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DTSTART;VALUE=DATE:20250616
DTEND;VALUE=DATE:20250621
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SUMMARY:ONLINE COURSE - Tidyverse for Ecologists (TIDY01) This course will be delivered live
DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, June 16th\, 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 \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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 – Ireland local 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 This course comprehensively introduces the Tidyverse and focuses on its use in data science projects. It is designed to give participants a strong foundation in R programming\, core Tidyverse packages\, and the Tidymodels framework. The course emphasises hands-on projects to apply learned concepts to real-world data analysis and modelling tasks applied to biology. By the end of the course\, participants should: Understand the fundamentals of R programming for data analysis. Be proficient in using core Tidyverse packages to clean\, transform\, and visualise data. Gain an introduction to basic machine learning concepts through the Tidymodels framework. Learn to preprocess\, build\, evaluate\, and interpret models using Tidymodels. Apply Tidyverse and Tidymodels tools to solve real-world problems through hands-on projects. \n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on data science-related projects.\nData scientists and applied researchers in public or private sectors who need to integrateadvanced R programming language into their project workflows.\nProfessionals looking to integrate tidyverse packages into their workflows or enhance theirunderstanding of R programming language.\nEcologists looking to understand the basic principles of advanced R programming language and implement them in their research.\n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Information\n Time zone – Ireland local time \nAvailability – 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 Introductory and Intermediate-level lectures interspersed with hands-on projects. The instructors will provide datasets\, but participants are welcome to bring their data. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. \nAll sessions will be video recorded and made available to all attendees as soon as possible. If 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. \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. \n \n \n \n \n \n Assumed quantitative knowledge\n No quantitative knowledge is required for this module. \n \n \n \n \n \n Assumed computer background\n Day one will cover the basics of R for the module. However\, some familiarity with any other programming language is welcome. \n \n \n \n \n \n Equipment and software requirements\n \nA computer with a working version of R or RStudio is required. R and RStudio are free and open-source software for PCs\, Macs\, and Linux computers. \nParticipants should be able to install additional software on their computers during the course (please ensure you have computer administration rights). \nAlthough not absolutely necessary\, a large monitor and a second screen could improve the learning experience. Participants are also encouraged to keep their webcams active to increase their interaction with the instructor and other students. \nDownload R \nDownload RStudio \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 \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 16th\n Day 1: A Short Course in R Basics (9:30 – 17:30) \nThis day provides participants with the foundational R skills required for working with Tidyverse andTidymodels. It is designed for beginners or those needing a refresher in R programming. \n Section 1 (R Essentials): This section focuses on R syntax\, variables\, data types\, conditionals (`if`\, `else`\, `elif`)\, loops (`for`\, `while`)\, and writing reusable code using functions. Section 2 (Data Structures and File Handling in R): This section emphasises understanding data structures (e.g.\, vectors\, data frames\, lists) and handling files by reading/writing data (e.g.\, CSVs) for manipulation and analysis. \n \n \n \n \n \n Tuesday 17th\n Day 2: Fundamentals of Tidyverse I (9:30 – 17:30) \nThis day introduces participants to the foundational concepts of Tidyverse packages and theirapplications to data science projects. \n Section 3 (Data Manipulation I): This section covers the basics of data manipulation using `dplyr` functions such as `filter()`\, `select()`\, `mutate()`\, `arrange()`\, and `summarise ()`. Participants will learn how to clean\, transform\, and prepare datasets for analysis. Section 4 (Data Visualisation I): This section introduces the principles of data visualisation using `ggplot2`. Participants will learn how to create basic plots such as scatterplots\, bar charts\, and line graphs while exploring the grammar of graphics. \n \n \n \n \n \n Wednesday 18th\n Day 3: Fundamentals of Tidyverse II (9:30 – 17:30) \nThis day builds on the foundations established in Day 2 and dives deeper into advanced datamanipulation and visualisation techniques. \n Section 5 (Data Manipulation II): This section extends the use of `dplyr` by introducing morecomplex operations such as joins\, grouping with `group_by()`\, and working with pipelines using`%>%`. Finally\, additional packages will be presented to enhance data manipulationprogramming. Section 6 (Data Visualisation II): Participants will explore advanced visualisation techniquesusing extensions of `ggplot2`\, such as creating animated plots with the `gganimate` package andinteractive visualisations with additional tools. \n \n \n \n \n \n Thursday 19th\n Day 4: Applying Tidyverse Fundamentals to Data Modelling (9:30 – 17:30) \nThis day introduces participants to machine learning concepts using core libraries for statistical modelling and deep learning. \n Section 7 (Introduction to regression): This section focuses on regression modelling usingTidymodels. Participants will learn to implement linear regression models\, evaluate modelperformance\, and interpret results. Section 8 (Introduction to Classification): This section introduces techniques such as supportvector machines and neural networks using Tidymodels. Participants will also explore methodsfor assessing the performance of classification models. \n \n \n \n \n \n Friday 20th\n Day 5: Data Science Workflow with Tidyverse (9:30 – 17:30) \nOn the final day\, participants will apply all their newly acquired skills to solve real-world problemsinspired by ecological datasets. \n Section 9 (The data science workflow): The workflow will be illustrated based on the corepackages introduced. The book "R for Data Science" will serve as a base literature for this day Section 10 (Hands-on project): Participants will work through a complete data science workflow\, including data cleaning\, transformation\, visualisation\, modelling\, and communication of results. \n \n \n \n \n \n \n Course Instructor\n \nDr. Gabriel Palma \nGabriel R. Palma obtained a B.Sc. in Biology from the University of São Paulo\, Brazil in 2021. He is currently a PhD researcher at the Hamilton Institute at Maynooth University\, Ireland\, funded by the Science Foundation Ireland’s Centre for Research Training in Foundations of Data Science. His research interests include statistical and mathematical modelling\, machine vision\, machine learning\, and applications to ecology and entomology. His personal webpage can be found here \nResearchGate\nGoogleScholar \n
URL:https://prstats.preprodw.com/course/tidyverse-for-ecologists-tidy01/
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/2024/07/Screenshot-2024-07-05-at-15.29.57.png
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20250225
DTEND;VALUE=DATE:20250228
DTSTAMP:20260418T191553
CREATED:20201010T135502Z
LAST-MODIFIED:20241120T124623Z
UID:10000328-1740441600-1740700799@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Introduction to generalised linear models using R and Rstudio (IGLM08) This course will be delivered live
DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, February 25th\, 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\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 – Ireland Local 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 COURSE DETAILS \nThis course provides a comprehensive practical and theoretical introduction to generalized linear models using R. Generalized linear models are generalizations of linear regression models for situations where the outcome variable is\, for example\, a binary\, or ordinal\, or count variable\, etc. The specific models we cover include binary\, binomial\, and categorical logistic regression\, Poisson and negative binomial regression for count variables\, as well as extensions for overdispersed and zero-inflated data. We begin by providing a brief overview of the normal general linear model. Understanding this model is vital for the proper understanding of how it is generalized in generalized linear models. Next\, we introduce the widely used binary logistic regression model\, which is is a regression model for when the outcome variable is binary. Next\, we cover the binomial logistic regression\, and the multinomial case\, which is for modelling outcomes variables that are polychotomous\, i.e.\, have more than two categorically distinct values. We will then cover Poisson regression\, which is widely used for modelling outcome variables that are counts (i.e the number of times something has happened). We then cover extensions to accommodate overdispersion\, starting with the quasi-likelihood approach\, then covering the negative binomial and beta-binomial models for counts and discrete proportions\, respectively. Finally\, we will cover zero-inflated Poisson and negative binomial models\, which are for count data with excessive numbers of zero observations. \n \n \n \n \n \n Intended Audiences\n This course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research\, and also widely throughout the public\, and private sector. \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Information\n Time zone – GMT+1 \nAvailability – TBC \nDuration – 3 x 1/2 days \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n 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. \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 Tuesday 25th\n Classes from 16:00 to 19:00 \nTopic 1: The general linear model. We begin by providing an overview of the normal\, as in normal distribution\, general linear model\, including using categorical predictor variables. Although this model is not the focus of the course\, it is the foundation on which generalized linear models are based and so must be understood to understand generalized linear models. \nTopic 2: Binary logistic regression. Our first generalized linear model is the binary logistic regression model\, for use when modelling binary outcome data. We will present the assumed theoretical model behind logistic regression\, implement it using R’s glm\, and then show how to interpret its results\, perform predictions\, and (nested) model comparisons. \nTopic 3: Binomial logistic regression. Here\, we show how the binary logistic regresion can be extended to deal with data on discrete proportions. We will also present alternative link functions to the logit\, such as the probit and complementary log-log links. \n \n \n \n \n \n Wednesday 26th\n Classes from 16:00 to 19:00 \nTopic 4: Categorical logistic regression. Categorical logistic regression\, also known as multinomial logistic regression\, is for modelling polychotomous data\, i.e. data taking more than two categorically distinct values. Like ordinal logistic regression\, categorical logistic regression is also based on an extension of the binary logistic regression case. \nTopic 5: Poisson regression. Poisson regression is a widely used technique for modelling count data\, i.e.\, data where the variable denotes the number of times an event has occurred. \n \n \n \n \n \n Thursday 27th\n Classes from 16:00 to 19:00 \nTopic 6: Overdispersion models. The quasi-likelihood approach for both the Poisson and binomial models. Negative binomial regression. The negative binomial model is\, like the Poisson regression model\, used for unbounded count data\, but it is less restrictive than Poisson regression\, specifically by dealing with overdispersed data. Beta-binomial regression. The beta-binomial model is an overdispersed alternative to the binomial. \nTopic 7: Zero inflated models. Zero inflated count data is where there are excessive numbers of zero counts that can be modelled using either a Poisson or negative binomial model. Zero inflated Poisson or negative binomial models are types of latent variable models. \n \n \n \n \n \n \n Course Instructor\n \nDr. Rafael De Andrade Moral \nRafael is an Associate Professor of Statistics at Maynooth University\, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo\, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology\, Wildlife Management\, Agriculture\, and Environmental Science. As director of the Theoretical and Statistical Ecology Group\, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy\, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here \nResearchGateGoogleScholarORCIDGitHub \n
URL:https://prstats.preprodw.com/course/introduction-to-generalised-linear-models-using-r-and-rstudio-iglm08/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/IGLM04R.png
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DTSTART;VALUE=DATE:20250210
DTEND;VALUE=DATE:20250215
DTSTAMP:20260418T191553
CREATED:20241114T114852Z
LAST-MODIFIED:20241114T144542Z
UID:10000467-1739145600-1739577599@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Machine Learning using Python (MLUP01) This course will be delivered live
DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, February 10th\, 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 \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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 – Ireland local 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 This course comprehensively introduces Machine Learning\, covering theoretical foundations and practical applications. It focuses on crucial machine learning techniques such as supervised and unsupervised learning algorithms\, using Python and popular libraries like Scikit-learn\, TensorFlow\, and Keras. The course emphasises hands-on projects to apply learned concepts to real-world ecological problems. By the end of the course\, participants should: \n\nUnderstand fundamental concepts in machine learning\, including supervised and unsupervised learning.\nBe able to preprocess data for machine learning tasks.\nUnderstand key algorithms for regression\, classification\, clustering\, and dimensionality reduction.\nGain proficiency in building neural networks and deep learning models.\nBe familiar with model selection techniques and hyperparameter tuning.\nHave confidence in deploying machine learning models in production environments.\nBe able to apply machine learning techniques to solve real-world problems through hands-on projects.\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on machine learning projects.\nData scientists and applied researchers in public or private sectors who need to implement machine learning solutions.\nProfessionals looking to integrate machine learning into their workflows or enhance their understanding of AI technologies.\nEcologists looking to understand the basic principles of Machine learning and implement them in their research.\n\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Information\n Time zone – Central Time Zone \nAvailability – 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 Introductory and Intermediate-level lectures interspersed with hands-on projects. The instructors will provide datasets\, but participants are welcome to bring their data. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. \nAll sessions will be video recorded and made available to all attendees as soon as possible. If 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. \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. \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical and mathematical concepts\, such as linear algebra. \n \n \n \n \n \n Assumed computer background\n Day one will cover the basics of Python for the module. However\, some familiarity with any other programming language is welcome. \n \n \n \n \n \n Equipment and software requirements\n A laptop computer with a working version of Python is required. Python is free and open-source software for PCs\, Macs\, and Linux computers.\nParticipants should be able to install additional software on their computers during the course (please ensure you have administration rights to your computer).\n\nAlthough not absolutely necessary\, a large monitor and a second screen could improve the learning experience. Participants are also encouraged to keep their webcams active to increase their interaction with the instructor and other students. \nhttps://www.python.org/downloads/\n \n \n \n \n \n \n \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 10th\n Day 1: A Short Course in Python Basics (9:30 – 17:30) \nThis day provides participants with the foundational Python skills required for machine learning tasks. This day is designed for beginners or those needing a refresher in Python programming. \n\nSection 1 (Python Essentials for Machine Learning): This section focuses on Python syntax\, variables\, data types\, conditionals (`if`\, `else`\, `elif`)\, loops (`for`\, `while`)\, and writing reusable code using functions.\nSection 2 (Data Structures and File Handling in Python): Focuses on lists\, dictionaries\, tuples\, sets\, and reading/writing files (e.g.\, CSVs) for data manipulation.\n\n \n \n \n \n \n Tuesday 11th\n Day 2: Fundamentals of Machine Learning (9:30 – 17:30) \nThis day focuses on the theoretical foundations of machine learning\, detailing the application of learning algorithms in preparation for the practical examples in Python. \n\nSection 3 (Introduction to Machine Learning): This section covers the definition of Machine learning\, types of Learning (Supervised\, Unsupervised\, Reinforcement\, Semi-Supervised)\, applications of Machine Learning and an overview of Python libraries for ML (NumPy\, scikit-learn)\nSection 4 (Fundamental learning algorithms): This section explores the available learning algorithms and focuses on their applications. We will also discuss the application of different algorithms with practical examples in Ecology.\n\n \n \n \n \n \n Wednesday 12th\n Day 3: Statistical Learning Theory (9:30 – 17:30) \nThis day focuses on the theoretical foundations of Statistical Learning Theory (SLT) and illustrates their practical implications. \n\nSection 5 (Important Definitions on SLT): In this section\, we will explore the concept of Statistical Learning Theory and its implications for classification tasks in supervised learning settings\, highlighting its importance for machine learning practitioners.\nSection 6 (Practical implications of the SLT): This section provides a detailed explanation of the practical consequences of statistical learning theory based on Vapniks’ findings and using Support Vector Machines as a helpful example in Python\n\n \n \n \n \n \n Thursday 13th\n Day 4: Classification boundaries and the power of Deep Neural networks (9:30 – 17:30) \nThis day introduces participants to the core libraries used in machine learning tasks. scikit-learn is used to implement machine learning algorithms\, and TensorFlow is used to build deep learning models. \n\nSection 7 (Classification with various learning algorithms): Offers a step-by-step guide to building learning algorithms using scikit-learn.\nSection 8 (Building Deep Learning Models with TensorFlow/Keras): Offers a step-by-step guide to building CNN models for image classification using TensorFlow/Keras.\n\n \n \n \n \n \n Friday 14th\n Day 5: The Machine Learning Pipeline (9:30 – 17:30) \nParticipants will learn about the end-to-end workflow of a typical machine learning project using ecological datasets as an illustration. \nSection 9 (Preprocessing data and selecting algorithms): This section focuses on preprocessing techniques in OpenCV before feeding images into TensorFlow models for training. An entomological example illustrating the Machine Learning Pipeline will be used. \nSection 10 (The Complete Machine Learning Pipeline: From Classification to Evaluating Learning): Covers the end-to-end machine learning workflow\, including using the data preprocessed data and creating scikit-learn pipelines to automate critical aspects of the workflow. \n \n \n \n \n \n \n Course Instructor\n \nDr. Gabriel Palma \nGabriel R. Palma obtained a B.Sc. in Biology from the University of São Paulo\, Brazil in 2021. He is currently a PhD researcher at the Hamilton Institute at Maynooth University\, Ireland\, funded by the Science Foundation Ireland’s Centre for Research Training in Foundations of Data Science. His research interests include statistical and mathematical modelling\, machine vision\, machine learning\, and applications to ecology and entomology. His personal webpage can be found here \nResearchGateGoogleScholar \n \n
URL:https://prstats.preprodw.com/course/machine-learning-using-python-mlup01/
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/2024/11/Screenshot-2024-11-13-at-14.55.58.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250203
DTEND;VALUE=DATE:20250208
DTSTAMP:20260418T191553
CREATED:20241113T143355Z
LAST-MODIFIED:20241114T143930Z
UID:10000466-1738540800-1738972799@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Machine Vision using Python (MVUP01) This course will be delivered live
DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, February 3rd\, 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 \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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 – Ireland local 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 Machine vision has produced many helpful image-processing techniques in several fields\, such as object detection\, classification\, and segmentation. Machine vision is an interdisciplinary discipline combining computer vision and machine learning methods\, mainly deep learning\, to solve vision problems. Common problems\, such as classification and localisation\, are typical examples that combine these research fields. These techniques have applications in many areas. Deep learning methods are commonly applied for image classification\, focusing on deep neural networks and Convolutional Neural Networks (CNNs)\, including concepts of transfer learning applied to image classification. This course introduces basic concepts of deep learning and machine vision applied to image classification using CNNs. To illustrate these methods\, a dataset of medically and forensically important flies is used. Other examples will also be used during the course to illustrate the applications of machine vision in ecology. \nBy the end of the course\, participants should: \n\nUnderstand the basic concepts behind the machine vision ecosystem in Python;\nUnderstand the machine vision pipeline workflow;\nUnderstand the application of standard Python packages such as OpenCV and Tensorflow;\nUnderstand the basic concepts behind Deep Neural Networks;\nUnderstand the basic concepts behind Convolutional Deep Neural Networks;\nUnderstand basic concepts behind Transfer learning;\nHave the confidence to implement basic Machine vision methods using Python;\nHave the confidence to combine basic computer vision and machine learning methods to perform vision tasks;\n\n \n \n \n \n \n Intended Audiences\n \nAcademics and post-graduate students working on projects related to machine vision\nApplied researchers and analysts in public\, private or third-sector organisations who need the reproducibility\, speed and flexibility of a programming language such as Python for machine vision;\nEcologists utilise Python to solve vision-related problems and look to update their knowledge in the machine vision area.\n\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Information\n Time zone – Central Time Zone \nAvailability – 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 Introductory and Intermediate-level lectures interspersed with hands-on projects. The instructors will provide datasets\, but participants are welcome to bring their data. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. \nAll sessions will be video recorded and made available to all attendees as soon as possible. If 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. \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. \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of statistical and mathematical concepts. Also\, a basic understanding of supervised learning. \n \n \n \n \n \n Assumed computer background\n Day one will cover the basics of Python for the module. However\, some familiarity with any other programming language is welcome \n \n \n \n \n \n Equipment and software requirements\n A laptop computer with a working version of Python is required. Python is free and open-source software for PCs\, Macs\, and Linux computers.\nParticipants should be able to install additional software on their computers during the course (please ensure you have administration rights to your computer).\n\nAlthough not absolutely necessary\, a large monitor and a second screen could improve the learning experience. Participants are also encouraged to keep their webcams active to increase their interaction with the instructor and other students. \nhttps://www.python.org/downloads/\n \n \n \n \n \n \n \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 3rd\n Day 1: A Short Course in Python Basics (9:30 – 17:30) \nThis day provides participants with the foundational Python skills required for machine vision tasks. This day is designed for beginners or those needing a refresher in Python programming. \n\nSection 1 (Python Essentials for Machine Vision): This section focuses on Python syntax\, variables\, data types\, conditionals (`if`\, `else`\, `elif`)\, loops (`for`\, `while`)\, and writing reusable code using functions.\nSection 2 (Data Structures and File Handling in Python): Focuses on lists\, dictionaries\, tuples\, sets\, and reading/writing files (e.g.\, CSVs) for data manipulation.\n\n \n \n \n \n \n Tuesday 4th\n Day 2: Fundamentals of Computer Vision (9:30 – 17:30) \nThis day focuses on the theoretical foundations of computer vision\, detailing the main aspects. \n\nSection 3 (Introduction to Computer Vision and Image Processing): This section covers the fundamental structure of an image\, basic image handling techniques\, and an introduction to computer graphics.\nSection 4 (Local Image Descriptors and Feature Mapping): This section explores local image descriptors\, such as the Harris Corner Detector\, and techniques for image-to-image mapping.\n\n \n \n \n \n \n Wednesday 5th\n Day 3: Fundamentals of Deep Learning (9:30 – 17:30) \nThis day focuses on the theoretical foundations of deep learning from Neural Networks to Convolutional Neural Networks (CNNs). \n\nSection 5 (Neural Networks: From Basics to Backpropagation): Introduces artificial neurons and explains how neural networks learn through backpropagation.\nSection 6 (Convolutional Neural Networks (CNNs) for Image Classification): Provides a detailed explanation of CNN architecture\, including convolution layers\, pooling layers\, and fully connected layers.\n\n \n \n \n \n \n Thursday 6th\n Day 4: Understanding the Machine Vision Ecosystem in Python (OpenCV & TensorFlow) (9:30 – 17:30) \nThis day introduces participants to the core libraries used in machine vision tasks. OpenCV is used for image processing\, and TensorFlow is used for building deep learning models. \n\nSection 7 (Building Deep Learning Models with TensorFlow/Keras): Offers a step-by-step guide to building CNN models for image classification using TensorFlow/Keras.\nSection 8 (Image Processing with OpenCV: Filters\, Edge Detection & Contours): Covers basic image manipulation techniques using OpenCV\, including resizing\, cropping\, applying filters (blurring/sharpening)\, edge detection (Canny)\, and contour detection.\n\n \n \n \n \n \n Friday 7th\n Day 5: The Machine Vision Pipeline (9:30 – 17:30) \nParticipants will learn about the end-to-end workflow of a typical machine vision project. \nSection 9 (Preprocessing Images for Deep Learning with OpenCV & TensorFlow): This section focuses on preprocessing techniques in OpenCV before feeding images into TensorFlow models for training. An entomological example illustrating the Machine Vision Pipeline will be used. \nSection 10 (The Complete Machine Vision Pipeline: From Image Capture to Classification): Covers the end-to-end machine vision workflow\, including image capture\, enhancement through preprocessing\, segmentation\, feature extraction\, and classification using machine learning classifiers. \n \n \n \n \n \n \n Course Instructor\n \nDr. Gabriel Palma \nGabriel R. Palma obtained a B.Sc. in Biology from the University of São Paulo\, Brazil in 2021. He is currently a PhD researcher at the Hamilton Institute at Maynooth University\, Ireland\, funded by the Science Foundation Ireland’s Centre for Research Training in Foundations of Data Science. His research interests include statistical and mathematical modelling\, machine vision\, machine learning\, and applications to ecology and entomology. His personal webpage can be found here \nResearchGateGoogleScholar \n
