BEGIN:VCALENDAR VERSION:2.0 PRODID:-//PR Statistics - ECPv6.10.0//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:PR Statistics X-ORIGINAL-URL:https://prstats.preprodw.com X-WR-CALDESC:Events for PR Statistics REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:Europe/London BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20220327T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20221030T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0000 TZOFFSETTO:+0100 TZNAME:BST DTSTART:20230326T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0100 TZOFFSETTO:+0000 TZNAME:GMT DTSTART:20231029T010000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;VALUE=DATE:20231204 DTEND;VALUE=DATE:20231209 DTSTAMP:20260419T041245 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:Oliver Hooker (Course Organiser)\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 \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:20231204 DTEND;VALUE=DATE:20231205 DTSTAMP:20260419T041245 CREATED:20240220T161332Z LAST-MODIFIED:20240709T140540Z UID:10000450-1701648000-1701734399@prstats.preprodw.com SUMMARY:ONLINE COURSE - Machine Learning with R (Intermediate - Advanced) (MLIAPR) DESCRIPTION:Oliver Hooker (Course Organiser)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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 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 \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 Day 1\n DAY 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 Day 2\n DAY 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 Day 3\n DAY 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 Day 4\n DAY 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 Gate\nGoogle Scholar\nORCID\nLinkedIn\nGitHub URL:https://prstats.preprodw.com/course/online-course-machine-learning-with-r-intermediate-advanced-mliapr/ LOCATION:Delivered remotely (Ireland)\, Western European Time\, Ireland CATEGORIES:Previously Recorded 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:20260419T041245 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:Oliver Hooker (Course Organiser)\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 \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:20260419T041245 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:Oliver Hooker (Course Organiser)\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' 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 \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:20230327 DTEND;VALUE=DATE:20230331 DTSTAMP:20260419T041245 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:Oliver Hooker (Course Organiser)\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 \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:20260419T041246 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:Oliver Hooker (Course Organiser)\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 \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:20230313 DTEND;VALUE=DATE:20230318 DTSTAMP:20260419T041246 CREATED:20220303T113648Z LAST-MODIFIED:20221011T141420Z UID:10000405-1678665600-1679097599@prstats.preprodw.com SUMMARY:ONLINE COURSE -Introduction to eco-phylogenetics and comparative analyses using R (ECPH02) This course will be delivered live DESCRIPTION:Oliver Hooker (Course Organiser)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, March 13th\, 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\nPre Recorded \n \n \n \n \n \n \n \n \n \n \n About This Course\n In this five day course\, we provide an introduction to eco-phylogenetics and comparative analyses using R. We begin by providing an  overview on the use of phylogenies as a tool for evolutionary biologists and modern techniques to deal with large phylogenies and to incorporate phylogenetic uncertainty in the analyses (day 1). We then cover some of the most relevant eco-phylogenetic analyses and provide examples from the community to themacro-ecological scale (day 2-3). Finally\, we introduce a diversity of classic and modern phylogenetic comparative methods to consider the historical relationship of lineages in eco-evolutionary research\, including models of trait evolution\, analysis of clade diversification and the use of phylogenies in spatial distribution models among others (day 4-5). \n \n \n \n \n \n Intended Audiences\n This course is aimed at anyone who wishes to introduce into phylogenetic ecology and comparative analyses. \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. \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• 14:30pm-16:30pm \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. 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 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. summary statistics\, distributions). As this is an introductory course\, no phylogenetic background is required. \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 Attendees of the course must use a computer with R/RStudio installed\, as well as the necessary additional R packages. Instructions on how to install the software will be provided before the start of the course. