and parallel programming and code acceleration. Note that in this course\, we will not be teaching Python fundamentals and general purpose programming\, but this knowledge will be assumed\, and is also provided in a preceding two-day course. \n			\n				\n				\n				\n				\n				Intended Audiences\n				This course is aimed at anyone who is interested in learning the fundamentals of Python generally and especially for ultimately using Python for data science and scientific applications. Although these applications are not covered directly here\, but are covered in a subsequent course\, the fundamentals taught here are vital for master data science and scientific applications of Python.\n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely\n			\n				\n				\n				\n				\n				Course Details\n				Availability – TBC \nDuration – 2 days \nContact hours – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n			\n				\n				\n				\n				\n				Teaching Format\n				This course will be hands-on and workshop based. Throughout each day\, there will be some brief introductory remarks for each new topic\, introducing and explaining key concepts. \nThe course will take place online using Zoom. On each day\, the live video broadcasts will occur between (UK local time) at:• 10am-12pm• 1pm-3pm• 4pm-6pm \nAll sessions will be video recorded and made available to all attendees as soon as possible\, hopefully soon after each 2hr session. Attendees in different time zones will be able to join in to some of these live broadcasts\, even if all of them are not convenient times. By joining any live sessions that are possible\, this will allow attendees to benefit from asking questions and having discussions\, rather than just watching prerecorded sessions. Although not strictly required\, using a large monitor or preferably even a second monitor will make the learning experience better. All the sessions will be video recorded\, and made available immediately on a private video hosting website. Any materials\, such as slides\, data sets\, etc.\, will be shared via GitHub. \n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				We will assume familiarity with some general statistical and mathematical concepts such as matrix algebra\, calculus\,probability distributions. However\, expertise with these concepts are not necessary. Anyone who has taken anyundergraduate (Bachelor’s) level course in mathematics\, or even advanced high school level\, can be assumed to havesufficient familiarity with these concepts. \n			\n				\n				\n				\n				\n				Assumed computer background\n				We assume familiarity with using Python and knowledge of general purpose programming in Python. This topics are covered comprehensively in a preceding two-day course\, which will provide all the prerequisites for this course. \n			\n				\n				\n				\n				\n				Equipment and software requirements\n				\nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience. \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n			\n				\n				\n				\n				\n				\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n  \n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE PROGRAMME\nWednesday 4th – Classes from 10:00 to 18:00 \nTopic 1: Numerical programming with numpy. Although not part of Python’s official standard library\, the numpy package is the part of the de facto standard library for any scientific and numerical programming. Here we will introduce numpy\, especially numpy arrays and their built in functions (i.e. “methods”). Here\, we will also consider how to speed up numpy code using the Numba just-in-time compiler. \nTopic 2: Data processing with pandas. The pandas library provides means to represent and manipulate data frames. Like numpy\, pandas can be see as part of the de facto standard library for data oriented uses of Python. Here\, we will focus on data wrangling including selecting rows and columns by name and other criteria\, applying functions to the selected data\, aggregating the data. For this\, we will use Pandas directly\, and also helper packages like siuba. \nThursday 5th – Classes from 10:00 to 18:00 \nTopic 3: Data Visualization. Python provides many options for data visualization. The matplotlib library is a low level plotting library that allows for considerable control of the plot\, albeit at the price of a considerable amount ofm low level code. Based on matplotlib\, and providing a much higher level interface to the plot\, is the seaborn library. This allows us to produce complex data visualizations with a minimal amount of code. Similar to seaborn is ggplot\, which is a direct port of the widely used R based visualization library. \nTopic 4: Statistical data analysis. In this section\, we will describe how to perform widely used statistical analysis in Python. Here we will start with the statsmodels\, which provides linear and generalized linear models as well as many other widely used statistical models. We will also cover rpy2\, which is and interface from Python to R. This allows us to access all of the the power of R from within Python. \nTopic 5: Symbolic mathematics. Symbolic mathematics systems\, also known as computer algebra systems\, allow us to algebraically manipulate and solve symbolic mathematical expression. In Python\, the principal symbolic mathematics library is sympy. This allows us simplify mathematical expressions\, compute derivatives\, integrals\, and limits\, solve equations\, algebraically manipulate matrices\, and more. \nTopic 6: Parallel processing. In this section\, we will cover how to parallelize code to take advantage of multiple processors. While there are many ways to accomplish this in