URL:https://prstats.preprodw.com/course/machine-vision-using-python-mvup01/
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/2024/11/Screenshot-2024-11-13-at-12.47.27.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250127
DTEND;VALUE=DATE:20250206
DTSTAMP:20260418T191553
CREATED:20230726T154721Z
LAST-MODIFIED:20240926T112209Z
UID:10000433-1737936000-1738799999@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Time Series Analysis and Forecasting using R and Rstudio (TSAF01) This course will be delivered live
DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, January 27th\, 2024\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCourse Program\nTIME ZONE – Central Time Zone – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.\n \n \n \n \n \n \n \n \n \n \n Course Details\n In this six-day course\, we provide a comprehensive practical and theoretical introduction to time series analysis and forecasting methods using R. Forecasting tools are useful in many areas\, such as finance\, meteorology\, ecology\, public policy\, and health. We start by introducing the concepts of time series and stationarity\, which will help us when studying ARIMA-type models. We will also cover autocorrelation functions and series decomposition methods. Then\, we will introduce benchmark forecasting methods\, namely the naïve (or random walk) method\, mean\, drift\, and seasonal naïve methods. After that\, we will present different exponential smoothing methods (simple\, Holt’s linear method\, and Holt-Winters seasonal method). We will then cover autoregressive integrated moving-average (or ARIMA) models\, with and without seasonality. We will also cover Generalized Additive Models (GAMs) and how they can be used to incorporate seasonality effects in the analysis of time series data. Finally\, we will cover Bayesian implementations of time series models and introduce extended models\, such as ARCH\, GARCH and stochastic volatility models\, as well as Brownian motion and Ornstein-Uhlenbeck processes. \n \n \n \n \n \n Intended Audiences\n This course is aimed at anyone who is interested in forecasting methods\,and using R for data science or statistics. R is widely used in all areas ofacademic scientific research\, and also widely throughout the public\, andprivate sector. \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Information\n Time zone – Central Time Zone \nAvailability – TBC \nDuration – 3 days \nContact hours – Approx. 14 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. The course will take place online using Zoom. On each day\, the live video broadcasts will occur during UK local time at: 6pm-9pm \nAll sessions will be video recorded and made available to all attendees as soon as possible. If 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. \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. \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of R and statistical concepts. Specifically\, linear regression models\, statistical significance\, and 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. \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 27th\n Classes from 12:00 to 16:00 (Central Time Zone) \nDAY 1 \nSection 1: Introductory concepts in time series analysis. White noise\, stationarity\, autocovariance and autocorrelation. \nSection 2: Useful plots in time series analysis. Time plots\, seasonal plots\, autocorrelation plots. Time series decomposition: additive and multiplicative using the fable package in R. \n \n \n \n \n \n Tuesday 28th\n Classes from 12:00 to 16:00 (Central Time Zone) \nDAY 2 \nSection 3: Time series decomposition: additive and multiplicative using the fable package in R. \nSection 4: Benchmark forecasting methods. The naïve\, mean\, drift\, and seasonal naïve methods. Cross-validation methods for time series analysis. \nTime series plots (Independant practical 1) please allow 3 hours to complete this before the next live session. This practical is not compulsory\, you can complete this after the course if you do not have time. \n \n \n \n \n \n Wednesday 29th\n Classes from 12:00 to 16:00 (Central Time Zone) \nDAY 3 \nSection 4 (‘ctd) \nSection 5: Exponential smoothing. Simple exponential smoothing\, Holt’s linear method\, Holt-Winters seasonalmethod\, and fable’s general ETS method. \nTime series decomposition and benchmark forecasting methods (Independant practical 2) please allow 3 hours to complete this before the next live session. This practical is not compulsory\, you can complete this after the course if you do not have time. \n \n \n \n \n \n Monday 3rd\n Classes from 12:00 to 16:00 (Central Time Zone) \nDAY 4 \nSection 6: Autoregressive (AR) and moving-average (MA) models. Unit root tests for stationarity. How to identity the order of an AR(p) or an MA(q) model using autocorrelation and partial autocorrelation plots. \nSection 7: Autoregressive integrated moving average (ARIMA) models and seasonal ARIMA models. Automatic order selection for a (seasonal) ARIMA model using fable. Linear regression with ARIMA errors. \nExponential smoothing (Independant practical 3) please allow 3 hours to complete this before the next live session. This practical is not compulsory\, you can complete this after the course if you do not have time. \n \n \n \n \n \n Tuesday 4th\n Classes from 12:00 to 16:00 (Central Time Zone) \nDAY 5 \nSection 8: Generalized Additive Models (GAMs). An introduction to semi-parametric regression using splines. Incorporating trends and seasonal components of a time series using a GAM. \nSection 9: An introduction to Bayesian modelling. Implementation of random walks\, autoregressive\, and moving average models using JAGS. \nARIMA models (Independant practical 4) please allow 3 hours to complete this before the next live session. This practical is not compulsory\, you can complete this after the course if you do not have time. \n \n \n \n \n \n Wednesday 5th\n Classes from 12:00 to 16:00 (Central Time Zone) \nDAY 6 \nSection 10: Modelling the variance as a time series process. Autoregressive conditional heteroskedasticity (ARCH) and generalized ARCH (GARCH) models. Stochastic volatility models. \nSection 11: Continuous time models. Brownian motion and Ornstein-Uhlenbeck processes. Fitting continuous time series models using JAGS. \nSection 12: Multivariate time series. Vector autoregression. Simple examples using JAGS. \nGAMs and Bayesian models (Independant practical 5) please allow 3 hours to complete this before the next live session. This practical is not compulsory\, you can complete this after the course if you do not have time. \n \n \n \n \n \n \n Course Instructor\n \nDr. Rafael De Andrade Moral \nRafael is an Associate Professor of Statistics at Maynooth University\, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo\, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology\, Wildlife Management\, Agriculture\, and Environmental Science. As director of the Theoretical and Statistical Ecology Group\, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy\, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here \nResearchGateGoogleScholarORCIDGitHub \n
URL:https://prstats.preprodw.com/course/time-series-analysis-and-forecasting-using-r-and-rstudio-tsaf01/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2022/02/MDAR-scaled.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20241112
DTEND;VALUE=DATE:20241122
DTSTAMP:20260418T191553
CREATED:20230726T162340Z
LAST-MODIFIED:20241114T125839Z
UID:10000434-1731369600-1732233599@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Introduction to Machine Learning using R and Rstudio (IMLR03) This course will be delivered live
DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nTuesday\, November 12th\, 2024\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCourse Program\nTIME ZONE – Central Time Zone – however all sessions will be recorded and made available allowing attendees from different time zones to follow. \nPlease email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.\n \n \n \n \n \n \n \n \n \n \n Course Details\n In this six-day course\, we provide a comprehensive practical and theoretical introduction to statistical machine learning using R. We start by introducing the concepts of supervised and unsupervised learning. We firstly explore unsupervised learning\, and introduce k-means andhierarchical clustering\, as well as principal components analysis. We then move to supervised learning methods\, and cover logistic regression and regularisation methods (such as ridge regression and the LASSO). After that\, we introduce the k-nearest neighbours method\, and classification and regression trees (CART). Finally\, we explore extensions to CART\, such as random forests and\, if time allows\, Bayesian additive regression trees (BART). \n \n \n \n \n \n Intended Audiences\n This course is aimed at anyone who is interested in statistical machine learning methods for clustering\, classification or prediction\, and using R for data science or statistics. R is widely used in all areas of academic scientific research\, and also widely throughout the public\, and private sector. \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Information\n Time zone – Central Time Zone \nAvailability – TBC \nDuration – 3 days \nContact hours – Approx. 24 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. The course will take place online using Zoom. On each day\, the live video broadcasts will occur during UK local time at: 6pm-9pm \nAll sessions will be video recorded and made available to all attendees as soon as possible. If 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. \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. \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. The course will take place online using Zoom. On each day\, the live video broadcasts will occur during UK local time at: 6pm-9pm \nAll sessions will be video recorded and made available to all attendees as soon as possible. If 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. \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. \n \n \n \n \n \n Assumed quantitative knowledge\n A basic understanding of R and statistical concepts. Specifically\, linear regression models\, statistical significance\, and 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. \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 Tuesday 12th\n Classes from 12:00 to 16:00 (Central Time Zone) \nDay 1 \nSection 1: Introductory concepts in statistical machine learning. Unsupervised vs. supervised learning. Useful plots in classification and clustering tasks. \nSection 2: Unsupervised learning methods: hierarchical clustering and the k-means method. \n \n \n \n \n \n Wednesday 13th\n Classes from 12:00 to 16:00 (Central Time Zone) \nDay 2 \nSection 3: Dimension reduction techniques and principal components analysis. \nSection 4: Regression and classification tasks. Supervised learning methods: linear and logistic regression. \n \n \n \n \n \n Thursday 14th\n Classes from 12:00 to 16:00 (Central Time Zone) \nDAY 3 \nSection 5: Tree-based methods. Classification and regression trees (CART)\, random forests. \nSection 6: Extensions to tree-based methods. Bayesian additive regression trees (BART). Combining tree-based methods with a parametric regression framework. \n \n \n \n \n \n Tuesday 19th\n Classes from 12:00 to 16:00 (Central Time Zone) \nDay 4 \nSection 7: Generalized additive models and cross-validation techniques. \n \n \n \n \n \n Wednesday 20th\n Classes from 12:00 to 16:00 (Central Time Zone) \nDay 5 \nSection 8: Tree-based methods. Classification and regression trees (CART)\, random forests. \nSection 9: Extensions to tree-based methods. Bayesian additive regression trees (BART). Boruta. \n \n \n \n \n \n Thursday 25th\n Classes from 12:00 to 16:00 (Central Time Zone) \nDay 6 \nSection 10: Neural networks. Fitting feedforward neural networks and multilayer perceptron using R. Selecting the number of neurons based on cross-validation and information criteria. Neural networks as statistical models. \nSection 11: Generalized additive models for location\, scale\, and shape (GAMLSS). Combining regression trees and neural networks within the GAMLSS regression framework. \n \n \n \n \n \n \n Course Instructor\n \nDr. Rafael De Andrade Moral \nRafael is an Associate Professor of Statistics at Maynooth University\, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo\, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology\, Wildlife Management\, Agriculture\, and Environmental Science. As director of the Theoretical and Statistical Ecology Group\, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy\, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here \nResearchGateGoogleScholarORCIDGitHub
URL:https://prstats.preprodw.com/course/introduction-to-machine-learning-using-r-and-rstudio-imlr03/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2023/07/Screenshot-2023-07-26-at-17.21.46.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240325
DTEND;VALUE=DATE:20240330
DTSTAMP:20260418T191554
CREATED:20240118T173343Z
LAST-MODIFIED:20240222T140338Z
UID:10000444-1711324800-1711756799@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Advancing in R (ADVR01) This course will be delivered live
DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, March 25th\, 2024\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCourse Program\nTIME ZONE – Ireland local 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 COURSE DETAILS \nThis course is designed to provide attendees with a comprehensive understanding of\nstatistical modelling and its applications in various fields\, such as ecology\, biology\, sociology\,\nagriculture\, and health. We cover all foundational aspects of modelling\, including all coding\naspects\, ranging from data wrangling\, visualisation and exploratory data analysis\, to\ngeneralized linear mixed models\, assessing goodness-of-fit and carrying out model\ncomparison. \nData wrangling\nFor data wrangling\, we focus on tools provided by R's tidyverse. Data wrangling is the art of\ntaking raw and messy data and formatting and cleaning it so that data analysis and\nvisualization may be performed on it. Done poorly\, it can be a time consuming\, laborious\,\nand error-prone. Fortunately\, the tools provided by R's tidyverse allow us to do data\nwrangling in a fast\, efficient\, and high-level manner\, which can have dramatic consequence\nfor ease and speed with which we analyse data. We start with how to read data of different\ntypes into R\, we then cover in detail all the dplyr tools such as select\, filter\, mutate\, and\nothers. Here\, we will also cover the pipe operator (%>%) to create data wrangling pipelines\nthat take raw messy data on the one end and return cleaned tidy data on the other. We\nthen cover how to perform descriptive or summary statistics on our data using dplyr’s\ngroup_by and summarise functions. We then turn to combining and merging data. Here\, we\nwill consider how to concatenate data frames\, including concatenating all data files in a\nfolder\, as well as cover the powerful SQL-like join operations that allow us to merge\ninformation in different data frames. The final topic we will consider is how to “pivot” data\nfrom a “wide” to “long” format and back using tidyr’s pivot_longer and pivot_wider\nfunctions. \nData visualisation\nFor visualisation\, we focus on the ggplot2 package. We begin by providing a brief overview\nof the general principles data visualization\, and an overview of the general principles behind\nggplot. We then proceed to cover the major types of plots for visualizing distributions of\nunivariate data: histograms\, density plots\, barplots\, and Tukey boxplots. In all of these\ncases\, we will consider how to visualize multiple distributions simultaneously on the same\nplot using different colours and "facet" plots. We then turn to the visualization of bivariate\ndata using scatterplots. Here\, we will explore how to apply linear and nonlinear smoothing\nfunctions to the data\, how to add marginal histograms to the scatterplot\, add labels to\npoints\, and scale each point by the value of a third variable. We then cover some additional\nplot types that are often related but not identical to those major types covered during the\nbeginning of the course: frequency polygons\, area plots\, line plots\, uncertainty plots\, violin\nplots\, and geospatial mapping. We then consider more fine grained control of the plot by\nchanging axis scales\, axis labels\, axis tick points\, colour palettes\, and ggplot "themes".\nFinally\, we consider how to make plots for presentations and publications. Here\, we will introduce how to insert plots into documents using RMarkdown\, and also how to create\nlabelled grids of subplots of the kind seen in many published articles. \nGeneralized linear models\nGeneralized linear models are generalizations of linear regression models for situations\nwhere the outcome variable is\, for example\, a binary\, or ordinal\, or count variable\, etc. The\nspecific models we cover include binary\, binomial\, and categorical logistic regression\,\nPoisson and negative binomial regression for count variables\, as well as extensions for\noverdispersed and zero-inflated data. We begin by providing a brief overview of the normal\ngeneral linear model. Understanding this model is vital for the proper understanding of how\nit is generalized in generalized linear models. Next\, we introduce the widely used binary\nlogistic regression model\, which is is a regression model for when the outcome variable is\nbinary. Next\, we cover the binomial logistic regression\, and the multinomial case\, which is\nfor modelling outcomes variables that are polychotomous\, i.e.\, have more than two\ncategorically distinct values. We will then cover Poisson regression\, which is widely used for\nmodelling outcome variables that are counts (i.e the number of times something has\nhappened). We then cover extensions to accommodate overdispersion\, starting with the\nquasi-likelihood approach\, then covering the negative binomial and beta-binomial models\nfor counts and discrete proportions\, respectively. Finally\, we will cover zero-inflated Poisson\nand negative binomial models\, which are for count data with excessive numbers of zero\nobservations. \nMixed models\nWe will focus primarily on multilevel linear models\, but also cover multilevel generalized\nlinear models. Likewise\, we will also describe Bayesian approaches to multilevel modelling.\nWe will begin by focusing on random effects multilevel models. These models make it clear\nhow multilevel models are in fact models of models. In addition\, random effects models\nserve as a solid basis for understanding mixed effects\, i.e. fixed and random effects\, models.\nIn this coverage of random effects\, we will also cover the important concepts of statistical\nshrinkage in the estimation of effects\, as well as intraclass correlation. We then proceed to\ncover linear mixed effects models\, particularly focusing on varying intercept and/or varying\nslopes regression models. We will then cover further aspects of linear mixed effects models\,\nincluding multilevel models for nested and crossed data data\, and group level predictor\nvariables. Towards the end of the course we also cover generalized linear mixed models\n(GLMMs)\, how to accommodate overdispersion through individual-level random effects\, as\nwell as Bayesian approaches to multilevel levels using the brms R package. \nModel selection and model simplification\nThroughout the course we consider the fundamental issue of how to measure model fit and\na model’s predictive performance\, and discuss a wide range of other major model fit\nmeasurement concepts like likelihood\, log likelihood\, deviance\, and residual sums of\nsquares. We thoroughly explore nested model comparison\, particularly in general and\ngeneralized linear models\, and their mixed effects counterparts. We discuss out-of-sample\ngeneralization\, and introduce leave-one-out cross-validation and the Akaike Information Criterion (AIC). We also cover general concepts and methods related to variable selection\,\nincluding stepwise regression\, ridge regression\, Lasso\, and elastic nets. Finally\, we turn to\nmodel averaging\, which may represent a preferable alternative to model selection.\n \n \n \n \n \n Intended Audiences\n This course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research\, and also widely throughout the public\, and private sector.\n \n \n \n \n \n Venue\n Delivered remotely\n \n \n \n \n \n Course Information\n Time zone – GMT+1 \nAvailability – TBC \nDuration – 3 x 1/2 days \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English\n \n \n \n \n \n Teaching Format\n 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:\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. \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. \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 25th\n Classes from 10:00-13:00 & 14:00-17:00 Ireland local time to 16:00 \nDay 1 \nTopic 1: Reading in data. We will begin by reading in data into R using tools such\nas readr and readxl. Almost all types of data can be read into R\, and here we will consider\nmany of the main types\, such as csv\, xlsx\, sav\, etc. Here\, we will also consider how to control\nhow data are parsed\, e.g.\, so that they are read as dates\, numbers\, strings\, etc. \nTopic 2: Wrangling with dplyr. We will next cover the very powerful dplyr R package. This\npackage supplies a number of so-called "verbs" — select\, rename\, slice\, filter\, mutate\, arrange\, etc. — each of which focuses on a key data manipulation tools\, such as selecting or changing variables. All of these verbs can be chained together using "pipes" (represented by %>%). Together\, these create powerful data wrangling pipelines that take raw data as input and return cleaned data as output. Here\, we will also learn about the key concept of "tidy data"\, which is roughly where each row of a data frame is an observation and each column is a variable. \nTopic 3: Summarizing data. The summarize and group_by tools in dplyr can be used with\ngreat effect to summarize data using descriptive statistics. \nTopic 4: Merging and joining data frames. There are multiple ways to combine data frames\,\nwith the simplest being "bind" operations\, which are effectively horizontal or vertical\nconcatenations. Much more powerful are the SQL-like "join" operations. Here\, we will\nconsider the inner_join\, left_join\, right_join\, full_join operations. In this section\, we will also\nconsider how to use purrr to read in and automatically merge large sets of files. \nTopic 5: Pivoting data. Sometimes we need to change data frames from "long" to "wide"\nformats. The R package tidyr provides the tools pivot_longer and pivot_wider for doing this.\n \n \n \n \n \n Tuesday 26th\n Classes from 10:00-13:00 & 14:00-17:00 Ireland local time to 16:00 \nDay 2 \nTopic 1: What is data visualization. Data visualization is a means to explore and understand\nour data and should be a major part of any data analysis. Here\, we briefly discuss why data\nvisualization is so important and what the major principles behind it are. \nTopic 2: Introducing ggplot. Though there are many options for visualization in R\, ggplot is\nsimply the best. Here\, we briefly introduce the major principles behind how ggplot works\,\nnamely how it is a layered grammar of graphics.\nTopic 3: Visualizing univariate data. Here\, we cover a set of major tools for visualizing\ndistributions over single variables: histograms\, density plots\, barplots\, Tukey boxplots. In each case\, we will explore how to plot multiple groups of data simultaneously using different colours and also using facet plots. \nTopic 4: Scatterplots. Scatterplots and their variants are used to visualize bivariate data.\nHere\, in addition to covering how to visualize multiple groups using colours and facets\, we\nwill also cover how to provide marginal plots on the scatterplots\, labels to points\, and how\nto obtain linear and nonlinear smoothing of the plots. \nTopic 5: More plot types. Having already covered the most widely used general purpose\nplots\, we now turn to cover a range of other major plot types: frequency polygons\, area\nplots\, line plots\, uncertainty plots\, violin plots\, and geospatial mapping. Each of these are\nimportant and widely used types of plots\, and knowing them will expand your repertoire. \nTopic 6: Fine control of plots. Thus far\, we will have mostly used the default for the plot\nstyles and layouts. Here\, we will introduce how to modify things like the limits and scales on\nthe axes\, the positions and nature of the axis ticks\, the colour palettes that are used\, and\nthe different types of ggplot themes that are available. \nTopic 7: Plots for publications and presentations. Thus far\, we have primarily focused on\ndata visualization as a means of interactively exploring data. Often\, however\, we also want\nto present our plots in\, for example\, published articles or in slide presentations. It is simple\nto save a plot in different file formats\, and then insert them into a document. However\, a\nmuch more efficient way of doing this is to use RMarkdown to run the R code and\nautomatically insert the resulting figure into a\, for example\, Word document\, pdf document\,\nhtml page\, etc. In addition\, here we will also cover how to make labelled grids of subplots\nlike those found in many scientific articles.\n \n \n \n \n \n Wednesday 27th\n Classes from 10:00-13:00 & 14:00-17:00 Ireland local time to 16:00 \nDay 3 \nTopic 1: The general linear model. We begin by providing an overview of the normal\, as in\nnormal distribution\, general linear model\, including using categorical predictor variables.\nAlthough this model is not the focus of the course\, it is the foundation on which generalized\nlinear models are based and so must be understood to understand generalized linear\nmodels. \nTopic 2: Binary logistic regression. Our first generalized linear model is the binary logistic\nregression model\, for use when modelling binary outcome data. We will present the\nassumed theoretical model behind logistic regression\, implement it using R’s glm\, and then\nshow how to interpret its results\, perform predictions\, and (nested) model comparisons. \nTopic 3: Binomial logistic regression. Here\, we show how the binary logistic regression can\nbe extended to deal with data on discrete proportions. We will also present alternative link\nfunctions to the logit\, such as the probit and complementary log-log links. \nTopic 4: Categorical logistic regression. Categorical logistic regression\, also known as multinomial logistic regression\, is for modelling polychotomous data\, i.e. data taking more than two categorically distinct values. Categorical logistic regression is based on an extension of the binary logistic regression case. \nTopic 5: Poisson regression. Poisson regression is a widely used technique for modelling\ncount data\, i.e.\, data where the variable denotes the number of times an event has occurred.\n \n \n \n \n \n Thursday 28th\n Classes from 10:00-13:00 & 14:00-17:00 Ireland local time to 16:00 \nTopic 1: Measuring model fit. Here\, the concept of conditional probability of the observed\ndata\, or of future data\, is of vital importance. This is intimately related\, though distinct\, to\nconcept of likelihood and the likelihood function\, which is in turn related to the concept of\nthe log likelihood or deviance of a model. Here\, we also show how these concepts are\nrelated to concepts of residual sums of squares\, root mean square error (rmse)\, and\ndeviance residuals. \nTopic 2: Nested model comparison. In this section\, we cover how to do nested model\ncomparison in general linear models\, generalized linear models\, and their mixed effects\n(multilevel) counterparts. First\, we precisely define what is meant by a nested model. Then\nwe show how nested model comparison can be accomplished in general linear models with\nF tests\, which we will also discuss in relation to R^2 and adjusted R^2. In generalized linear\nmodels\, we can accomplish nested model comparison using deviance based chi-square tests\nvia Wilks’s theorem. \nTopic 3: Overdispersion models. The quasi-likelihood approach for both the Poisson and\nbinomial models. Negative binomial regression. The negative binomial model is\, like the\nPoisson regression model\, used for unbounded count data\, but it is less restrictive than\nPoisson regression\, specifically by dealing with overdispersed data. Beta-binomial\nregression. The beta-binomial model is an overdispersed alternative to the binomial. \nTopic 4: Zero inflated models. Zero inflated count data is where there are excessive\nnumbers of zero counts that can be modelled using either a Poisson or negative binomial\nmodel. Zero inflated Poisson or negative binomial models are types of latent variable\nmodels. \nTopic 5: Random effects models. The defining feature of multilevel models is that they are\nmodels of models. We begin by using a binomial random effects model to illustrate this.