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links. \nhttps://cran.r-project.org/Download RStudio \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 \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 13th\n Classes from 8:00 to 16:30 \n• Introduction and a brief phylogenetic primer. Basic terminology for non- phylogeneticists\, phylogenetic inference (quick overview)\, phylogenies aevolutionary hypotheses. \n• Working with phylogenies. Newick format and structure of the R phylo object. Elementary operations on phylogenies (pruning\, resolving polytomies\, sticking species). Visualizing large phylogenies. \n• Building purpose-specific mega-trees from extant trees and incorporating phylogenetic uncertainty. Software phylocom\, V.PhyloMaker\, SUNPLIN and randtip R package. \n  \n \n \n \n \n \n Tuesday 14th\n • Introduction to the eco-phylogenetic framework\, classical conception and posterior modifications. \n• Phylogenetic alpha diversity (how much? how different? how regular?). Community data matrices\, null models\, applications to biodiversity conservation. \n• Phylogenetic beta diversity. The turnover and nestedness component of beta diversity. \n \n \n \n \n \n Wednesday 15th\n • Incorporating the exact branching pattern of phylogenies into eco-phylogenetic analyses. \n• Spatial phylogenetics. RPD\, RPE and CANEPE analysis. \n• Overview of functional trait ecology. Functional richness\, evenness and divergence.Community weighted means. \n• Phylogenetic imputation of trait datasets. Bounding prediction uncertainty using evolutionary models. Phylogenies as a null model in ecology \n \n \n \n \n \n Thursday 16th\n \nThe phylogenetic comparative method\, from independent contrasts to sophisticated modelling.\nAnalyses of phylogenetic signal and models of evolution: rationale\, common- practice\, and new trends.\nCorrelated evolution and ancestral trait reconstruction.\nAnalyses of diversification\, speciation and extinction rates in a geographic context.\n \n\n \n \n \n \n \n Friday 17th\n \nThe need to account for phylogenetic relationships in models.\nMost common phylogenetic modelling approaches: PGLS\, PGLMM\, BayesianPMM.\nPutting phylogenies in the geography: how to combine phylogenies with species distribution models.\n\n \n \n \n \n \n \n Course Instructor\n \n  URL:https://prstats.preprodw.com/course/introduction-to-eco-phylogenetics-and-comparative-analyses-using-r-ecph02/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time Zone\, United Kingdom CATEGORIES: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:20260419T041246 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:Oliver Hooker (Course Organiser)\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 \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:20230223 DTEND;VALUE=DATE:20230225 DTSTAMP:20260419T041246 CREATED:20200617T005308Z LAST-MODIFIED:20230214T123352Z UID:10000310-1677110400-1677283199@prstats.preprodw.com SUMMARY:ONLINE COURSE - Making Beautiful And Effective Maps In R (MAPR04) This course will delivered live DESCRIPTION:Oliver Hooker (Course Organiser)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nThursday\, February 23rd\, 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. \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 The aim of the course is to show you how to use R to make pretty\, yet appealing maps using the R programming language. Several R packages related to spatial data processing and visualization will be introduced during the course. The course will teach you how create publication-ready static maps\, animated maps\, interactive maps\, and simple map applications using a mixture of lectures and computer exercises. \nBy the end of the course participants should: \n\nUnderstand the basic concepts behind the tmap package\nBe able to create a variety types of static maps\, including raster maps\, choropleth maps\, and point maps\nKnow how to create interactive maps and simple map applications using the shiny package\nBe able to create facet maps and map animations to represent spatiotemporal phenomenon\nKnow how to utilize  specific-purpose mapping packages to create cartograms or grid maps\nHave the confidence to apply map making 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 to create publication-ready maps\, interactive maps for their websites\, or simple mapping web applications \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 to quickly create maps for their reports or websites \nThe course is designed for intermediate R users interested in maps making 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 Availability – 20 places \nDuration – 2 days \nContact hours – Approx. 