Python\, here we will focus on the multiprocessing \n			\n				\n				\n				\n				\n				Course Instructor\n \n\n\n\nDr. Mark Andrews\n\nWorks AtSenior Lecturer\, Psychology Department\, Nottingham Trent University\, England \n\nTeaches\nFree 1 day intro to r and r studio (FIRR)\nIntroduction To Statistics Using R And Rstudio (IRRS03)\nIntroduction to generalised linear models using r and rstudio (IGLM)\nIntroduction to mixed models using r and rstudio (IMMR)\nNonlinear regression using generalized additive models (GAMR)\nIntroduction to hidden markov and state space models (HMSS)\nIntroduction to machine learning and deep learning using r (IMDL)\nModel selection and model simplification (MSMS)\nData visualization using gg plot 2 (r and rstudio) (DVGG)\nData wrangling using r and rstudio (DWRS)\nReproducible data science using rmarkdown\, git\, r packages\, docker\, make & drake\, and other tools (RDRP)\nIntroduction/fundamentals of bayesian data analysis statistics using R (FBDA)\nBayesian data analysis (BADA)\nBayesian approaches to regression and mixed effects models using r and brms (BARM)\nIntroduction to stan for bayesian data analysis (ISBD)\nIntroduction to unix (UNIX01)\nIntroduction to python (PYIN03)\nIntroduction to scientific\, numerical\, and data analysis programming in python (PYSC03)\nMachine learning and deep learning using python (PYML03)\nPython for data science\, machine learning\, and scientific computing (PDMS02)\n\n  \nPersonal website \n\n\nResearchGate \nGoogle Scholar \nMark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham\, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition\, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis\, particularly as applied to data from the social and behavioural sciences. Since 2015\, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences.
URL:https://prstats.preprodw.com/course/introduction-to-scientific-numerical-and-data-analysis-programming-in-python-pysc03/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom
CATEGORIES:Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/PYSC03.png
GEO:53.1423672;-7.6920536
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20220427
DTEND;VALUE=DATE:20220429
DTSTAMP:20260419T064422
CREATED:20220224T223604Z
LAST-MODIFIED:20220329T153816Z
UID:10000397-1651017600-1651190399@prstats.preprodw.com
SUMMARY:ONLINE COURSE – Introduction To Python And Programming In Python (PYIN03) This course will be delivered live
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nWednesday\, April 20th\, 2022\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – GMT – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you).\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				About This Course\n				\nPython is one of the most widely used and highly valued programming languages in the world\, and is especially widely used in data science\, machine learning\, and in other scientific computing applications. In order to use Python confidently and competently for these applications\, it is necessary to have a solid foundation in the fundamentals of general purpose Python. This two day course provides a general introduction to the Python environment\, the Python language\, and general purpose programming in Python. We cover how to install and set up a Python computing environment\, describing how to set virtual environments\, how to use Python package installers\, and overview some Python integrated development environments (IDE) and Python Jupyter notebooks. We then provide a comprehensive introduction to programming in Python\, covering all the following major topics: data types and data container types\, conditionals\, iterations\, functional programming\, object oriented programming\, modules\, packages\, and imports. Note that in this course\, we will not be covering numerical and scientific programming in Python directly. That is provided in a subsequent two-day course\, for which the topics covered in this course are a necessary prerequisite. \n\n			\n				\n				\n				\n				\n				Intended Audiences\n				\nThis course is aimed at anyone who is interested in learning the fundamentals of Python generally and especially for ultimately using Python for data science and scientific applications. Although these applications are not covered directly here\, but are covered in a subsequent course\, the fundamentals taught here are vital for master data science and scientific applications of Python. \n\n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely\n			\n				\n				\n				\n				\n				Course Details\n				Availability – TBC \nDuration – 2 days \nContact hours – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English\n			\n				\n				\n				\n				\n				Teaching Format\n				This course will be hands-on and workshop based. Throughout each day\, there will be some brief introductory remarks for each new topic\, introducing and explaining key concepts. \nThe course will take place online using Zoom. On each day\, the live video broadcasts will occur between (UK local time) at:\n• 10am-12pm\n• 1pm-3pm\n• 4pm-6pm \nAll sessions will be video recorded and made available to all attendees as soon as possible\, hopefully soon after each 2hr session. Attendees in different time zones will be able to join in to some of these live broadcasts\, even if all of them are not convenient times. By joining any live sessions that are possible\, this will allow attendees to benefit from asking questions and having discussions\, rather than just watching prerecorded sessions. Although not strictly required\, using a large monitor or preferably even a second monitor will make the learning experience better. All the sessions will be video recorded\, and made available immediately on a private video hosting website. Any materials\, such as slides\, data sets\, etc.