\nSpecifically\, we show how multilevel models are models of the variability in models of\ndifferent clusters or groups of data. \nTopic 6: Normal random effects models. Normal\, as in normal distribution\, random effects\nmodels are the key to understanding the more general and widely used linear mixed effects\nmodels. Here\, we also cover the key concepts of statistical shrinkage and intraclass\ncorrelation.\n \n \n \n \n \n Friday 29th\n Classes from 10:00-13:00 & 14:00-17:00 Ireland local time to 16:00 \nDay 5 \nTopic 1: Out of sample predictive performance: cross validation and information criteria.\nHere\, we describe how to measure out of sample predictive performance\, which measures\nhow well a model can generalize to new data. This is arguably the gold-standard for\nevaluating any statistical models. A practical means to measure out of sample predictive\nperformance is cross-validation\, especially leave-one-out cross-validation. Leave-one-out\ncross-validation can\, in relatively simple models\, be approximated by Akaike Information\nCriterion (AIC)\, which can be exceptionally simple to calculate. We will discuss how to\ninterpret AIC values\, and describe other related information criteria\, some of which will be\nused in more detail in later sections. \nTopic 2: Linear mixed effects models. Next\, we turn to multilevel linear models\, also known\nas linear mixed effects models. We specifically deal with the cases of varying intercept\nand/or varying slope linear regression models. \nTopic 3: Multilevel models for nested data. Here\, we will consider multilevel linear models\nfor nested\, as in groups of groups\, data. As an example\, we will look at multilevel linear\nmodels applied to data from students within classes that are themselves within different\nschools\, and where we model the variability of effects across the classes and across the\nschools. \nTopic 4: Multilevel models for crossed data. In some multilevel models\, each observation\noccurs in multiple groups\, but these groups are not nested. For example\, animals may be\nmembers of different species and in different locations\, but the species are not subsets of\nlocations\, nor vice versa. These are known as crossed or multiclass data structures. \nTopic 5: Group level predictors. In some multilevel regression models\, predictor variable are\nsometimes associated with individuals\, and sometimes associated with their groups. In this\nsection\, we consider how to handle these two situations. \nTopic 6: Generalized linear mixed models (GLMMs). Here\, we extend the linear mixed model\nto the exponential family of distributions and showcase an example using the Poisson\nGLMM. We also cover how to accommodate overdispersion through individual-level\nrandom effects. \nTopic 7: Bayesian multilevel models. All of the models that we have considered can be\nhandled\, often more easily\, using Bayesian models. Here\, we provide an brief introduction\nto Bayesian models and how to perform examples of the models that we have considered\nusing Bayesian methods and the brms R package. \nTopic 8: Variable selection. Variable selection is a type of nested model comparison. It is\nalso one of the most widely used model selection methods\, and variable selection of some\nkind is almost always done routinely in all data analysis. In particular\, we cover stepwise\nregression (and its limitations)\, all subsets methods\, ridge regression\, Lasso\, and elastic nets.\nTopic 9: Model averaging. Rather than selecting one model from a set of candidates\, it is\narguably always better perform model averaging\, using all the candidates models\, weighted by the predictive performance. We show how to perform model average using information\ncriteria.\n \n \n \n \n \n \n Course Instructor\n \nDr. Rafael De Andrade Moral \nRafael is an Associate Professor of Statistics at Maynooth University\, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo\, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology\, Wildlife Management\, Agriculture\, and Environmental Science. As director of the Theoretical and Statistical Ecology Group\, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy\, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here \nResearchGate\nGoogleScholar\nORCID\nGitHub
URL:https://prstats.preprodw.com/course/advancing-in-r-advr01/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/jpeg:https://prstats.preprodw.com/wp-content/uploads/2024/01/nick-owuor-astro-nic-portraits-wDifg5xc9Z4-unsplash-scaled.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240312
DTEND;VALUE=DATE:20240315
DTSTAMP:20260418T191554
CREATED:20230915T125012Z
LAST-MODIFIED:20240118T153339Z
UID:10000437-1710201600-1710460799@prstats.preprodw.com
SUMMARY:CURSO ONLINE – Introdução a Modelos Mistos usando R e R Studio (IMMR08) Este curso será ministrado ao vivo
DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Data do Evento \nTerça-feira\, 12th Março\, 2024\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n FORMATO DO CURSO\nEste é um ‘CURSO AO VIVO’ – o instructor ministrará as aulas e treinará os participantes através de aulas práticas por meio de uma conexão por video; uma boa conexão com a internet é essencial. \nPROGRAMA\nFUSO HORÁRIO – Horário de Brasília – porém\, todas as sessões serão gravadas e disponibilizadas online\, permitindo que participantes de outros fusos horários também acompanhem. \nPor favor\, envie um email para oliverhooker@prstatistics.com para maiores detalhes\, ou para discutir como Podemos acomodá-lo(a). \n \n \n \n \n \n \n \n \n \n \n DETALHES DO CURSO\n \nEste curso fornece uma introdução teórica e prática a modelos mistos\, também conhecidos como modelos multi-nível ou hierárquicos. Nosso foco primário será em modelos lineares mistos\, porém também cobriremos modelos lineares generalizados mistos. Também descreveremos abordagens Bayesianas para modelos mistos. Começaremos com modelos de efeitos aleatórios. Esses modelos mostram\, com clareza\, como os modelos mistos são\, na verdade\, “modelos de modelos”. Também\, modelos de efeitos aleatórios servem como uma base sólida para auxiliar o entendimento de modelos mistos. Nós também trataremos de conceitos importantes relacionados a shrinkage\, ou “redução/encolhimento” dos efeitos aleatórios\, e correlação intraclasse. Então\, cobriremos modelos lineares mistos\, com foco particular em modelos de intercepto e/ou coeficientes angulares aleatórios. Depois\, cobriremos modelos mistos para dados com estrutura aninhada ou cruzada\, bem como preditores de nível de grupo. Então\, trataremos de modelos lineares generalizados mistos e como utilizar efeitos aleatórios a nível observacional para acomodar superdispersão. Por fim\, cobriremos uma breve introdução à abordagem Bayesiana por meio do pacote brms. \n\n \n \n \n \n \n PÚBLICO ALVO\n \nEste curso tem como público alvo qualquer pessoa que estiver interessada em utilizar R para ciência de dados ou estatística. R é amplamente utilizado em todas as áreas da pesquisa científica\, bem como nos setores público e privado. \n\n \n \n \n \n \n LOCAL\n Ministrado remotamente.\n \n \n \n \n \n NFORMAÇÃO DO CURSO\n Fuso horário – Horário de Brasília \nDisponibilidade – A definir \nDuração – 3 x 1/2 dias \nHoras de contato – Aprox. 12 horas \nCréditos – Equivalente a 1 crédito \nIdioma – Português\n \n \n \n \n \n FORMATO DE ENSINO\n Este curso será um workshop prático. Para cada tópico\, haverá uma apresentação estilo aula\, isto é\, utilizando slides ou lousa eletrônica\, para introduzir conceitos-chaves e teoria. Então\, apresentaremos como realizar as variadas análises estatísticas utilizando o R. Todo o código que o instrutor fornecerá durante as sessões será disponibilizado em um repositório público do GitHub após as sessões. \nNo início de cada dia\, nos certificaremos de que todos estão confortáveis com o uso do Zoom e discutiremos os procedimentos para fazer perguntas e postar comentários. \nEmbora não seja estritamente necessário\, utilizar um monitor grande (ou preferivelmente um segundo monitor) tornará a experiencia de aprendizado melhor\, porque você poderá ver meu R Studio e seu próprio R Studio simultaneamente. \nTodas as sessões serão gravadas e disponibilizadas imediatamente em um link protegido por senha. \nTodos os materiais\, como slides\, conjuntos de dados\, etc.\, serão compartilhados via GitHub. \n \n \n \n \n \n CONHECIMENTO QUANTITATIVO NECESSÁRIO\n \nUm entendimento básico de conceitos estatísticos chaves. Especificamente\, modelos de regressão linear\, significância estatística e testes de hipóteses. \n\n \n \n \n \n \n CONHECIMENTO COMPUTACIONAL NECESSÁRIO\n Familiaridade com o R. Importar/exportar dados\, manipular data frames\, ajustar modelos estatísticos básicos e gerar gráficos simples. \n \n \n \n \n \n REQUERIMENTOS DE EQUIPAMENTO E SOFTWARE\n \nUm computador com o R e R Studio instalados é necessário. R e R Studio são ambos gratuitos e disponíveis para PC\, Mac e Linux.Participantes devem poder instalar softwares adicionais em seus computadores durante o curso (por favor\, certifique-se de que você tem direitos de administrador em seu computador).Um monitor grande e uma segunda tela\, embora não seja absolutamente necessário\, melhorará a experiência de aprendizado. Participantes também são encorajados a manter suas câmeras ligadas para aumentar a interação entre o instrutor e os demais participantes. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer). \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students. \n\n\n\n\n\n\nFaça o download do R \n\n\nFaça o download do RStudio \n\n\nFaça o download do Zoom \n\n\n\n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n POR FAVOR\, LEIA – POLÍTICA DE CANCELAMENTO \nCancelamentos são aceitos até 28 dias antes da data de início do curso e estão sujeitos a uma taxa de cancelamento de 25%. Cancelamentos após esse período podem ser considerados\, contate oliverhooker@prstatistics.com. Falha em participar do curso resultará no custo completo do curso sendo cobrado. No evento improvável de o curso ser cancelado devido a imprevistos\, um reembolso completo das taxas do curso será creditado. \n \n \n \n \n \n Se você estiver incerto em relação à adequabilidade do curso\, por favor entre em contato por email para saber mais oliverhooker@prstatistics.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PROGRAMA DO CURSO\n \n \n \n \n \n \n \n \n \n \n Terça-feira 12th\n Aulas das 14:00 às 18:00 (Horário de Brasília) \nDIA 1 \n\nTópico 1: Modelos de efeitos aleatórios. A característica que define modelos mistos é o fato de que eles são “modelos de modelos”. Começaremos utilizando modelos binomiais de efeitos aleatórios para ilustrar esse conceito. Especificamente\, mostramos como modelos mistos são modelos da variabilidade em modelos de diferentes clusters\, ou grupos de dados. \n\n\nTópico 2: Modelo normal de efeitos aleatórios. Esses são modelos chaves para entender o modelo misto de uma maneira mais geral. Aqui\, cobriremos os conceitos estatísticos de shrinkage e de correlação intraclasse. \n\n \n \n \n \n \n Quarta-feira 13th\n Aulas das 14:00 às 18:00 (Horário de Brasília) \nTópico 3: Modelo linear misto. Agora\, cobriremos os modelos lineares mistos. Lidamos\, especificamente\, com os casos de interceptos e/ou coeficientes angulares aleatórios. \nTópico 4: Modelos mistos para dados com estrutura aninhada. Aqui\, consideramos modelos para dados com estrutura aninhada\, isto é\, grupos de grupos. Como um exemplo\, aplicaremos modelos mistos a dados de estudantes dentro de classes dentro de escolas\, onde modelamos a variabilidade dos efeitos entre classes e entre escolas. \nTópico 5: Modelos mistos para dados com estrutura cruzada. Em alguns modelos mistos\, cada observação ocorre em múltiplos grupos\, que não estão aninhados. Por exemplo\, animais podem ser membros de diferentes grupos taxonômicos e em diferentes locais\, mas os grupos taxonômicos não são subconjuntos dos locais\, ou vice-versa. \n \n \n \n \n \n Quinta-feira 14th\n Aulas das 14:00 às 18:00 (Horário de Brasília) \nTópico 6: Preditores a nível de grupo. Em alguns modelos mistos\, variáveis preditoras podem estar associadas a indivíduos ou a grupos. Nesta seção\, consideramos como lidar com essas duas situações. \nTópico 7: Modelos lineares generalizados mistos. Aqui\, estendemos o modelo linear misto para a família exponencial de distribuições e mostramos um exemplo usando o MLG misto Poisson. Também abordamos como acomodar superdispersão por meio de efeitos aleatórios a nível individual. \nTópico 8: Modelos mistos Bayesianos. Todos os modelos considerados podem ser ajustados utilizando a abordagem Bayesiana. Aqui\, fornecemos uma breve introdução a modelos Bayesianos e como ajustar os modelos mistos que consideramos durante o curso utilizando o pacote brms. \n \n \n \n \n \n \n Instrutor do curso\n \nDr. Rafael De Andrade Moral \nRafael é Professor Associado de Estatística na Maynooth University\, Irlanda. Bacharel em Biologia e Doutor em Estatística pela Universidade de São Paulo\, Rafael tem interesse em ensino e pesquisa em modelagem estatística aplicada a ecologia\, manejo da fauna silvestre\, agricultura e ciências ambientais. Como diretor do grupo de pesquisa em ecologia teórica e estatística\, Rafael reúne uma comunidade de pesquisadores que utilizam ferramentas matemáticas e estatísticas para melhor compreenderem o mundo natural. Como uma estratégia de ensino alternativa\, Rafael vem produzindo vídeos musicais e paródias para promover a Estatística nas mídias sociais e na sala de aula. Sua página pessoal pode ser encontrada aqui. \nResearchGateGoogleScholarORCIDGitHub
URL:https://prstats.preprodw.com/course/introducao-a-modelos-mistos-usando-r-e-r-studio-immr08/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2023/09/Screenshot-2023-09-15-at-13.59.26.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240220
DTEND;VALUE=DATE:20240223
DTSTAMP:20260418T191554
CREATED:20230829T210013Z
LAST-MODIFIED:20240118T152938Z
UID:10000436-1708387200-1708646399@prstats.preprodw.com
