index – Mark Andrews

I teach training courses, varying in length from 1 to 5 days, on data science and statistics. The topics I cover include Bayesian data analysis, multilevel modelling, nonlinear regression, structural equation modelling and path analysis, reproducible research, machine learning, deep learning, general data analysis and statistics using R, data science and scientific programming with Python.

Introduction to Bayesian Data Analysis using R

10-12 June, 2025

This three-day course introduces the principles and practice of Bayesian data analysis using R. It covers fundamental concepts of Bayesian inference and modelling, and demonstrates how to perform Bayesian data analysis in practice with R using the powerful brms package. Topics include Bayesian approaches to linear regression, generalized linear models, and multilevel and mixed effects models.

Introduction to Multilevel and Mixed Effects Models using R

This two-day course provides a theoretical and practical introduction to multilevel and mixed effects models, including linear and generalized linear variants. It covers how to understand, fit, and interpret these models in R, explore both nested and crossed data structures, incorporate group-level predictors, quantify explained variance, and perform power analyses.

Introduction to Generalized Linear Models using R

This two-day course introduces you to generalized linear models (GLMs) in R, moving beyond the standard linear model to handle binary, ordinal, categorical, and count-based outcomes. By exploring a variety of models —– from logistic and ordinal regressions to Poisson, negative binomial, and zero-inflated models — you’ll learn how to choose, implement, and interpret the right approach for your data.

Introduction to Statistics using R and RStudio

29-30 May, 2025

This two-day course offers a comprehensive introduction to R and RStudio for data science and statistical analysis in both academic and professional contexts. Participants will learn how to set up and efficiently work with RStudio, learn the fundamentals of the R language and environment, learn how to use R for data cleaning and data visualization, learn how to create reproducible reports with RMarkdown and Quarto, and how to conduct a a wide range of commonly used statistical analyses.

Nonlinear Regression using Generalized Additive Models

4-6 September, 2024

This course offers an in-depth introduction to nonlinear regression analysis using generalized additive models. Starting with simple extensions to the general and generalized linear models framework using polynomial regression, the course progresses to cover more complex concepts such as basis functions, spline and radial basis functions. The major focus is on generalized additive models (GAMs) and generalized additive mixed models (GAMMs), which encompass a wide range of topics including nonlinear spatial and temporal models and interaction models.

Introduction to Mixed Effects Regression

20-21 June, 2024

This two-day course covers random effects multilevel models, linear mixed effects models, and multilevel models for nested and crossed data, as well as group level predictor variables.

Introduction to Bayesian Data Analysis with R

12-14 June, 2024

This course provides a comprehensive introduction to Bayesian methods, both theoretically and practically, with a focus on data analysis using R. The course covers fundamental concepts of Bayesian inference and modelling, and demonstrates how to perform Bayesian data analysis in practice using R and the brms package. Topics include Bayesian approaches to linear regression, generalized linear models, and multilevel and mixed effects models. The course also includes practical applications of Stan based Markov Chain Monte Carlo (MCMC) methods.

Introduction to Multilevel (hierarchical, or mixed effects) Models

29-30 May, 2024

This two-day course provides a comprehensive practical and theoretical introduction to multilevel models, also known as hierarchical or mixed effects models. The focus is primarily on multilevel linear models, multilevel generalized linear models, and Bayesian approaches to multilevel modelling. The course covers random effects multilevel models, linear mixed effects models, and multilevel models for nested and crossed data, as well as group level predictor variables.

Introduction to Generalized Linear Models with R

22-23 May, 2024

This two-day course provides a comprehensive practical and theoretical introduction to generalized linear models using R. The course covers specific models including binary, binomial, ordinal, and categorical logistic regression, Poisson and negative binomial regression for count variables, and zero-inflated Poisson and negative binomial regression models.

Introduction to Data Visualization and Data Dashboards with R using ggplot and Shiny

This two-day course offers a comprehensive introduction to data visualization in R using ggplot. The course covers the general principles of data visualization, major types of plots for visualizing univariate and bivariate data, and provides fine-grained control of the plot by changing axis scales, labels, tick points, color palettes, and ggplot themes. The course also introduces how to insert plots into documents using RMarkdown and how to create labeled grids of subplots for presentations and publications.