\, will be shared via GitHub.\n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				No particular knowledge of mathematics or statistics is required.\n			\n				\n				\n				\n				\n				Assumed computer background\n				\nNo prior experience with Python or any other programming language is required. Of course\, any familiarity with any other programming will be helpful\, but is not required. \n\n			\n				\n				\n				\n				\n				Equipment and software requirements\n				\nAttendees of the course must use a computer with Python (version 3) installed. This can in fact be done entirely online for free using Google’s Colaboratory without needing to install any software on your own laptop or desktop. If you are new to Python\, this approach is highly recommended. You will be able to immediately starting learning Python without any installation or configuration of software. This entire course can be done using this approach. \nIf you prefer to install and use Python on your machine\, instructions on how to install and configure all the software needed for this course are provided here. We will also provide time during the workshops to ensure that all software is installed and configured properly. \n\n\n  \n\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.\n			\n				\n				\n				\n				\n				\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE PROGRAMME\nWednesday 27th \nClasses from 10:00 to 18:00 \nTopic 1: Installing and setting up Python. There are many ways to write and execute code in Python. Which to use depends on personal preference and the type of programming that is being done. Here\, we will explore some of the commonly used Integrated Development Environments (IDE) for Python\, which include Spyder and PyCharm. Here\, we will also introduce Jupyter notebooks\, which are widely used for scientific applications of Python\, and are an excellent tool for doing reproducible interactive work. Also as part of this topic\, we will describe how to use virtual environments and package installers such as pip and conda. \nTopic 2: Data Structures. We will begin our coverage of programming with Python by introducing its different data structures.and operations on data structures This will begin with the elementary data types such as integers\, floats\, Booleans\, and strings\, and the common operations that can be applied to these data types. We will then proceed to the so-called collection data structures\, which primarily include lists\, dictionaries\, tuples\, and sets. \nTopic 3: Programming I. Having introduced Python’s data types\, we will now turn to how to program in Python. We will begin with iteration\, such as the for and while Here\, we also cover some of Python’s functional programming features\, specifically list\, dictionary\, and set comprehensions. \nThursday 28th \nClasses from 10:00 to 18:00 \nTopic 4: Programming II. Having covered iterations\, we now turn to other major programming features in Python\, specifically\, conditionals\, functions\, and exceptions. \nTopic 5: Object Oriented Programming. Python is an object oriented language and object oriented programming in Python is extensively used in anything beyond the very simplest types of programs. Moreover\, compared to other languages\, object oriented programming in Python is relatively easy to learn. Here\, we provide a comprehensive introduction to object oriented programming in Python. \nTopic 6: Modules\, packages\, and imports. Python is extended by hundreds of thousands of additional packages. Here\, we will cover how to install and import these packages\, but more importantly\, we will show how to write our own modules and packages\, which is remarkably easy in Python relative to some programming languages. \n			\n				\n				\n				\n				\n				Course Instructor\n \n\n\n\nDr. Mark Andrews\n\nWorks At\nSenior Lecturer\, Psychology Department\, Nottingham Trent University\, England \n\nTeaches\nFree 1 day intro to r and r studio (FIRR)\nIntroduction To Statistics Using R And Rstudio (IRRS03)\nIntroduction to generalised linear models using r and rstudio (IGLM)\nIntroduction to mixed models using r and rstudio (IMMR)\nNonlinear regression using generalized additive models (GAMR)\nIntroduction to hidden markov and state space models (HMSS)\nIntroduction to machine learning and deep learning using r (IMDL)\nModel selection and model simplification (MSMS)\nData visualization using gg plot 2 (r and rstudio) (DVGG)\nData wrangling using r and rstudio (DWRS)\nReproducible data science using rmarkdown\, git\, r packages\, docker\, make & drake\, and other tools (RDRP)\nIntroduction/fundamentals of bayesian data analysis statistics using R (FBDA)\nBayesian data analysis (BADA)\nBayesian approaches to regression and mixed effects models using r and brms (BARM)\nIntroduction to stan for bayesian data analysis (ISBD)\nIntroduction to unix (UNIX01)\nIntroduction to python (PYIN03)\nIntroduction to scientific\, numerical\, and data analysis programming in python (PYSC03)\nMachine learning and deep learning using python (PYML03)\nPython for data science\, machine learning\, and scientific computing (PDMS02)\n\n  \nPersonal website\n\nResearchGate \nGoogle Scholar \nMark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham\, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition\, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis\, particularly as applied to data from the social and behavioural sciences. Since 2015\, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences.