16 hours \nECT’s – Equal to 1.5 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n The course will be a mixture of theoretical and practical. Each concept will be first described and explained\, and next the attendees will exercise the topics using provided data sets.\n \n \n \n \n \n Assumed quantitative knowledge\n Understanding basic GIS concepts\, such as spatial vector\, spatial raster\, coordinate reference systems would be beneficial\, but is not necessary. \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 \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 Thursday 24th\n Classes from 09:00 to 17:00Introduction to mapping packages in RMaking static mapsApplying point\, lines\, polygons\, and raster map layersCustomizing mapsCreating interactive mapsSaving maps \n \n \n \n \n \n Friday 25th\n Classes from 09:00 to 17:00Making facet mapsCreating animated mapsMaking inset mapsUsing specific-purpose mapping packagesCreating simple map applicationsOther mapping packages in R \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. \nResearchGateGoogleScholarORCIDLinkedInGitHub \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/making-beautiful-and-effective-maps-in-r-mapr04/ LOCATION:Delivered remotely (Poland)\, Central European Summer Time\, Poland CATEGORIES:Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/MAPR03R.png END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20230130 DTEND;VALUE=DATE:20230204 DTSTAMP:20260419T041246 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:Oliver Hooker (Course Organiser)\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&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 \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 & CarePre-Deployment TestingPreparing for Tagging & 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 & A \n\n\n \n \n \n \n \n Course Instructor\nStephanie Smedbol\nDirector of Product Management & Customer Success\, Innovasea Fish TrackingStephanie has been working with Innovasea for 12 years in a number of roles in R&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:20260419T041246 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:Oliver Hooker (Course Organiser)\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 \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:20260419T041246 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:Oliver Hooker (Course Organiser)\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 \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:20221212 DTEND;VALUE=DATE:20221216 DTSTAMP:20260419T041246 CREATED:20220310T151719Z LAST-MODIFIED:20230727T113948Z UID:10000377-1670803200-1671148799@prstats.preprodw.com SUMMARY:Ecological niche modelling using R (ENMRPR) DESCRIPTION:Oliver Hooker (Course Organiser)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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 \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 Course Details\n Last Up-Dated 15:03:2019 \nDuration – Approx. 28 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 \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 Day 1\n Day 1 – Approx. 7 hours \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 Day 2\n Day 2 – Approx. 7 hours \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 Day 3\n Day 3 – Approx. 7 hours \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 Day 4\n Day 4 – Approx. 7 hours \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 Day 5\n Day 5 – Approx. 7 hours \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-enmrpr/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:Previously Recorded 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:20260419T041246 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:Oliver Hooker (Course Organiser)\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 \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:20260419T041246 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:Oliver Hooker (Course Organiser)\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 \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:20260419T041246 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:Oliver Hooker (Course Organiser)\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 \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;TZID=Europe/London:20220713T160000 DTEND;TZID=Europe/London:20220713T163000 DTSTAMP:20260419T041246 CREATED:20220304T185953Z LAST-MODIFIED:20220628T122226Z UID:10000369-1657728000-1657729800@prstats.preprodw.com SUMMARY:FREE SEMINAR - Remote Sensing With Satellite Multi-Spectral Sensors\, drone RGB and Near Infrared cameras and Aircraft And Drone LiDAR Sensors (RSFS01) DESCRIPTION:Oliver Hooker (Course Organiser)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nWednesday\, July 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 \nFree seminar \n\n\nThis is a free ~30 minute seminar including a Q and A session at the end for our up-coming courses\, Remote Sensing with satellite multi-spectral sensors (RSMS01)\, Remote Sensing with drone