Introduction to Statistics using R and RStudio

3-4 April, 2024

This two-day course provides a comprehensive introduction to R and RStudio for data science and statistics in academic or professional settings. It covers the fundamentals of R and the R environment, data wrangling, data visualization, RMarkdown, and an introduction to statistics using R.

Introduction to Bayesian Data Analysis with R

10-12 January, 2024

This course provides a comprehensive introduction to Bayesian methods, both theoretically and practically. It begins with the fundamental concepts of Bayesian inference and modelling, and then proceeds to Bayesian approaches to linear regression, generalized linear models, and multilevel and mixed effects models. The course also includes practical sessions on how to perform Bayesian data analysis in R using the brms package and Stan based Markov Chain Monte Carlo (MCMC) methods.

Introduction to Bayesian Data Analysis with R

21-23 June, 2023

This course provides a comprehensive introduction to Bayesian methods, both theoretically and practically, with a focus on data analysis using R. The course covers fundamental concepts of Bayesian inference and modelling, and demonstrates how to perform Bayesian data analysis in practice using R and the brms package. Topics include Bayesian approaches to linear regression, generalized linear models, and multilevel and mixed effects models. The course also includes practical applications of Stan based Markov Chain Monte Carlo (MCMC) methods.

Introduction to Multilevel (hierarchical, or mixed effects) Models

This two-day course provides a comprehensive practical and theoretical introduction to multilevel models, also known as hierarchical or mixed effects models. The focus is primarily on multilevel linear models, multilevel generalized linear models, and Bayesian approaches to multilevel modelling. The course covers random effects multilevel models, linear mixed effects models, and multilevel models for nested and crossed data, as well as group level predictor variables.

Introduction to Generalized Linear Models with R

24-25 May, 2023

This two-day course provides a comprehensive practical and theoretical introduction to generalized linear models using R. The course covers specific models including binary, binomial, ordinal, and categorical logistic regression, Poisson and negative binomial regression for count variables, and zero-inflated Poisson and negative binomial regression models.

Introduction to Data Visualization with R using ggplot

10-11 May, 2023

This two-day course offers a comprehensive introduction to data visualization in R using ggplot. The course covers the general principles of data visualization, major types of plots for visualizing univariate and bivariate data, and provides fine-grained control of the plot by changing axis scales, labels, tick points, color palettes, and ggplot themes. The course also introduces how to insert plots into documents using RMarkdown and how to create labeled grids of subplots for presentations and publications.

Introduction to Data Wrangling using R and Tidyverse

25-26 April, 2023

This two-day course provides a comprehensive practical introduction to data wrangling using R, focusing on tools provided by R’s tidyverse. The course is designed for anyone involved in real-world data analysis, where raw data is often messy and complex. It covers key topics such as reading data into R, data manipulation with dplyr, summarizing data, merging and joining data frames, and pivoting data using tidyr.

Introduction to Statistics using R and RStudio

11-12 April, 2023

This two-day course provides a comprehensive introduction to R and RStudio for data science and statistics in academic or professional settings. It covers the fundamentals of R and the R environment, data wrangling, data visualization, RMarkdown, and an introduction to statistics using R.

Social network analysis

5-6 December, 2022

This workshop provides a comprehensive introduction to the theory and application of social network analysis using R. It covers the basic principles of network science theory, particularly focusing on graph theory and theory of random networks. The course also includes practical sessions on manipulating, visualizing, and describing social network data using R tools, and statistical regression modelling of network data.

Bayesian Data Analysis

18-20 October, 2022

This course provides a comprehensive introduction to Bayesian methods, both theoretically and practically, with a focus on data analysis across various scientific research fields. The course starts with the fundamental concepts of Bayesian inference and Bayesian modelling, and then delves into practical Bayesian data analysis using R and the brms package. Topics covered include Bayesian approaches to linear regression, generalized linear models, and multilevel and mixed effects models, all taught through the use of real-world data-sets and problems.

Bayesian Data Analysis

18-22 July, 2022

This course provides a comprehensive introduction to Bayesian methods, both theoretically and practically. It covers the fundamental concepts of Bayesian inference and Bayesian modelling, and how these methods differ from classical statistics. The course also provides a hands-on introduction to Bayesian data analysis with real-world problems, using the brms package and Stan based Markov Chain Monte Carlo (MCMC) methods, which are considered the state-of-the-art approach to modern Bayesian data analysis.