URL:https://prstats.preprodw.com/course/introduction-to-python-and-programming-in-python-pyin03/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/PYIN03R.png
GEO:53.1423672;-7.6920536
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20220214
DTEND;VALUE=DATE:20220219
DTSTAMP:20260419T064422
CREATED:20190808T160414Z
LAST-MODIFIED:20221019T153619Z
UID:10000300-1644796800-1645228799@prstats.preprodw.com
SUMMARY:ONLINE COURSE - GIS And Remote Sensing Analyses With R (GARM01) This course will be delivered live
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nMonday\, February 14th\, 2022\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – Western European Standard Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				About This Course\n				The course will cover the basics to perform spatial analyses using R as a Geographical Information System (GIS) platform and Remote Sensing as main data source. The course will provide a brief theoretical background of GIS tools and Remote Sensing data and techniques. By the end of this 4-day practical course\, attendees will have the capacity to search satellite imagery\, to manipulate Remote Sensing data\, to create new variables\, as well as to choose the best spatial tools and techniques to perform spatial analyses and interpret their results. \nThe course will be mainly practical\, with some theoretical lectures. All modelling processes and calculations will be performed with R\, the free software environment for statistical computing and graphics (http://www.r-project.org/). Attendees will learn to use the Rpackage RSToolbox for Remote Sensing image processing and analysis such as calculating spectral indices\, principal component transformation\, or unsupervised and supervised classification. \n			\n				\n				\n				\n				\n				Intended Audiences\n				This course is orientated to PhD and MSc students\, as well as other students and researchers working on biogeography\, spatial ecology\, or related disciplines. \n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely \n			\n				\n				\n				\n				\n				Course Details\n				Availability – 25 places \nDuration – 4 days \nContact hours – Approx. 28 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English \n			\n				\n				\n				\n				\n				Teaching Format\n				Introductory lectures on the concepts and applications of GIS and Remote Sensing.Practical lectures on most used spatial tools. Presentations and round-table discussions about the analysis requirements of attendees (option for them to bring their own data). Data sets for computer practical modules will be provided by the instructor\, but participants are welcome to bring their own data. \n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				Basic knowledge in Geographical Information Systems\, Remote Sensing\, and spatial analyses. \n			\n				\n				\n				\n				\n				Assumed computer background\n				Familiarity with R. Ability to import/export data\, manipulate data frames\, fit basic statistical models & generate simple exploratory and diagnostic plots. \n			\n				\n				\n				\n				\n				Equipment and software requirements\n				\nA laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. \n\n\nAll the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \n\n\nA working webcam is desirable for enhanced interactivity during the live sessions\, we encourage attendees to keep their cameras on during live zoom sessions. \n\n\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will improve he learning experience \n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n \n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \n			\n				\n				\n				\n				\n				If you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Programme\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Monday 21st\n				Classes from 09:00 to 17:00Theory – Introduction to GIS.Practical – Introduction to GIS with R: Import and plot data.Theory – Coordinate systems.Practical – Projecting vectorial & raster files. \n			\n				\n				\n				\n				\n				Tuesday 22nd\n				Classes from 09:30 – 17:00Theory – Vector database operations.Practical – Attribute and spatial queries: join/merge\, filter/subset\, select by attribute\, select bylocation\, summarize\, add/calculate new attributes (columns)\, plot attributes.Theory – Vector analyses.P: Vector analyses – buffer\, merge\, dissolve\, intersect\, union\, select\, calculate areas. \n			\n				\n				\n				\n				\n				