RGB and Near Infrared cameras (RSWD01) and Remote Sensing with aircraft and drone LiDAR sensors (RSLD01). \n\n\nTime \n16:00 – 16:30 GMT \n\n\nSpeaker \n\n\nCourse Instructor Nelson Pires \nAbout these courses \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.This 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\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. This 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 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 \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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 Course Instructor\n \n\n\n\n\n\nDr. Nelson Pires\nWorks at –\nTeaches – URL:https://prstats.preprodw.com/course/free-seminar-on-remote-sensing/ LOCATION:Delivered remotely (Portugal)\, Portugal CATEGORIES:Free Seminars,Home Seminars ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/03/RSMS01-1.png GEO:39.399872;-8.224454 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20220620 DTEND;VALUE=DATE:20220623 DTSTAMP:20260419T041246 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:Oliver Hooker (Course Organiser)\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 \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:20260419T041247 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:Oliver Hooker (Course Organiser)\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 \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:20220521 DTSTAMP:20260419T041247 CREATED:20220218T165305Z LAST-MODIFIED:20220224T004857Z UID:10000350-1652832000-1653091199@prstats.preprodw.com SUMMARY:ONLINE COURSE – Statistical Radiocarbon Dating And Age Depth Modelling (RDAD01) This course will be delivered live DESCRIPTION:Oliver Hooker (Course Organiser)\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 – 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 This course will provide attendees with the basics to understand and implement age-depth models for partially dated stratigraphic data. The focus will be on radiocarbon dating but the approach extends to many other forms of dated information\, and will be relevant to those who have a wide variety of palaeo-environmental reconstruction problems. As is common in age-depth modelling\, the Bayesian paradigm will be used to create the age-depth models\, though no prior experience with Bayesian software or methods is required. The course will cover the use of multiple different R packages though the focus will be on the author’s own Bchron software. Attendees are encouraged to bring their own data sets and explore them using the tools covered during the course. \n \n \n \n \n \n Intended Audiences\n Research 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 Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time zone – Western European Time \nAvailability – 30 places \nDuration – 3 days \nContact hours – Approx. 21 hours \nECT’s – Equal to 2 ECT’s \nLanguage – English \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 A mixture of lectures and hands-on practicals. 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 Assumed quantitative knowledge\n Basic knowledge of statistics and statistical models (e.g. generalised linear modelling) required. Basic experience of exploring data sets\, and fitting regression and generalised linear models with R required. \n \n \n \n \n \n Assumed computer background\n Some 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 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 \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\nWednesday 18th – Classes from 09:30 – 17:30Day 1: Introduction to Radiocarbon dating; introduction to Bayesian statistics; basics of radiocarbon calibration \nThursday 19th – Classes from 09:30 – 17:30Day 2: Methods for calibrating radiocarbon dating; introducing prior information into radiocarbon date; basics of age-depth modelling \nFriday 20th – Classes from 09:30 – 17:30Day 3: Age-depth modelling approaches (Bacon\, Bchron\, Clam\, Oxcal); extending and using age-depth models in palaeo-environmental reconstruction \n\n  \n \n \n \n \n \n Course Instructor\n \n  \n  \nProf. Andrew Parnell \nWorks at – Teaches –  URL:https://prstats.preprodw.com/course/online-course-statistical-radiocarbon-dating-and-age-depth-modelling-rdad01-this-course-will-be-delivered-live/ LOCATION:Delivered remotely (Ireland)\, Western European Time\, Ireland CATEGORIES:Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/Screenshot-2022-01-12-at-17.28.45.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;VALUE=DATE:20220518 DTEND;VALUE=DATE:20220520 DTSTAMP:20260419T041247 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:Oliver Hooker (Course Organiser)\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 \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;TZID=Europe/London:20220517T150000 DTEND;TZID=Europe/London:20220517T153000 DTSTAMP:20260419T041247 CREATED:20220221T231747Z LAST-MODIFIED:20220517T124351Z UID:10000333-1652799600-1652801400@prstats.preprodw.com SUMMARY:FREE SEMINAR - Bayesian GLM's For Ecologists (BGFE01S) DESCRIPTION:Oliver Hooker (Course Organiser)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Registration is now closed\, if you would still like to register please send an email to oliverhooker@prstatistics.com and we will try and add you before the seminar start time.