Introduction to Machine Learning and Deep Learning using R

29-30 June, 2022

This two-day course offers an introduction to machine learning and deep learning using R. The course covers major machine learning applications, including binary and multiclass classification problems, decision trees, random forests, and unsupervised learning methods. It also delves into artificial neural networks and deep learning, using TensorFlow and Keras deep learning toolboxes. The course is designed to be hands-on and workshop-based, with examples and exercises to reinforce learning.

Model selection and model simplification

15-16 June, 2022

This two-day course provides a comprehensive understanding of statistical model building, evaluation, selection, comparison, simplification, and averaging. It delves into measuring model fit, predictive performance, nested model comparison, out-of-sample predictive performance, over-fitting, and various methods of variable selection. The course concludes with a focus on model averaging and Bayesian methods of model comparison.

Introduction to Stan for Bayesian Data Analysis

17-20 January, 2022

This course provides a comprehensive introduction to the Stan language for Bayesian modeling. It covers the theory behind Stan, including Bayesian inference and Markov Chain Monte Carlo (MCMC) methods. Participants will learn to write Stan models, apply them to practical examples such as linear and logistic regression, and explore more complex models like probabilistic mixture models.

Bayesian data analysis

10-14 January, 2022

This course provides a comprehensive introduction to Bayesian methods, both theoretically and practically. It covers the fundamental concepts of Bayesian inference and modelling, and practical application in R using the brms package. Topics include Bayesian approaches to linear regression, generalized linear models, and multilevel and mixed effects models.

Model selection and model simplification

24-25 November, 2021

This two-day course provides a comprehensive understanding of statistical model building, evaluation, selection, comparison, simplification, and averaging. The course covers key concepts such as measuring model fit, predictive performance, nested model comparison, over-fitting, cross-validation, and Bayesian methods of model comparison.

Introduction to Machine Learning and Deep Learning using R

17-18 November, 2021

This two-day course offers an introduction to machine learning and deep learning using R. The course covers major topics in machine learning including binary and multiclass classification, decision trees, random forests, and unsupervised learning methods. It also introduces artificial neural networks and deep learning using Torch in R.

Introduction to Multilevel (hierarchical, or mixed effects) Models

10-11 November, 2021

This two-day course provides a comprehensive introduction to multilevel models, focusing on multilevel linear models and Bayesian approaches. The course covers random effects models, linear mixed effects models, and multilevel models for nested and crossed data. It also includes practical sessions using R.

Introduction to Generalized Linear Models

3-4 November, 2021

This two-day course offers a comprehensive introduction to generalized linear models using R. The course covers specific models including binary, binomial, ordinal, and categorical logistic regression, Poisson and negative binomial regression for count variables, and zero-inflated Poisson and negative binomial regression models. The course is practical and workshop-based, with lecture-style presentations to introduce key concepts and theories.

Introduction to R and RStudio

October 20, 2021

This one-day course offers a comprehensive introduction to R and RStudio for data science and statistics. It covers the fundamentals of the R language and environment, data processing and visualization, and widely used statistical methods. The course serves as a foundation for further progression in data analysis, data science, or statistics using R.

Introduction to Python for Data Science

13-15 October, 2021

This course provides a comprehensive introduction to Python programming with a focus on data science applications. Major topics include general purpose programming in Python, numerical computing with numpy, data processing with pandas, and parallel processing. The course also covers the integration of Python into R Statistical software.

Introduction to Data Wrangling and Data Visualization using R

4-8 October, 2021

This course offers a practical introduction to data wrangling and data visualization using R. Participants will learn to use R’s tidyverse tools for data wrangling and ggplot for data visualization. The course covers reading, cleaning, reshaping, and merging data, as well as creating various types of plots for data visualization.

Introduction to Data Science using R

14-23 September, 2021

This four-day online course provides an introduction to advanced topics in statistics and data science using R. The course covers data wrangling, data visualization, processing and visualizing NetCDF satellite data, programming in R, reproducible data analysis with RMarkdown, and various linear models. The course is practical, hands-on, and workshop-based with minimal lecture-style presentations.