Wednesday 23rd\n				Classes from 09:30 – 17:00Theory – Raster GIS.Practical – Raster analyses: rasterize\, crop\, mask\, merge\, distance surface\, zonal statistics.Theory – Introduction to Remote Sensing. RS as main data source: RS sensors & variables.RS software.Practical – Getting and plotting RS data. Downloading\, reading\, and plotting RS data in R.Manipulating satellite data. \n			\n				\n				\n				\n				\n				Thursday 24th\n				Classes from 09:30 – 17:00Theory – Working with RS variables. Image classification\, Vegetation indexes\, data fusion.Practical – Calculating RS variables with RStoolbox: Vegetation indexes and classificationmethods.Theory: Remote Sensing applications to biologyPractical: Statistical analyses with RS data. \n			\n			\n				\n				\n				\n				\n				\n				\n					Dr. Neftali Sillero\n					\n					Neftalí Sillero works in the analysis and identification of biodiversity spatial patterns\, from species to populations and individuals. For this\, he uses four powerful tools to better understand how space influence biodiversity: Geographical Information Systems\, Remote Sensing\, Ecological Niche Modelling\, and Spatial Statistics. His main areas of research are: application of new technologies on species’ distributions atlases\, ecological modelling of species’ ranges\, identification of biogeographical regions and species’ chorotypes\, mapping and modelling road-kill hotspots\, and spatial analyses of home ranges. \nHe has more than 10 years’ experience working in ecological niche models. He has authored >70 peer reviewed publications and he is since 2007 Chairman of the Mapping Committee of the Societas Herpetologica Europaea\, where he is the PI of the NA2RE project (www.na2re.ismai.pt)\, the New Atlas of Amphibians and Reptiles of Europe \nPersonal websiteWork WebpageResearchGateGoogleScholar \n					\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Teaches\n				\nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)\n\n			\n				\n				\n				\n				\n				Teaches\n				\nEcological Niche Modelling Using R (ENMR)\nAdvanced Ecological Niche Modelling Using R (ANMR)\nGIS And Remote Sensing Analyses With R (GARM)
URL:https://prstats.preprodw.com/course/gis-and-remote-sensing-analyses-with-r-garm01/
LOCATION:Delivered remotely (Portugal)\, Portugal
CATEGORIES:Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/GARM01R.png
GEO:39.399872;-8.224454
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20211213
DTEND;VALUE=DATE:20211218
DTSTAMP:20260419T064422
CREATED:20220425T145328Z
LAST-MODIFIED:20220804T114533Z
UID:10000408-1639353600-1639785599@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Remote Sensing With Aircraft And Drone LiDAR Sensors (RSLD01) This course will be delivered live
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nMonday\, December 12th\, 2021\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – GMT+1 – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				About This Course\n				\nUnmanned Airborne Vehicles (UAVs) equipped with consumer-grade imaging/ranging and direct geo-referencing systems have been proven as a potential Remote Sensing platform that could satisfy the needs of a wide range of civilian applications. The continuous developments in direct georeferencing and Remote Sensing (i.e.\, passive and active imaging sensors in the visible and infrared range – RGB cameras and LiDAR) is providing the professional geospatial community with ever-growing opportunities to provide accurate 3D information used in environmental research to collect information about the Earth\, such as vegetation and tree species. \n\n\nThis 4-day course aims to provide participants with an integrated \n\n\nend-to-end perspective going from measurement techniques to end- \n\n\nuser applications\, covering issues related to LiDAR sensors coupled on aircraft and UAVs\, computing exercises on the processing of 3D point clouds to produce geospatial products. \n\n			\n				\n				\n				\n				\n				Intended Audiences\n				\nAny researchers (PhD and MSc students\, post-docs\, primary investigators) and environmental professionals who are specialised in a variety of Earth Science disciplines and wish to expand and improve their knowledge and skills. \n\n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely \n			\n				\n				\n				\n				\n				Course Details\n				Availability – 30 places \nDuration – 4 days \nContact hours – Approx. 