\nEvent Date \n​\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \nFree seminar \n\n\nThis is a free ~30 minute seminar including a Q and A session at the end for our up-coming course “Bayesian GLM’s for Ecologists”. \n\n\nTime \n\n\n15:00-15:30 GMT+1 \n\n\nSpeaker \n\n\nCourse Instructor Dr. Carl Smith \n\n\nAbout this course \nThis 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\nONLINE COURSE – Bayesian GLM’s For Ecologists (BGFE01) This course will be delivered live \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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/bayesian-glms-for-ecologists-bgfe01s/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom CATEGORIES:All Live Courses,Free Seminars,Home Seminars 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:20220504 DTEND;VALUE=DATE:20220506 DTSTAMP:20260419T041247 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:Oliver Hooker (Course Organiser)\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 \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:20260419T041247 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:Oliver Hooker (Course Organiser)\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 \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:20220425 DTEND;VALUE=DATE:20220429 DTSTAMP:20260419T041247 CREATED:20220218T162949Z LAST-MODIFIED:20220414T173819Z UID:10000349-1650844800-1651190399@prstats.preprodw.com SUMMARY:ONLINE COURSE – Advances In Spatial Analysis Of Multivariate Ecological Data: Theory And Practice (MVSP05) This course is pre-recorded with live help DESCRIPTION:Oliver Hooker (Course Organiser)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, April 25th\, 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 ‘PRE-RECORDED’ course\, lectures are pre-recorded and shared via zoom. The instructors will be available for live help with practicals and to answer any questions. A good internet connection is essential. \nTime Zone\nTIME ZONE – Multiple timezones – 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 course will describe recent methods (concepts and R tools) that can be used to analyse spatial patterns in community ecology. The umbrella concept of the course is beta diversity\, which is the spatial variation of communities. Researchers in spatial ecology\, population genetics and landscape genetics will find these methods useful as they are applicable to all types of communities (bacteria\, plants\, animals) sampled along transects\, regular grids or irregularly distributed sites. The new methods\, collectively referred to as spatial eigen-function analysis\, are grounded into techniques commonly used by community ecologists\, which will be described first: simple ordination (PCA\, CA\, PCoA)\, multivariate regression and canonical analysis\, permutation tests. The choice of dissimilarities that are appropriate for community composition data will also be discussed. The focal question is to determine how much of the community variation (beta diversity) is due to environmental sorting and to community-based processes\, including neutral processes. Recently developed methods to partition beta diversity in different ways will be presented. Extensions will be made to temporal and space-time data. \n \n \n \n \n \n Intended Audiences\n Research postgraduates\, practicing academics and primary investigators in spatial ecology particularly communities (bacteria\, plants\, animals) sampled along transects\, regular grids or irregularly distributed sites. The skills learnt can also be applied by management and environmental professionals in government and industry. \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Time Zone –Multiple timezones \nAvailability – TBC \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 This course will be a combination of pre-recorded lectures delivered by Prof. Pierre Legendre\, Practical sessions with live support via email or video link and final live summary with Q and A at the end of each day Prof Pierre Legendre. \nThere 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. \nThe recordings will be aired via zoom. Recordings last for approx. 4 hours. Followed by 3-hour practical. And then an approx. 