Introduction to Machine Learning and Deep Learning using R

9-10 June, 2021

This two-day course offers an introduction to machine learning and deep learning using R. It covers key topics such as binary and multiclass classification, decision trees, random forests, unsupervised learning methods, and deep learning using Torch in R.

Introduction to Bayesian Data Analysis

This seminar provides a comprehensive introduction to Bayesian methods, highlighting their conceptual and theoretical differences from classical statistical methods. The course offers an overview of the fundamental concepts of Bayesian inference and modelling, and practical application of Bayesian data analysis in R.

Bayesian Approaches to Regression and Mixed Effects Models using R and brms

26-27 May, 2021

This course provides an introduction to Bayesian methods for data analysis using R and the brms package. It covers Bayesian approaches to linear and generalized linear models, and multilevel and mixed effects models. The course aims to demonstrate the power, flexibility, and extensibility of Bayesian methods in statistical data analysis.

The Fundamentals of Bayesian Data Analysis

19-20 May, 2021

This course offers a comprehensive introduction to Bayesian methods, both theoretical and practical. It covers the fundamental concepts of Bayesian inference and modelling, and demonstrates how to conduct Bayesian data analysis in R. The course is designed for anyone interested in learning and applying Bayesian data analysis across various scientific fields.

Introduction to Machine Learning and Deep Learning using Python

12-13 May, 2021

This two-day course offers an introduction to machine learning and deep learning using Python. The course covers practical applications using the scikit-learn toolbox, including binary and multiclass classification problems, decision trees, random forests, and unsupervised learning methods. It also introduces artificial neural networks and deep learning using the PyTorch toolbox.

A Brief Introduction to Statistical Data Analysis with Python

This course provides a concise overview of data analysis and statistics in Python. It covers data processing with pandas, statistical analysis with statsmodels, and data visualization using matplotlib and seaborn.

Introduction to Python and Programming in Python

28-29 April, 2021

This course offers a comprehensive introduction to Python and its programming environment. It covers Python installation, setting up virtual environments, using package installers, and an overview of Python IDEs and Jupyter notebooks. The course also delves into Python programming, discussing data types, conditionals, iterations, functional programming, object-oriented programming, and modules.

Introduction to Data Wrangling using R

21-22 April, 2021

This two-day course provides a practical introduction to data wrangling using R, focusing on the tidyverse tools. The course covers reading data, using dplyr tools, summarizing data, merging data frames, and pivoting data. It is designed for anyone involved in real-world data analysis.

Model selection and model simplification

14-15 April, 2021

This two-day course provides a comprehensive understanding of statistical model building, evaluation, selection, comparison, simplification, and averaging. It delves into measuring model fit, predictive performance, nested model comparison, out-of-sample predictive performance, over-fitting, and various methods of variable selection. The course concludes with a focus on model averaging and Bayesian methods of model comparison.

Introduction to Data Visualization using R

7-8 April, 2021

This two-day course offers a comprehensive introduction to data visualization in R using ggplot. The course covers the principles of data visualization and various types of plots. It also provides hands-on experience with RStudio and includes fine-tuned control of plots and making plots for presentations and publications.

Introduction to Multilevel (hierarchical, or mixed effects) Models

31 March - 1 April, 2021

This two-day course offers a comprehensive introduction to multilevel models, focusing on multilevel linear models and multilevel generalized linear models. It also covers Bayesian approaches to multilevel modelling. The course is practical and hands-on, with a focus on using R for statistical analyses.

Introduction to Generalized Linear Models

24-25 March, 2021

This two-day course offers a comprehensive introduction to generalized linear models using R. The course covers various models including binary, binomial, ordinal, and categorical logistic regression, Poisson and negative binomial regression for count variables, and zero-inflated Poisson and negative binomial regression models. It is designed for anyone interested in advanced statistical modelling in academic research or the public and private sectors.

Introduction to statistics using R and RStudio

17-18 March, 2021

This two-day course offers a comprehensive introduction to R and RStudio for data science and statistics. It covers the fundamentals of the R language and environment, data processing and formatting, data visualization, RMarkdown, and widely used statistical methods. The course is designed to provide a solid foundation for further progression in data analysis, data science, or statistics using R.