24 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English \n			\n				\n				\n				\n				\n				Teaching Format\n				\nThe course will be divided into theoretical lectures to introduce and explain key concepts and theories\, and practices with computing exercises on the processing of LiDAR data and point clouds. Afternoon practicals will be based on the topics covered in the morning lectures. \n\n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				\nFamiliarity with Geographic Information Systems and geospatial data (i.e.\, raster and vector data) could be useful\, but not mandatory. A basic understanding of physics radiation and proprieties of electromagnetic spectrum could be also useful\, but not required. \n\n			\n				\n				\n				\n				\n				Assumed computer background\n				\nNo prior experience with LiDAR processing software\, point cloud data or any programming language is required. \n\n			\n				\n				\n				\n				\n				Equipment and software requirements\n				\nAttendees of the course must use a computer with any Operating System installed (GNU/Linux\, MS Windows or MacOS). The course will use Open-Source software (FOSS) and some proprietary software which will be downloaded\, installed and configured during the lectures. \n\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n \n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \n			\n				\n				\n				\n				\n				\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE PROGRAMME\n\nMonday 6th February – Classes from 10:00 to 17:00 \n\n\nModule 1: Fundamentals of Light Detection and Ranging (LiDAR) technique. Theoretical principles of a LiDAR systems. Electronic and sensor components. Main differences between spatial\, aerial and terrestrial platforms. The physics of laser signals: Introduction to discrete and full-waveform LiDAR and signal return analysis. Resolutions and precisions achieved. Advantages and disadvantages of the technique. Practice: Introduction to LiDAR data\, platforms and services. Overview of the available processing software and programming languages/libraries. \n\n\nTuesday 7th February – Classes from 10:00 to 17:00 \n\n\nModule 2: Interpretation of LiDAR data. Introduction to metrics/products such as Digital Elevation Models\, Digital Terrain Models and Canopy Height Models. Tree delineation approaches and algorithms (ex. Watershed Algorithm). Discrete versus full-waveform LiDAR data. Echo Decomposition for peak point extraction. Voxelisation of full-waveform LiDAR data. Introduction to binary files: Discrete and full-waveform LiDAR LAS files formats. Practice: Tridimensional point cloud processing and analysis. Filtering\, measuring and classification of LiDAR point clouds. \n\n\nMonday 13th February – Classes from 10:00 to 17:00 \n\n\nModule 3: Managing and exploring a LAS dataset. Visualization advanced techniques\, metadata analysis and content reports\, LiDAR points classification into ground points and non-ground points\, buildings and high vegetation classification. Coordinate Reference System transforms. LIDAR points triangulation into a TIN in order to create a Digital Elevation Model. Elevation contours extraction from a LiDAR point cloud and boundary polygon extraction. RGB colour sampled from an orthomosaic. \n\n\nTuesday 14th February – Classes from 10:00 to 17:00 \n\n\nModule 4: Different applications for LiDAR data: biodiversity monitoring\, forest health monitoring\, urban planning\, wood trade\, archaeology and heritage monitoring and automated driving. Other types of LiDAR systems: Space-based liDAR for measuring ice sheet mass balance\, cloud and aerosol heights. Bathymetric LiDAR for the study of underwater depth of ocean floors. Practice: Post-processing of LiDAR products\, Digital Terrain Model and elevation profile analysis. Measurements of distances\, areas and volumes. Integration with external geospatial data in a Geographic Information System (GIS). \n\n			\n				\n				\n				\n				\n				Course Instructor\n \nDr. Nelson Pires\n\n– Works at: University of Porto\, Portugal \n\n\n– Delivers: \n\n\nRemote Sensing with satellite multi-spectral sensors (RSMS01) \n\n\nRemote Sensing with drone RGB and Near Infrared cameras (RSWD01) \n\n\nRemote Sensing with aircraft and drone LiDAR sensors (RSLD) \n\n\nNelson holds a degree in Physics and Surveying Engineering\, a MSc and PhD degrees in Surveying Engineering from University of Porto. With more than 10 years of experience in teaching at higher education institutions and doing research work in several geospatial subjects. Past and recent research includes subjects in atmospheric corrections with high-precision Global Navigation Satellite Systems analysis\, aerial and close-range photogrammetric studies with drones for coastal monitoring and map production\, multi-spectral and SAR-imaging Remote Sensing for ocean wind-generated waves and ocean dynamics. \n\n\nORCID: https://orcid.org/0000-0002-6629-8060