1 hour (we have more time if needed) Q and A session with Pierre Legendre. \nRecordings will be aired to accommodate different time zones listed below in GMT. \nGroup 1Recordings 08:00 – 12:00Practical 12:30 – 15:30 \nGroup 2Recordings 12:00 – 16:00Practical 16:30 – 19:30 \nGroup 1 and 2Live Q and A – 20:00 – 21:00 (with Prof. Pierre Legendre) \nYou can use this link to find a time zone which suites you best. \nhttps://www.timeanddate.com/worldclock/?query=gmt \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. \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 \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 25th\n Lesson 1: Ordination in reduced spaceSection 1.0. Ordination in reduced space: An introductionSection 1.1. Principal component analysis (PCA)Section 1.2. Correspondence analysis (CA)Section 1.3. Principal coordinate analysis (PCoA)Section 1.4. Metric ordination methods in ecology (included with 1.3) \nPractical 1 \n \n \n \n \n \n Tuesday 26th\n Lesson 2: Dissimilarities and transformations \nLesson 3: Tests of statistical significance \nLesson 4: Linear regressionSection 4.1 Multiple linear regressionSection 4.2 Partial regression and variation partitioning \nPractical 2 \n \n \n \n \n \n Wednesday 27th\n Lesson 5: Canonical analysis \nLesson 6: Beta diversitySection 6.1. Partitioning beta diversitySection 6.2. Replacement and richness differenceSection 6.3. Temporal beta diversity \nPractical 3 \n \n \n \n \n \n Thursday 28th\n Lesson 7: Spatial modellingSection 7.1. Origin of spatial structures in ecologySection 7.2. Spatial eigenfunction modellingSection 7.3. Space-time interaction \nLesson 8: Mantel test in spatial analysis \nPractical 4 \n \n \n \n \n \n \n Course Instructor\n \n \n \n \n \n \n \nProf. Pierre Legendre\nComing Soon… URL:https://prstats.preprodw.com/course/advances-in-spatial-analysis-of-multivariate-ecological-data-theory-and-practice-mvsp05/ LOCATION:Delivered remotely (Canada) CATEGORIES:Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/MVSP04R.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/London:20220406T133000 DTEND;TZID=Europe/London:20220406T143000 DTSTAMP:20260419T041247 CREATED:20220221T223934Z LAST-MODIFIED:20220406T095735Z UID:10000358-1649251800-1649255400@prstats.preprodw.com SUMMARY:FREE SEMINAR - Introduction To Multivariate Analysis In Ecology And Evolutionary Biology using R (IMAE01S) This course will be delivered live DESCRIPTION:Oliver Hooker (Course Organiser)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nOnline registration has now closed\, please email oliverhooker@prstatistics.com to be added to the seminar or to receive a link to the recording\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \nFree seminar \n\n\nThis is a free ~30 minute seminar including a Q and A session at the end for our up-coming course “Introduction to Multivariate Analysis in Ecology and Evolutionary Biology using R” \n\n\nTime \n\n\n13:30-14:00 Eastern European Standard Time \n\n\nSpeaker \n\n\nCourse Instructor Dr. Antoine Becker-Scarpitta \n\n\nAbout this course \nThis community analytics course is designed for students who have recently started their projects or researchers who are starting using the R ecosystem. During this three-day course\, we will cover the basic concepts of multivariate analysis and their implementation in R. This course is a complement to the PR Statistic offering allowing also beginners and non-programmers to discover the statistical tools needed to analyze an ecological dataset in research\, natural resource management or conservation context. This course is not geared toward any particular taxonomic group or ecological system. \nWe will cover diversity indices\, distance measures and multivariate distance-based methods\, clustering\, classification\, and ordination techniques. We will focus on the concept of the methods and their implementation on R using different R packages. We will use real-world examples to implement analyses\, such as describing patterns along gradients of environmental or anthropogenic disturbances\, quantifying the effects of continuous and discrete predictors\, data mining. The course will consist of lectures\, work on R code scripts\, and exercises for participants. \n\n \n \n \n \n \n \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 URL:https://prstats.preprodw.com/course/introduction-to-multivariate-analysis-in-ecology-and-evolutionary-biology-using-r-imae01s/ LOCATION:Delivered remotely (Finland)\, Western European Time\, United Kingdom CATEGORIES:All Live Courses,Free Seminars,Home Seminars ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/IMAE01.png GEO:55.378051;-3.435973 END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/London:20220323T180000 DTEND;TZID=Europe/London:20220323T183000 DTSTAMP:20260419T041247 CREATED:20220221T205439Z LAST-MODIFIED:20220322T164050Z UID:10000320-1648058400-1648060200@prstats.preprodw.com SUMMARY:FREE SEMINAR – Advances in Spatial Analysis of Multivariate Ecological Data: Theory and Practice (MVSP05S) DESCRIPTION:Oliver Hooker (Course Organiser)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Registration is now closed\, if you would still like to register please send an email to oliverhooker@prstatistics.com and we will try and add you before the seminar start time.