Nonlinear Regression using Generalized Additive Models

17-16 December, 2020

This course offers an introduction to nonlinear regression analysis using generalized additive models. It covers polynomial regression, spline and radial basis function regression, and generalized additive models (GAMs) and generalized additive mixed models (GAMMs). The course is designed for anyone interested in learning and applying nonlinear regression methods.

Introduction to Machine Learning and Deep Learning using Python

9-10 December, 2020

This two-day course offers an introduction to machine learning and deep learning using Python. The course covers practical applications using the scikit-learn toolbox, including binary and multiclass classification problems, decision trees, random forests, and unsupervised learning methods. The course also introduces artificial neural networks and deep learning using the PyTorch toolbox.

Introduction to Scientific, Numerical, and Data Analysis Programming in Python

2-3 December, 2020

This course provides an introduction to numerical programming, data processing, and data analysis in Python. It covers the use of numpy, Pandas, statsmodels, and rpy2, along with major data visualization tools like matplotlib, seaborn, and ggplot. The course also cover other scientific Python tools for symbolic mathematics and parallel programming.

Introduction to Multilevel (hierarchical, or mixed effects) Models

25-26 November, 2020

This two-day course provides a comprehensive introduction to multilevel models, focusing primarily on multilevel linear models and multilevel generalized linear models. The course also covers Bayesian approaches to multilevel modelling and provides practical hands-on experience using R.

Introduction to Python and Programming in Python

18-19 November, 2020

This two-day course offers a comprehensive introduction to Python environment and programming. It covers setting up a Python computing environment, using Python package installers, and introduces Python IDEs and Jupyter notebooks. The course also delves into Python programming, discussing data types, conditionals, iterations, functional programming, object-oriented programming, and modules.

Introduction to Generalized Linear Models

11-12 November, 2020

This two-day course offers a comprehensive introduction to generalized linear models using R. We cover models for binary, binomial, ordinal, and categorical logistic regression, as well as Poisson and negative binomial regression for count variables. The course also includes zero-inflated Poisson and negative binomial regression models.

Using Git and GitHub for R based data analysis projects

3-4 November, 2020

This two-day course provides a comprehensive understanding of using Git and GitHub for managing R based data analysis projects. It starts with a conceptual overview of version control software, focusing on Git and GitHub. The course then delves into practical usage of Git commands and the powerful workflows enabled by Git branching and merging.

Introduction to statistics using R and RStudio

28-29 October, 2020

This two-day course provides a comprehensive introduction to R and RStudio for data science and statistics. The course covers the fundamentals of the R language and environment, data processing and formatting, data visualization, RMarkdown, and widely used statistical methods. The course aims to provide a solid foundation for further progression in any kind of data analysis, data science, or statistics using R.

Data wrangling and RMarkdown-based reproducible reports using R

14-15 September, 2020

This two-day course offers a practical introduction to data wrangling and RMarkdown-based reproducible reports using R. The course covers the use of dplyr tools for data wrangling and the creation of reproducible data analysis reports using RMarkdown, knitr, and related tools. Additional tools for reproducible research in R, such as Git, R packages, Docker, and Make/Drake, are also discussed.

Introduction to Data Wrangling using R

3-4 September, 2020

This two-day course offers a practical introduction to data wrangling using R, focusing on the tidyverse tools. The course covers reading data into R, using dplyr tools for data manipulation, performing descriptive statistics, merging data frames, and pivoting data formats. The aim is to equip participants with the skills to transform raw, messy data into a clean, tidy format for efficient data analysis.

Introduction to Data Visualization using R

20-21 August, 2020

This two-day course offers a comprehensive introduction to data visualization in R using ggplot. The course covers major types of plots, including histograms, density plots, barplots, scatterplots, and geospatial mapping. It also provides fine-grained control of plots and guides on creating plots for presentations and publications.

Introduction to Multilevel (hierarchical, or mixed effects) Models

6-7 August, 2020

This two-day course provides a comprehensive introduction to multilevel models, focusing on multilevel linear models and multilevel generalized linear models. The course also covers Bayesian approaches to multilevel modelling. The training is hands-on and workshop-based, with a strong emphasis on practical application using R.

Introduction to Generalized Linear Models

23-24 July, 2020

This two-day course offers a comprehensive introduction to generalized linear models using R. The course covers specific models including binary, binomial, ordinal, and categorical logistic regression, Poisson and negative binomial regression for count variables, and zero-inflated Poisson and negative binomial regression models. The course is practical and hands-on, with lecture-style presentations followed by statistical analyses using R.