SUMMARY:CURSO ONLINE – Introdução a Modelos Lineares Generalizados usando R e R Studio (IGLM07) Este curso será ministrado ao vivo
DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Data do Evento \nTerça-feira\, 20th Fevereiro\, 2024\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n FORMATO DO CURSO\nEste é um ‘CURSO AO VIVO’ – o instructor ministrará as aulas e treinará os participantes através de aulas práticas por meio de uma conexão por video; uma boa conexão com a internet é essencial. \nPROGRAMA\nFUSO HORÁRIO – Horário de Brasília – porém\, todas as sessões serão gravadas e disponibilizadas online\, permitindo que participantes de outros fusos horários também acompanhem. \nPor favor\, envie um email para oliverhooker@prstatistics.com para maiores detalhes\, ou para discutir como Podemos acomodá-lo(a). \n \n \n \n \n \n \n \n \n \n \n DETALHES DO CURSO\n Este curso fornece uma introdução teórica e prática aos modelos lineares generalizados usando o R. Modelos lineares generalizados (MLGs) são generalizações de modelos de regressão linear para situações em que a variável resposta é\, por exemplo\, binária\, ou categórica\, ou de contagem\, etc. Os modelos específicos que apresentaremos incluem regressão logística binária\, binomial e categórica\, regressão Poisson e binomial negativa para variáveis de contagem. Também apresentaremos modelos de regressão de Poisson e binomial negativo inflacionados de zeros. Começaremos com uma breve recapitulação do modelo linear normal. Entender esse modelo é vital para um entendimento apropriado de como ele pode ser generalizado na teoria dos MLGs. Depois\, introduziremos o modelo de regressão logística binário amplamente utilizado\, que é um modelo de regressão para quando a variável resposta é binária. Depois\, apresentamos o caso da regressão logística binomial (duas categorias)\, e por fim multinomial\, para modelar respostas politômicas\, isto é\, que podem integrar mais de duas categorias. Depois apresentaremos a regressão Poisson\, que é amplamente utilizada para modelar variáveis respostas de contagem (isto é\, o número de vezes que algo aconteceu). Depois apresentaremos modelos de superdispersão\, que acomodam uma variabilidade maior do que a esperada pelos modelos de Poisson e binomial. Apresentaremos os modelos de quase-verossimilhança\, binomial negativo e beta-binomial\, para dados de contagens e proporções discretas\, respectivamente. Por fim\, apresentaremos modelos de Poisson e binomial negativo inflacionados de zeros\, para dados de contagem com um excesso de observações nulas.\n \n \n \n \n \n PÚBLICO ALVO\n Este curso tem como público alvo qualquer pessoa que estiver interessada em utilizar R para ciência de dados ou estatística. R é amplamente utilizado em todas as áreas da pesquisa científica\, bem como nos setores público e privado.\n \n \n \n \n \n LOCAL\n Ministrado remotamente.\n \n \n \n \n \n NFORMAÇÃO DO CURSO\n Fuso horário – Horário de Brasília \nDisponibilidade – A definir \nDuração – 3 x 1/2 dias \nHoras de contato – Aprox. 12 horas \nCréditos – Equivalente a 1 crédito \nIdioma – Português\n \n \n \n \n \n FORMATO DE ENSINO\n Este curso será um workshop prático. Para cada tópico\, haverá uma apresentação estilo aula\, isto é\, utilizando slides ou lousa eletrônica\, para introduzir conceitos-chaves e teoria. Então\, apresentaremos como realizar as variadas análises estatísticas utilizando o R. Todo o código que o instrutor fornecerá durante as sessões será disponibilizado em um repositório público do GitHub após as sessões. \nNo início de cada dia\, nos certificaremos de que todos estão confortáveis com o uso do Zoom e discutiremos os procedimentos para fazer perguntas e postar comentários. \nEmbora não seja estritamente necessário\, utilizar um monitor grande (ou preferivelmente um segundo monitor) tornará a experiencia de aprendizado melhor\, porque você poderá ver meu R Studio e seu próprio R Studio simultaneamente. \nTodas as sessões serão gravadas e disponibilizadas imediatamente em um link protegido por senha. \nTodos os materiais\, como slides\, conjuntos de dados\, etc.\, serão compartilhados via GitHub.\n \n \n \n \n \n CONHECIMENTO QUANTITATIVO NECESSÁRIO\n Um entendimento básico de conceitos estatísticos chaves. Especificamente\, modelos de regressão linear\, significância estatística e testes de hipóteses.\n \n \n \n \n \n CONHECIMENTO COMPUTACIONAL NECESSÁRIO\n Familiaridade com o R. Importar/exportar dados\, manipular data frames\, ajustar modelos estatísticos básicos e gerar gráficos simples.\n \n \n \n \n \n REQUERIMENTOS DE EQUIPAMENTO E SOFTWARE\n \nUm computador com o R e R Studio instalados é necessário. R e R Studio são ambos gratuitos e disponíveis para PC\, Mac e Linux.\nParticipantes devem poder instalar softwares adicionais em seus computadores durante o curso (por favor\, certifique-se de que você tem direitos de administrador em seu computador).\nUm monitor grande e uma segunda tela\, embora não seja absolutamente necessário\, melhorará a experiência de aprendizado. Participantes também são encorajados a manter suas câmeras ligadas para aumentar a interação entre o instrutor e os demais participantes. \n\n\n\n\n\nParticipants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer). \n\n\n\n\n\n\nA large monitor and a second screen\, although not absolutely necessary\, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students. \n\n\n\n\n\nDownload R \n\n\nDownload RStudio \n\n\nDownload Zoom \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n POR FAVOR\, LEIA – POLÍTICA DE CANCELAMENTO \nCancelamentos são aceitos até 28 dias antes da data de início do curso e estão sujeitos a uma taxa de cancelamento de 25%. Cancelamentos após esse período podem ser considerados\, contate oliverhooker@prstatistics.com. Falha em participar do curso resultará no custo completo do curso sendo cobrado. No evento improvável de o curso ser cancelado devido a imprevistos\, um reembolso completo das taxas do curso será creditado. \n \n \n \n \n \n Se você estiver incerto em relação à adequabilidade do curso\, por favor entre em contato por email para saber mais oliverhooker@prstatistics.com \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PROGRAMA DO CURSO\n \n \n \n \n \n \n \n \n \n \n Terça-feira 20th\n Aulas das 14:00 às 18:00 (Horário de Brasília) \nDIA 1 \nTópico 1: O modelo linear geral. Começamos com uma recapitulação do modelo normal\, incluindo uso de variáveis preditoras. Embora esse modelo não seja o foco do curso\, é o pilar central no qual os modelos lineares generalizados estão baseados e\, portanto\, deve ser compreendido para que haja entendimento dos modelos lineares generalizados (MLGs). \nTópico 2: Regressão logística binária. Nosso primeiro MLG é o de regressão logística binária (ou Bernoulli)\, a ser utilizado para modelar respostas binárias. Apresentaremos o modelo teórico por trás da regressão logística\, implementaremos utilizando a função glm do R e mostraremos como interpretar os resultados\, calcular predições e comparar modelos encaixados. \nTópico 3: Regressão logística binomial. Aqui\, mostramos como a regressão logística para variáveis binarias pode ser estendida para lidar com dados que consistem de proporções discretas. Também apresentaremos funções de ligação alternativas ao logito\, como a probito e complemento log-log. \n \n \n \n \n \n Quarta-feira 21st\n Aulas das 14:00 às 18:00 (Horário de Brasília) \nTópico 4: Regressão logística categórica. Também conhecida como regressão multinomial\, é utilizada pra modelar dados politômicos\, isto é\, dados que assumem mais do que duas categorias distintas. Assim como a regressão logística ordinal\, a regressão logística categórica também se baseia em uma extensão do caso de regressão logística binária. \nTópico 5: Regressão Poisson. A regressão Poisson é uma técnica amplamente utilizada para modelar dados de contagem\, isto é\, dados em que a variável resposta denota o número de vezes que um evento ocorreu. \n \n \n \n \n \n Quinta-feira 22nd\n Aulas das 14:00 às 18:00 (Horário de Brasília) \nTópico 6: Modelos de superdispersão. A abordagem de quase-verossimilhança para os modelos de Poisson e binomial. Regressão binomial negativa. O modelo binomial negativo é\, assim como o modelo de regressão Poisson\, utilizado para dados de contagem\, mas é menos restritivo do que o modelo de Poisson\, especificamente por lidar com dados superdispersos. Regressão beta-binomial. O modelo beta-binomial é uma alternativa ao modelo binomial que acomoda superdispersão. \nTópico 7: Modelos inflacionados de zeros. Dados de contagens inflacionados de zeros apresentam um número excessivo de contagens nulas quando modelados utilizando um modelo de Poisson on binomial negativo. Os modelos de Poisson ou binomial negativo inflacionados de zeros são exemplos de modelos de variáveis latentes. \n \n \n \n \n \n \n Instrutor do curso\n \nDr. Rafael De Andrade Moral \nRafael é Professor Associado de Estatística na Maynooth University\, Irlanda. Bacharel em Biologia e Doutor em Estatística pela Universidade de São Paulo\, Rafael tem interesse em ensino e pesquisa em modelagem estatística aplicada a ecologia\, manejo da fauna silvestre\, agricultura e ciências ambientais. Como diretor do grupo de pesquisa em ecologia teórica e estatística\, Rafael reúne uma comunidade de pesquisadores que utilizam ferramentas matemáticas e estatísticas para melhor compreenderem o mundo natural. Como uma estratégia de ensino alternativa\, Rafael vem produzindo vídeos musicais e paródias para promover a Estatística nas mídias sociais e na sala de aula. Sua página pessoal pode ser encontrada aqui. \nResearchGateGoogleScholarORCIDGitHub
URL:https://prstats.preprodw.com/course/curso-online-introducao-a-modelos-lineares-generalizados-usando-r-e-r-studio-iglm07/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom
CATEGORIES:All Live Courses,Home Courses,Live Online Courses
ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2022/02/IGLM04R.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240123
DTEND;VALUE=DATE:20240126
DTSTAMP:20260418T191554
CREATED:20240220T161152Z
LAST-MODIFIED:20240709T135913Z
UID:10000449-1705968000-1706227199@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Introduction to Machine Learning using R and Rstudio (IMLRPR)
DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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 In this three-day course\, we provide a comprehensive practical and theoretical introduction to statistical machine learning using R. We start by introducing the concepts of supervised and unsupervised learning. We firstly explore unsupervised learning\, and introduce k-means and\nhierarchical clustering\, as well as principal components analysis. We then move to supervised learning methods\, and cover logistic regression and regularisation methods (such as ridge regression and the LASSO). After that\, we introduce the k-nearest neighbours method\, and classification and regression trees (CART). Finally\, we explore extensions to CART\, such as random forests and\, if time allows\, Bayesian additive regression trees (BART).\n \n \n \n \n \n Intended Audiences\n This course is aimed at anyone who is interested in statistical machine learning methods for clustering\, classification or prediction\, and using R fordata science or statistics. R is widely used in all areas of academic scientific research\, and also widely throughout the public\, and private sector.\n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Information\n Time zone – NA \nAvailability – NA \nDuration – 3 1/2 days \nContact hours – Approx. 12 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n \nThis course will be largely practical\, hands-on\, and workshop based. For each topic\, there will first be some lecture style presentation\, i.e.\, using slides or blackboard\, to introduce and explain key concepts and theories. Then\, we will cover how to perform the various statistical analyses using R. \n\n\nAny code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. For the breaks between sessions\, and between days\, optional exercises will be provided. Solutions to these exercises and brief discussions of them will take place after each break. \n\n\nThe course will take place online using Zoom. On each day\, the live video broadcasts will occur during UK local time at: • 6pm-10pm \n\n\nAll sessions will be video recorded and made available to all attendees as soon as possible. \n\n\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\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 \n Assumed quantitative knowledge\n A basic understanding of R and statistical concepts. Specifically\, linear regression models\, statistical significance\, and 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. \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 \nSection 1: Introductory concepts in statistical machine learning. Unsupervised vs. supervised learning. Useful plots in classification and clustering tasks. Unsupervised learning methods: hierarchical clustering and the k-means method. \nSection 2: Dimension reduction techniques and principal components analysis. \n \n \n \n \n \n Day 2\n DAY 2 \nSection 3: Regression and classification tasks. Supervised learning methods: linear and logistic regression\, regularisation methods (ridge\, LASSO and elastic net). \nSection 4: More supervised learning methods: smoothing methods\, splines\, and generalized additive models. Cross-validation techniques. \n \n \n \n \n \n Day 3\n DAY 3 \nSection 5: Tree-based methods. Classification and regression trees (CART)\, random forests. \nSection 6: Extensions to tree-based methods. Bayesian additive regression trees (BART). Combining tree-based methods with a parametric regression framework. \n \n \n \n \n \n \n Course Instructor\n \nDr. Rafael De Andrade Moral \nRafael is an Associate Professor of Statistics at Maynooth University\, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo\, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology\, Wildlife Management\, Agriculture\, and Environmental Science. As director of the Theoretical and Statistical Ecology Group\, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy\, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here \nResearchGate\nGoogleScholar\nORCID\nGitHub
URL:https://prstats.preprodw.com/course/online-course-introduction-to-machine-learning-using-r-and-rstudio-imlrpr/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom
CATEGORIES:Previously Recorded Courses