URL:https://prstats.preprodw.com/course/online-course-remote-sensing-with-aircraft-and-drone-lidar-sensors-rsld01/
LOCATION:Delivered remotely (Portugal)\, Portugal
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/VGNR04R.png
GEO:39.399872;-8.224454
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20210920
DTEND;VALUE=DATE:20210921
DTSTAMP:20260419T064422
CREATED:20220219T015845Z
LAST-MODIFIED:20220804T113932Z
UID:10000314-1632096000-1632182399@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Remote Sensing With Satellite Multi-Spectral Sensors (RSMS01) This course will be delivered live
DESCRIPTION:ONLINE COURSE – Metabarcoding Pipelines for Eukariotic Communities (MPEC01) This course will be delivered live\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Event Date \nMonday\, September 20th\, 2021\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n					\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n				\n					\n						\n						\n							\n							\n						\n					\n				\n				\n				\n				\n			\n			\n				\n				\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nTime Zone\nTIME ZONE – GMT+1 – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you. \n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				Course Details\n				\nSatellite Remote Sensing has become a common tool to investigate the different fields of Earth and environmental sciences. The progress of the performance capabilities of the optoelectronic and radar devices mounted on-board remote sensing platforms have further improved the capability of instruments to acquire information about the Earth and its resources for global\, regional and local assessments. Disciplines such as agriculture\, hydrology\, and ecosystem studies have all developed a strong Remote Sensing component\, facilitating our understanding of the environment and its processes over a broad range of spatial and temporal scales. \n\n\nThis 4-day course aims to provide participants with an integrated end-to-end perspective going from measurement techniques to end-user applications\, covering issues related to Remote Sensing\, Earth System Modelling and Data Assimilation as well as hands-on computing exercises on the processing of Earth Observation data. \n\n			\n				\n				\n				\n				\n				Intended Audiences\n				\nAny researchers (PhD and MSc students\, post-docs\, primary investigators) and environmental professionals who are specialised in a variety of Earth Science disciplines and wish to expand and improve their knowledge and skills. \n\n			\n				\n				\n				\n				\n				Venue\n				Delivered remotely \n			\n				\n				\n				\n				\n				Course Information\n				Availability – 30 places \nDuration – 4 days \nContact hours – Approx. 24 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English \n			\n				\n				\n				\n				\n				Teaching Format\n				\nThe course will be divided into theoretical lectures to introduce and explain key concepts and theories\, and practices with computing exercises on the processing of Earth Observation data. Afternoon practicals will be based on the topics covered in the morning lectures. \n\n			\n				\n				\n				\n				\n				Assumed quantitative knowledge\n				\nFamiliarity with Geographic Information Systems and geospatial data (i.e.\, raster and vector data) could be useful\, but not mandatory. A basic understanding of physics radiation and proprieties of electromagnetic spectrum could be also useful\, but not required. \n\n			\n				\n				\n				\n				\n				Assumed computer background\n				\nNo prior experience with Remote Sensing software and data or any programming language is required. Familiarity with any digital image processing technique will be helpful\, but is not required. \n\n			\n				\n				\n				\n				\n				Equipment and software requirements\n				\nAttendees of the course must use a computer with any Operating System installed (GNU/Linux\, MS Windows or MacOS). The course will use only Open-Source software (FOSS) which will be downloaded\, installed and configured during the lectures. \n\n			\n			\n			\n				\n				\n				\n				\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				\n	\n		Tickets	\n	\n	\n	\n	\n	\n	\n		The numbers below include tickets for this event already in your cart. Clicking "Get Tickets" will allow you to edit any existing attendee information as well as change ticket quantities.	