Reproducible Data Science using RMarkdown, Git, R packages, Docker, Make & Drake, and other tools

29 June - 4 July, 2020

This course offers a comprehensive introduction to reproducible data analysis using various R-based and general computing tools. The focus is on creating an open and transparent workflow that can be easily reproduced by others. The tools covered include RMarkdown, Git & GitHub, R packages, Docker, Gnu Make, and Drake. The course is designed to equip participants with the skills to conduct and share reproducible data analysis.

Nonlinear regression using R: Generalized Linear Models, Generalized Additive Models, Basis function regression, Gaussian processes, and other methods.

25-29 May, 2020

This course offers an introduction to nonlinear regression analysis, focusing on general and generalized linear models, generalized additive models, spline and radial basis function regression, and Gaussian process regression. The course demonstrates how these statistical modelling frameworks can handle nonlinear relationships between predictor and outcome variables. It is designed for anyone interested in learning and applying nonlinear regression methods.

Python for data science, machine learning, and scientific computing

This course offers a comprehensive introduction to Python programming with a focus on its applications in data science, machine learning, and scientific computing. Participants will learn about Python’s principal tools for data processing, visualization, numerical computing, and high-performance computing. The course also covers the use of Jupyter notebooks for interactive programming and reproducibility.

Introduction to R and RStudio

January 8, 2020

This workshop offers a comprehensive introduction to R, a powerful tool for data analysis widely used in psychology. It is designed for beginners, requiring no prior experience with R, but assumes familiarity with statistics.

Python for data science, machine learning, and scientific computing

23-27 September, 2019

This workshop offers a comprehensive introduction to Python programming, with a focus on its applications in data science, machine learning, and scientific computing. Participants will learn to use Python’s principal tools for data manipulation, visualization, numerical computing, and machine learning. The course also covers the use of Jupyter notebooks for interactive programming and reproducibility.

Structural Equation Models, Path Analysis, Causal Modelling and Latent Variable Models Using R

16-20 September, 2019

This five-day workshop provides a comprehensive introduction to structural equation modelling, path analysis, causal modelling, mediation analysis, latent variable modelling, Bayesian networks, graphical models, and other related topics. The course begins with a thorough review of regression modelling, then moves on to path analysis and structural equation modelling. The course also covers latent variable models, particularly factor analysis and latent class models.

Nonlinear Regression and Generalized Additive Models

9-13 September, 2019

This workshop offers an introduction to nonlinear regression analysis, focusing on general and generalized linear models, generalized additive models, spline and radial basis function regression, and Gaussian process regression. The course demonstrates how these statistical modelling frameworks can be extended to handle nonlinear relationships between predictor and outcome variables. It also covers the use of basis functions, artificial neural networks, and Bayesian nonlinear regression methods.

Introduction to Bayesian Multilevel Regression using R and brms

September 3, 2019

This workshop provides an introduction to Bayesian multilevel regression modelling using the brms R package. It covers the basics of multilevel modelling, the Bayesian approach to the topic, and hands-on advice on how to use brms with real world examples. The course is suitable for anyone interested in multilevel regression modelling.

Data Analysis Using R

15-16 July, 2019

This two-day workshop provides a comprehensive introduction to R and statistical data analysis. The first day covers R fundamentals, data wrangling, and visualization. The second day covers statistical tests and linear models, including generalized linear models and multilevel modelling.

Doing open, transparent, and reproducible research with RMarkdown and Git

This workshop provides a comprehensive introduction to using RMarkdown and Git for open, transparent, and reproducible research. Participants will learn how to couple data, analysis code, and manuscript text from the earliest stages of data-analysis. The course also covers how to share RMarkdown documents with collaborators and the public using Git and Github.

Introduction to R and RStudio

This one-day workshop offers a comprehensive introduction to R and RStudio, aimed at beginners. The course covers R fundamentals, including RStudio overview, R commands, data structures, functions, scripts, and packages. It also includes data analysis and visualization using ggplot2, with a focus on linear regression and Anova models.