\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Free seminar \nThis is a free ~30 minute seminar including a Q and A session at the end for our up-coming course “Advances In Spatial Analysis Of Multivariate Ecological Data: Theory And Practice”. \nTime \n18:00 GMT \nSpeaker \nCourse Instructor Prof. Pierre Legendre \nAbout this course \nThe course will describe recent methods (concepts and R tools) that can be used to analyse spatial patterns in community ecology. The umbrella concept of the course is beta diversity\, which is the spatial variation of communities. These methods are applicable to all types of communities (bacteria\, plants\, animals) sampled along transects\, regular grids or irregularly distributed sites. The new methods\, collectively referred to as spatial eigen-function analysis\, are grounded into techniques commonly used by community ecologists\, which will be described first: simple ordination (PCA\, CA\, PCoA)\, multivariate regression and canonical analysis\, permutation tests. The choice of dissimilarities that are appropriate for community composition data will also be discussed. The focal question is to determine how much of the community variation (beta diversity) is due to environmental sorting and to community-based processes\, including neutral processes. Recently developed methods to partition beta diversity in different ways will be presented. Extensions will be made to temporal and space-time data. \n \n \n \n \n \n \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 email any course enquiries to oliverhooker@prstatistics.com URL:https://prstats.preprodw.com/course/advances-in-spatial-analysis-of-multivariate-ecological-data-theory-and-practice-mvsp05s/ LOCATION:Delivered remotely (Canada) CATEGORIES:All Live Courses,Free Seminars,Home Seminars ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/MVSP04R.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/London:20220323T170000 DTEND;TZID=Europe/London:20220323T173000 DTSTAMP:20260419T041247 CREATED:20220221T230711Z LAST-MODIFIED:20220512T151922Z UID:10000329-1648054800-1648056600@prstats.preprodw.com SUMMARY:FREE SEMINAR - Statistics For Biodiversity And Conservation (SFBC01S) DESCRIPTION:Oliver Hooker (Course Organiser)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nRegistration is now closed\, if you would still like to register please send an email to oliverhooker@prstatistics.com and we will try and add you before the seminar start time.\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \nFree seminar \n\n\nThis is a free ~30 minute seminar including a Q and A session at the end for our up-coming course “Statistics for Biodiversity and Conservation”. \n\n\nTime \n\n\nTBC \n\n\nSpeaker \n\n\nCourse Instructor Dr. Carl Smith and Dr. Mark Warren \n\n\nCourse description \nThe 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 \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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-sfbc01s/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom CATEGORIES:Free Seminars,Home Seminars 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:20220214 DTEND;VALUE=DATE:20220219 DTSTAMP:20260419T041247 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:Oliver Hooker (Course Organiser)\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 \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:20220118 DTEND;VALUE=DATE:20220121 DTSTAMP:20260419T041247 CREATED:20210228T165859Z LAST-MODIFIED:20220224T220357Z UID:10000340-1642464000-1642723199@prstats.preprodw.com SUMMARY:ONLINE COURSE - Introduction To Stan For Bayesian Data Analysis (ISBD01) This course will be delivered live DESCRIPTION:Oliver Hooker (Course Organiser)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, January 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 (note the later times to accommodate attendees from the Americas) – 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 Stan (https://mc-stan.org) is “a state-of-the-art platform for statistical modeling and high-performance statistical computation. Thousands of users rely on Stan for statistical modeling\, data analysis\, and prediction in the social\, biological\, and physical sciences\, engineering\, and business.” Stan is a powerful programming language for developing and fitting custom Bayesian statistical models. In this course\, we provide a general introduction to the Stan language\, and describe how to use it to develop and run Bayesian models. We begin by first covering the theory behind Stan\, which covers Bayesian inference\, Markov Chain Monte Carlo (MCMC) for sampling from probability distributions\, and the efficient Hamiltonian Monte Carlo (HMC) method that Stan implements. Next\, we learn how to write Stan models by creating simple Bayesian such as binomial models and models using normal distributions. In so doing\, the basics of the Stan language will be apparent. Although