ATTACH;FMTTYPE=image/png:https://prstats.preprodw.com/wp-content/uploads/2023/07/Screenshot-2023-07-26-at-17.21.46.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20220118
DTEND;VALUE=DATE:20220121
DTSTAMP:20260418T191554
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:Delivered remotely (United Kingdom)\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
BEGIN:VEVENT
DTSTART;VALUE=DATE:20210526
DTEND;VALUE=DATE:20210528
DTSTAMP:20260418T191554
CREATED:20210228T155419Z
LAST-MODIFIED:20220224T204912Z
UID:10000338-1621987200-1622159999@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Bayesian Approaches To Regression And Mixed Effects Models Using R and brms (BARM01)
DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nThursday\, May 26th\, 2021\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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 Bayesian methods are now increasingly widely used for data analysis based on linear and generalized linear models\,and multilevel and mixed effects models. The aim of this course is to provide a solid introduction to Bayesian approaches to these topics using R and the brms package. Ultimately\, in this course\, we aim to show how Bayesian methods provide a very powerful\, flexible\, and extensible approach to general statistical data analysis. We begin by covering Bayesian approaches to linear regression. We will compare and contrast\, in both practical and theoretical terms\, the Bayesian approach and classical approach to linear regression. This will allow us to easily identify the major similarities and major differences\, both in terms of concepts and practice\, between the Bayesian and classical approaches. We will then proceed to Bayesian approaches to generalized linear models\, including binary logistic regression\, ordinal logistic regression\, Poisson regression\, zero-inflated models\, etc. In this coverage\, we will see the very wide range of models to which Bayesian methods can be easily applied. Finally\, we will cover Bayesian approaches to multilevel and mixed effects models. Here again\, we will see how Bayesian methods allow us to easily extend traditionally used methods like linear and generalized linear mixed effects models. We will also see how Bayesian methods allow us to control model complexity and solve algorithmic problems (e.g. model convergence problems) that can plague classical approaches to multilevel and mixed effects models. Throughout this course\, we will be using\, via the brms package\, Markov Chain Monte Carlo (MCMC) methods. However\, full technical details of MCMC will will be described here\, but will be provided in subsequent Bayesian data analysis courses. \n \n \n \n \n \n Intended Audiences\n This course is aimed at anyone who is in interested in using Bayesian approaches to regression\, multilevel\, and mixedeffects models in any area of science\, including the social sciences\, life sciences\, physical sciences. No prior experienceor familiarity with Bayesian statistics is required. \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 during UK local time (GMT+0) at:• 12pm-2pm• 3pm-5pm• 6pm-8pm \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 We assume familiarity with inferential statistics concepts like hypothesis testing and statistical significance\, and some practical experience with linear regression\, logistic regression\, mixed effects models. \n \n \n \n \n \n Assumed computer background\n Some experience and familiarity with R is required. However\, although we will be using R extensively\, all the code that we use will be made available\, and so attendees will usually just need to copy and paste and add minor modifications to this 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. 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\nWednesday 26th \nClasses from 12:00 to 20:00 \nTopic 1: 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. \nTopic 2: 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 assumehomogeneity 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 ofthe 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. \nThursday 27th \nClasses from 12:00 to 20:00 \nTopic 3: Bayesian generalized linear models. Generalized linear models include models such as logistic regression\, including multinomial and ordinal logistic regression\, Poisson regression\, negative binomialregression\, 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 howpowerful 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. \nTopic 4: 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 willalso 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 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. \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Let’s connectLorem ipsum dolor sit amet\, consectetuer adipiscing elit.\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n General Info\n info@website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Twitter\n @website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Facebook\n website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Copyright PR Statistics 2022 | Privacy Policy | Disclaimer | Site Map
URL:https://prstats.preprodw.com/course/bayesian-approaches-to-regression-and-mixed-effects-models-using-r-and-brms-barm01/
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/BARM01R.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20210519
DTEND;VALUE=DATE:20210521
DTSTAMP:20260418T191554
CREATED:20210228T154358Z
LAST-MODIFIED:20220224T202438Z
UID:10000336-1621382400-1621555199@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Introduction / Fundamentals of Bayesian Data Analysis statistics using R (FBDA01)
DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nThursday\, May 19th\, 2021\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCourse Program\nTIME ZONE – UTC+2 – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n \n \n \n \n \n \n \n \n \n \n Course Details\n 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 teach 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 begin with a gentle introduction to all the fundamental principles and concepts of Bayesian methods: the likelihood function\, prior distributions\, posterior distributions\, high posterior density intervals\, posterior predictive distributions\, marginal likelihoods\, Bayesian model selection\, etc. We will do this using some simple probabilistic models that are easy to understand and easy to work with. We then proceed to more practically useful Bayesian analyses\, specifically general linear models. For these analyses\, we will use real world data sets\, and carry out the analysis using the brms package in R\, which is an excellent and powerful package for Bayesian analysis. In this coverage\, we will also provide a brief introduction to Markov Chain Monte Carlo methods\, although these will be described in more detail in subsequent Bayesian data analysis courses. \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 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 statisticsis required. \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Information\n Venue – Delivered remotely \nTime zone – GMT+0 \nAvailability – TBC \nDuration – 2 days \nContact hours – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n \n\n\nThis course will be largely practical\, hands-on\, and workshop based. For each topic\, there will first be some lecture style presentation\, i.e.\, using slides or blackboard\, to introduce and explain key concepts and theories. Then\, we will cover how to perform the various statistical analyses using R. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. For the breaks between sessions\, and between days\, optional exercises will be provided. Solutions to these exercises and brief discussions of them will take place after each break. \nThe course will take place online using Zoom. On each day\, the live video broadcasts will occur during UK local time (GMT+0) at:• 12pm-2pm• 3pm-5pm• 6pm-8pm \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. \nAssumed quantitative knowledge \nWe assume familiarity with inferential statistics concepts like hypothesis testing and statistical significance\, and some practical experience with commonly used methods like linear regression\, correlation\, or t-tests. Most or all of these concepts and methods are covered in a typical undergraduate statistics courses in any of the sciences and related fields. \nAssumed computer background \nR experience is desirable but not essential. Although we will be using R extensively\, all the code that we use will be made available\, and so attendees will just need to copy and paste and add minor modifications to this code. Attendees should install R and RStudio and some R packages on their own computers before the workshops\, and have some minimal familiarity with the R environment. \nEquipment and software requirements \nA computer with a working version of R or RStudio is required. R and RStudio are both available as free and opensource software for PCs\, Macs\, and Linux computers. In addition to R and RStudio\, some R packages\, particularly brms\, are required. Instructions on how to install R/RStudio and all required R packages will be provided before the course begins. \nUNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com \n\n\n\n \n \n \n \n \n Assumed quantitative knowledge\n Coming soon.. \n \n \n \n \n \n Assumed computer background\n Coming soon.. \n \n \n \n \n \n Equipment and software requirements\n Attendees will need to install/update R/RStudio and various additional R packages. \nThis can be done on Macs\, Windows\, and Linux. \nR – https://cran.r-project.org/ \nRStudio – https://www.rstudio.com/products/rstudio/download/ \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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\n\n\n\nWednesday 19th – Classes from 12:00 to 20:00 \nTopic 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 alternativeV 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. \nTopic 2: Introducing Bayes’ rule. Bayes’ rule can be described as a means to calculate the probability of causesfrom some known effects. As such\, it can be used as a means for performing statistical inference. In this sectionof the course\, we will work through some simple and intuitive calculations using Bayes’ rule. Ultimately\, all ofBayesian data analysis is based on an application of these methods to more complex statistical models\, and sounderstanding these simple cases of the application of Bayes’ rule can help provide a foundation for the morecomplex cases. \nTopic 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. \nThursday 20th – Classes from 12:00 to 20:00 \nTopic 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. \nTopic 2: Introducing Bayes’ rule. Bayes’ rule can be described as a means to calculate the probability of causesfrom some known effects. As such\, it can be used as a means for performing statistical inference. In this sectionof the course\, we will work through some simple and intuitive calculations using Bayes’ rule. Ultimately\, all ofBayesian data analysis is based on an application of these methods to more complex statistical models\, and sounderstanding these simple cases of the application of Bayes’ rule can help provide a foundation for the morecomplex cases. \nTopic 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 \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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Let’s connectLorem ipsum dolor sit amet\, consectetuer adipiscing elit.\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n General Info\n info@website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Twitter\n @website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Facebook\n website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Copyright PR Statistics 2022 | Privacy Policy | Disclaimer | Site Map
URL:https://prstats.preprodw.com/course/introduction-fundamentals-of-bayesian-data-analysis-statistics-using-r/
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/FBDA01R.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20210317
DTEND;VALUE=DATE:20210319
DTSTAMP:20260418T191554
CREATED:20201008T142659Z
LAST-MODIFIED:20220224T172018Z
UID:10000319-1615939200-1616111999@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Introduction to statistics using R and Rstudio (IRRS03)
DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nThursday\, May 26th\, 2021\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Course Format\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \nCourse Program\nTIME ZONE – UTC+2 – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n \n \n \n \n \n \n \n \n \n \n Course Details\n In this two day course\, we provide a comprehensive introduction to R and how it can be used for data science and statistics. We begin by providing a thorough introduction to RStudio\, which is the most popular and powerful interfaces for using R. We then introduce all the fundamentals of the R language and R environment: variables and assignment\, data structures\, operators\, functions\, scripts\, packages\, projects\, etc. We then provide an introduction to data processing and formatting (aka\, data wrangling)\, an introduction to data visualization\, an introduction to RMarkdown\, and introduce how to some of the most widely used statistical methods such as linear regression\, Anovas\, etc. From this course\, you will gain a comprehensive introduction to R\, which will serve as foundation for progressing further with R to any kind of data analysis\, data science\, or statistics. \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 interested in using R for data science or statistics. R is widely used in all areas of academic scientific research\, and also widely throughout the public\, and private sector. \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Information\n Time zone – GMT+0 \nAvailability – TBC \nDuration – 2 days \nContact hours – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \n \n \n \n \n \n Teaching Format\n \n\n\nThis course will be largely practical\, hands-on\, and workshop based. For each topic\, there will first be some lecture style presentation\, i.e.\, using slides or blackboard\, to introduce and explain key concepts and theories. Then\, we will cover how to perform the various statistical analyses using R. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. For the breaks between sessions\, and between days\, optional exercises will be provided. Solutions to these exercises and brief discussions of them will take place after each break. \nThe course will take place online using Zoom. On each day\, the live video broadcasts will occur during UK local time (GMT+0) at:• 12pm-2pm• 3pm-5pm• 6pm-8pm \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. \nAssumed quantitative knowledge \nWe will assume only a minimal amount of familiarity with some general statistical and mathematical concepts. These concepts will arise when we discuss statistics and data analysis. Anyone who has taken any undergraduate (Bachelor’s) level course on (applied) statistics can be assumed to have sufficient familiarity with these concepts. \nAssumed computer background \nNo prior experience with R or any other programming language is required. Of course\, any familiarity with any other programming will be helpful\, but is not required. \nEquipment and software requirements \nAttendees of the course will need to use a computer on which RStudio can be installed. This includes Mac\, Windows\, and Linux\, but not tablets or other mobile devices. Instructions on how to install and configure all the required software\, which is all free and open source\, will be provided before the start of the course. We will also provide time during the workshops to ensure that all software is installed and configured properly. \nUNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com \n\n\n\n \n \n \n \n \n Assumed quantitative knowledge\n Coming soon.. \n \n \n \n \n \n Assumed computer background\n Coming soon.. \n \n \n \n \n \n Equipment and software requirements\n Attendees will need to install/update R/RStudio and various additional R packages. \nThis can be done on Macs\, Windows\, and Linux. \nR – https://cran.r-project.org/ \nRStudio – https://www.rstudio.com/products/rstudio/download/ \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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\n\n\n\nWednesday 17th – Classes from 12:00 to 20:00 \nTopic 1: The What and Why of R. We’ll start by briefly explaining what R is\, what is used for\, and why is has become so popular. \nTopic 2: Guided tour of RStudio. RStudio is the most widely used interface to R. We will provide a tour of all its parts and features and how to use it effectively. \nTopic 3: First steps in R. Now\, we cover all the fundamentals of R and the R environment. These include variables and assignment\, data structures such as vectors\, data frames\, lists\, etc\, operations on data structures\, functions\, scripts\, installing and loading packages\, using RStudio projects\, reading in data\, etc. This topic will be detailed so that everyone obtains a solid grasp on these fundamentals\, which makes all subsequent learning much easier. \nThursday 18th – Classes from 12:00 to 20:00 \nTopic 4: Introducing wrangling. Data wrangling\, which is the art of cleaning and restructuring data is a big topic. Here\, we just provide an introduction (subsequent courses in this series will cover wrangling in depth). Here\, we will primarily focus on filtering\, slicing\, selecting\, renaming\, and mutating data frames. \nTopic 5: Data visualization. Data visualization is another big and important topics. Here\, we just provide an introduction\, specifically an introduction to ggplot (subsequent courses in this serious will cover visualization in depth). We’ll cover scatterplots\, boxplots\, histograms\, and their variants. \nTopic 6: RMarkdown. RMarkdown is a powerful tool for creating reproducible research reports\, as well as slides\, scientific website\, posters\, etc. In an RMarkdown document\, we mix R code and the narrative text of the report\, and the outputs of the R code\, including figures\, are included in the final document. \nTopic 7: Introduction to Statistics using R. There are many thousands of statistical methods built into R. Here\, we will simply provide an introduction to some of the most widely used methods. In particular\, we will cover linear regression\, Anova\, and some other simple test. The aim of this section is to get a sense of how statistical analysis is done in a R\, and how to perform some of the most widely used methods. \n\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.\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Let’s connectLorem ipsum dolor sit amet\, consectetuer adipiscing elit.\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n General Info\n info@website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Twitter\n @website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Facebook\n website.com\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Copyright PR Statistics 2022 | Privacy Policy | Disclaimer | Site Map
URL:https://prstats.preprodw.com/course/introduction-to-statistics-using-r-and-rstudio-irrs03/
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/IRRS03R.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20200504
DTEND;VALUE=DATE:20200509
DTSTAMP:20260418T191554
CREATED:20200326T212723Z
LAST-MODIFIED:20220225T000841Z
UID:10000303-1588550400-1588982399@prstats.preprodw.com
SUMMARY:ONLINE COURSE - Python for data science\, machine learning\, and scientific computing (PDMS02)
DESCRIPTION:Delivered remotely (United Kingdom)\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n Event Date \nMonday\, May 4th\, 2020\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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. This course provides both a general introduction to programming with Python and a comprehensive introduction to using Python for data science\, machine learning\, and scientific computing. The major topics that we will cover include the following: the fundamentals of general purpose programming in Python; using Jupyter notebooks as a reproducible interactive Python programming environment; numerical computing using numpy; data processing and manipulations using pandas; data visualization using matplotlib\, seaborn\, ggplot\, bokeh\, altair\, etc; symbolic mathematics using sympy; data science and machine learning using scikit-learn\, keras\, and tensorflow; Bayesian modelling using PyMC3 and PyStan; high performance computing with Cython\, Numba\, IPyParallel\, Dask. Overall\, this course aims to provide a solid introduction to Python generally as a programming language\, and to its principal tools for doing data science\, machine learning\, and scientific computing. (Note that this course will focus on Python 3 exclusively given that Python 2 has now reached it end of life). \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 how Python can be used for data science\, broadly defined. Python and Python based data science is applicable to academic research in all fields of science and engineering as well as data intensive industries and services such as finance\, pharmaceuticals\, healthcare\, IT\, and manufacturing. \n \n \n \n \n \n Venue\n Delivered remotely \n \n \n \n \n \n Course Details\n Availability – 15 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 be hands-on and workshop based. Throughout each day\, there will be some lecture style presentation\, i.e.\, using slides\, introducing and explaining key concepts. However\, even in these cases\, the topics being covered will include practical worked examples that will work through together. \n \n \n \n \n \n Assumed quantitative knowledge\n We will assume only a minimal amount of familiarity with some general statistical and mathematical concepts. These concepts will arise when we discuss numerical computing\, symbolic maths\, and statistics and machine learning. However\, expertise and proficiency with these concepts are not necessary. Anyone who has taken any undergraduate (Bachelor’s) level course on (applied) statistics or mathematics can be assumed to have sufficient familiarity with these concepts. \n \n \n \n \n \n Assumed computer background\n No 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 Equipment and software requirements\n \nAttendees of the course should bring a laptop computer with Python (version 3) and the Python packages that we will use (such as numpy\, pandas\, sympy\, etc) installed. All the required software is free and open source and is available on Windows\, MacOs\, and Linux. Instructions on how to install and configure all the software will be provided before the start of the course. We will also provide time during the workshops to ensure that all software is installed and configured properly. \n\n\nDownload Python \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n PLEASE READ – CANCELLATION POLICY \nCancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \n \n \n \n \n \n \nIf you are unsure about course suitability\, please get in touch by email to find out more oliverhooker@prstatistics.com \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n COURSE PROGRAMME\n\nMonday 4th \nClasses from 09:30 to 17:30 \nTopic 1: The What and Why of Python. In order to provide some general background and context\, we will describe Python where came from\, what its major design principles and intended use was originally\, and where and how it is now currently used. We will see that Python is now extremely widely used\, especially in powering the web\, in data science and machine learning\, and system level programming. Here\, we also compare and contrast Python and R\, given that both are extremely widely used in data science. \nTopic 2: 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 mention and briefly describe Jupyter notebooks\, which are widely used for scientific applications of Python\, and are an excellent tool for doing reproducible interactive work. We will cover Jupyter more extensively starting on Day 3. Also as part of this topic\, we will describe how to use virtual environments and package installers such as pip and conda. \nTopic 3: Introduction to Python: 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 4: Introduction to Python: Programming. 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 loops. We will then cover conditionals and functions. \nTuesday 5th \nClasses from 09:30 to 17:30 \nTopic 5: 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\, and also how to write our own modules and packages. \nTopic 6: 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”). \nTopic 7: 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. \nTopic 8: 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. \n• Topic 9: Other Python programming features. In this section\, we will cover some important features of Python not yet covered. These include exception handling\, list and dictionary comprehensions\, itertools\, advanced collection types including defaultdict\, anonymous functions\, decorators\, etc. \nWednesday 6th \nClasses from 09:30 to 17:30 \nTopic 10: Jupyter notebooks and Jupyterlab. Although we have already introduced Jupyter notebooks\, here we will explore them properly. Jupyter notebooks are reproducible and interactive computing environment that support numerous programming languages\, although Python remains the principal language used in Jupyter notebooks. Here\, we’ll explore their major features and how they can be shared easily using GitHub and Binder. \nTopic 11: 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 of 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. In this section\, we will also consider a set of other visualization libraries for Python. These include plotly\, bokeh\, and altair. \nTopic 12: 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 13: 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 package\, which provides linear and generalized linear models as well as many other widely used statistical models. We will also introduce the scikit-learn package\, which we will more widely use on Day 4\, and use it for regression and classification analysis. \nThursday 7th \nClasses from 09:30 to 17:30 \nTopic 14: Machine learning. Python is arguably the most widely used language for machine learning. In this section\, we will explore some of the major Python machine learning tools that are part of the scikit-learn package. This section continues our coverage of this package that began in Topic 12 on Day 3. Here\, we will cover machine learning tools such as support vector machines\, decision trees\, random forests\, k-means clustering\, dimensionality reduction\, model evaluation\, and cross-validation. \nTopic 15: Neural networks and deep learning. A popular subfield of machine learning involves the use of artificial neural networks and deep learning methods. In this section\, we will explore neural networks and deep learning using the keras library\, which is a high level interface to neural network and deep learning libraries such as Tensorflow\, Theano\, or the Microsoft Cognitive Toolkit (CNTK). Examples that we will consider here include image classification and other classification problems taken from\, for example\, the UCI Machine Learning Repository. \nFriday 8th \nClasses from 09:30 to 16:00 \nTopic 16: Bayesian models. Two probabilistic programming languages for Bayesian modelling in Python are PyMC3 and PyStan. PyMC3 is a Python native probabilistic programming language\, while PyStan is the Python interface to the Stan programming language\, which is also very widely used in R. Both PyMC3 and PyStan are extremely powerful tools and can implement arbitrary probabilistic models. Here\, we will not have time to explore either in depth\, but will be able to work through a number of nontrivial examples\, which will illustrate the general feature and usage of both languages. \nTopic 17: High performance Python. The final topic that we will consider in this course is high performance computing with Python. While many of the tools that we considered above extremely quickly because they interface with compiled code written in C/C++ or Fortran\, Python itself is a high level dynamically typed and interpreted programming language. As such\, native Python code does not execute as fast as compiled languages such as C/C++ or Fortran. However\, it is possible to achieve compiled language speeds in Python by compiling Python code. Here\, we will consider Cython and Numba\, both of which allow us achieve C/C++ speeds in Python with minimal extensions to our code. Also\, in this section\, we will consider parallelization in Python\, in particular using IPyParallel and Dask\, both of which allow easy parallel and distributed processing using Python. \n\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/python-for-data-science-machine-learning-and-scientific-computing-pdms02/
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/PDMS02.png
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