\n\n\n\n	\n	Tickets are no longer available\n\n\n	\n		\n		\n		\n	\n\n\n\n\n\n	\n\n \n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \n			\n				\n				\n				\n				\n				\nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n			\n			\n				\n				\n				\n				\n			\n			\n				\n				\n				\n				\n				\n				\n				\n				\n				\n				COURSE PROGRAMME\n\nMonday 26th September – Classes from 10:00 to 17:00 \n\n\nModule 1: Fundamentals of Remote Sensing. Concepts of satellite orbits\, spacial resolutions\, temporal resolutions\, spectral and radiometric resolutions. Different types of sensors and processing levels of Earth Observation satellites. The physics of atmosphere and spectral signatures. Conceptual understanding of Remote Sensing\, where the participants will be able to identify its advantages and disadvantages. Introduction to data platforms\, software tools\, web portals\, and environmental monitoring applications. Practice: Introduction to Remote Sensing software. \n\n\nTuesday 27th September – Classes from 10:00 to 17:00 \n\n\nModule 2: Earth Observation Programmes. The National Aeronautics and Space Administration (NASA) LANDSAT Program and the European Space Agency (ESA) Copernicus/SENTINEL Program. History and Objectives. Satellite missions chronology. Different spatial\, temporal\, spectral and radiometric resolutions. LANDSAT Multispectral Scanner (MSS) and SENTINEL-2 Multispectral Instrument (MSI) sensor designs. Uses of Earth Observation satellite imagery for natural resources management\, climate change\, environmental disasters and ecology. Practice: Introduction to satellite image processing.  \n\n\nThursday 29th September – Classes from 10:00 to 17:00 \n\n\nModule 3: Remote Sensing for Vegetation Monitoring and Agricultural Applications. Satellite observations to assess a wide variety of geophysical and biophysical parameters\, including precipitation\, temperature\, evapotranspiration\, soil moisture\, and vegetation health. Band combination e index classification for vegetation monitoring. Remote Sensing data for agriculture monitoring\, specifically drought and crop monitoring. Practice: Supervised and unsupervised classification methods. \n\n\nFriday 30th September – Classes from 10:00 to 17:00 \n\n\nModule 4: Satellite Applications for Biodiversity Conservation. Specific applications and hands-on demonstrations of how to use Remote Sensing data to derive conservation policies and management decisions. Remote Sensing for Conservation and Biodiversity: Animal Movement\, Dynamic Habitat Index for Biodiversity\, Vegetation Carbon Stock Corridors and techniques for Land Change Detection. Land Management and Ecosystem Based Tools: Coral Reef Watch and MODIS NDVI Anomalies and Time Series. Practice: Image fusion and Pansharpening techniques. \n\n			\n				\n				\n				\n				\n				Course Instructor\n \n \n \n \n \n \nDr. Nelson Pires\n\n– Works at: University of Porto\, Portugal \n\n\n– Delivers: \n\n\nRemote Sensing with satellite multi-spectral sensors (RSMS01) \n\n\nRemote Sensing with drone RGB and Near Infrared cameras (RSWD01) \n\n\nRemote Sensing with aircraft and drone LiDAR sensors (RSLD) \n\n\nNelson holds a degree in Physics and Surveying Engineering\, a MSc and PhD degrees in Surveying Engineering from University of Porto. With more than 10 years of experience in teaching at higher education institutions and doing research work in several geospatial subjects. Past and recent research includes subjects in atmospheric corrections with high-precision Global Navigation Satellite Systems analysis\, aerial and close-range photogrammetric studies with drones for coastal monitoring and map production\, multi-spectral and SAR-imaging Remote Sensing for ocean wind-generated waves and ocean dynamics. \n\n\nORCID: https://orcid.org/0000-0002-6629-8060 \n\n 
URL:https://prstats.preprodw.com/course/remote-sensing-with-satellite-multi-spectral-sensors-rsms01/
LOCATION:Delivered remotely (Portugal)\, Portugal
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/03/RSMS01.png
GEO:39.399872;-8.224454
END:VEVENT
END:VCALENDAR