Stan can be used with multiple different type of statistical programs (Python\, Julia\, Matlab\, Stata)\, we will use Stan with R exclusively\, specifically using the rstan or cmdstanr packages. Using thesepackages\, we will can compile and sample from a HMC sampler for the Bayesian models we defined\, plot and summarize the results\, evaluate the models\, etc. We then cover some widely used and practically useful models including linear regression\, logistic regression\, multilevel and mixed effects models. We will end by covering some more complex models\, including probabilistic mixture models. \nTHIS IS ONE COURSE IN OUR R SERIES – LOOK OUT FOR COURSES WITH THE SAME COURSE IMAGE TO FIND MORE IN THIS SERIES \n \n \n \n \n \n Intended Audiences\n This course is aimed at anyone who is in interested in doing advanced Bayesian data analysis using Stan. Stan is a state of the art tool for advanced analysis across all academic scientific disciplines\, engineering\, and business\, and other sectors. \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 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 (Eastern Standard Time) between 12:00-17:00. \nAll sessions will be video recorded and made available to all attendees as soon as possible. \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. \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 from 5pm-9pm. 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 We assume familiarity with inferential statistics concepts like hypothesis testing and statistical significance\, and practical experience with linear regression\, logistic regression\, mixed effects models using R. \n \n \n \n \n \n Assumed computer background\n Some experience and familiarity with R is required. No prior experience with Stan itself is required. \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 \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\nTuesday 18th \nClasses from 17:00 to 21:00 \nTopic 1: Hamiltonian Monte Carlo for Bayesian inference. We begin by describing Bayesian inference\, whose objective is the calculation of a probability distribution over a high dimensional space\, namely the posterior distribution. In general\, this posterior distribution can not be described analytically\, and so to summarize or make predictions from the posterior distribution\, we must draw samples from it. For this\, we can use Markov Chain Monte Carlo (MCMC) methods including the Metropolis sampler\, sometimes known as random-walk Metropolis. Hamiltonian Monte Carlo (HMC)\, which Stan implements\, is ultimately an efficient version of the Metropolis sampler that does not involve random walk behaviour. In this introductory section of the course\, we will go through these major theoretical topics in sufficient detail to be able to understand how Stan works. \nTopic 2: Univariate models. To learn the Stan language and how to use it to develop Bayesian models\, we will start with simple models. In particular\, we will look at binomial models and models involving univariate normal distributions. The models will allow us to explore many of the major features of the Stan language\, including how to specify priors\, in conceptually easy examples. Here\, we will also learn how to use rstan and cmdstanr to compile the HMC sampler from the defined Stan model\, and draw samples from it. \nWednesday 19th \nClasses from 17:00 to 21:00 \nTopic 2: Univariate models continued \nTopic 3: Regression models. Having learned the basics of Stan using simple models\, we now turn to more practically useful examples including linear regression\, general linear models with categorical predictor variables\, logistic regression\, Poisson regression\, etc. All of these examples involve the use of similar programming features and specifications\, and so they are easily extensible to other regression models. \nThursday 20th \nClasses from 17:00 to 21:00 \nTopic 4: Multilevel and mixed effects models. As an extension of the regression models that we consider in the previous topic\, here we consider multilevel and mixed effects models. We primarily concentrate on linear mixed effects models\, and consider the different ways to specify these models in Stan. \nTopic 5: Because Stan is a programming language\, it essentially gives us the means to create any bespoke or custom statistical model\, and not just those that are widely used. In this final topic\, we will cover some more complex cases to illustrate it power. In particular\, we will cover probabilistic mixture models\, which are a type of latent variable model. \n\n  \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\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. URL:https://prstats.preprodw.com/course/introduction-to-stan-for-bayesian-data-analysis-isbd01/ LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom CATEGORIES:Live Online Courses ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/ISBD01R.png END:VEVENT END:VCALENDAR