Bayesian Multilevel Modelling

25-29 March, 2019

This five-day workshop provides an introduction to Bayesian multilevel modelling using R, Stan, and its R-based brms interface. The course covers fundamental principles and concepts of Bayesian data analysis, multilevel models, and regression models. The final part of the course explores multilevel probabilistic mixture models, particularly valuable in the modelling of text data.

Introduction to Bayesian data analysis for social and behavioural sciences using R and Stan

3-7 December, 2018

This five-day workshop offers an introduction to Bayesian data analysis using R and Stan. The course covers fundamental principles of Bayesian data analysis, practical Bayesian analyses using general linear models, and advanced topics like Markov Chain Monte Carlo. Real world datasets are used for hands-on learning.

Bayesian Data Analysis

25-26 September, 2018

This two-day workshop provides an introduction to Bayesian data analysis, contrasting it with classical approaches. The first day covers the fundamental concepts of Bayesian statistical inference. The second day offers a theoretical and practical foundation for real-world Bayesian data analysis in psychology and social sciences, focusing on regression models and their applications using Stan and brms.

Introduction to R

September 19, 2018

This one-day workshop offers a comprehensive introduction to R, aimed at beginners. The course covers R fundamentals, including RStudio, R commands, data structures, functions, and data visualization using ggplot2. Participants will also learn how to perform statistical data analysis in R, focusing on linear regression and Anova models.

Bayesian Data Analysis

August 28, 2018

This workshop offers a hands-on introduction to Bayesian data analysis. It covers fundamental concepts of Bayesian statistical inference, explores major concepts and issues in Bayesian statistics, and covers Bayesian general and generalized linear regression models. The course aims to provide a solid foundation in the theory and practice of Bayesian data analysis, particularly for psychological research.

Bayesian Parameter Estimation

This workshop offers a hands-on introduction to Bayesian data analysis and parameter estimation. It covers fundamental concepts of Bayesian statistical inference, contrasting them with frequentist methods. The course also delves into Bayesian linear regression models and their application in psychological research.

Prior Exposure Bayesian Data Analysis Workshops: September, 2017

27-28 September, 2017

This two-day workshop covers advanced topics in Bayesian data analysis, focusing on multilevel models and probabilistic mixture models. The first day is dedicated to multilevel linear and generalized linear models using the mcmcglmm R package and JAGS. The second day delves into probabilistic mixture models, nonlinear regression, and the theoretical basis of Markov Chain Monte Carlo samplers, primarily using JAGS.

Bayesian Methods for Language Sciences

29-30 June, 2017

This is a 5-day course on Bayesian methods for language sciences, focusing on probabilistic language models and statistical data analysis of empirical data. The course is relevant to researchers in the language sciences, particularly those from Natural Language Processing, Linguistics or Psychology backgrounds.

Prior Exposure Bayesian Data Analysis Workshops: June, 2017

28-30 June, 2017

This three-day workshop introduces Bayesian data analysis and R programming. The course begins with a general introduction to R and data processing, followed by an overview of Bayesian data analysis and its differences from classical approaches. The final day covers the theoretical and practical details of Bayesian inference, with a focus on Bayesian linear models.

Prior Exposure Bayesian Data Analysis Workshops: April, 2017

5-7 April, 2017

This three-day workshop introduces Bayesian data analysis using R. The course begins with a general introduction to R and data processing, followed by an overview of Bayesian data analysis and its differences from classical approaches. The final day delves into the theoretical and practical details of Bayesian inference, with a focus on Bayesian linear models.

Introduction to Bayesian Data Analysis using R and Jags

12-13 December, 2016

This is a two-day workshop on Bayesian data analysis using R and JAGS. The course provides an introduction to Bayesian data analysis and R, with a focus on Bayesian linear and generalized linear regression.

Prior Exposure Bayesian Data Analysis Workshops: September, 2016

14-16 September, 2016

This three-day workshop covers advanced topics in Bayesian data analysis. The first day focuses on linear mixed effects modelling using R. The second day explores Bayesian approaches to multilevel models using the MCMCglmm R package and JAGS. The third day delves into Bayesian approaches to probabilistic mixture models and nonlinear regression, with a deeper look into the theoretical basis of MCMC samplers.

Prior Exposure Bayesian Data Analysis Workshops: June, 2016

15-17 June, 2016

This is a three-day workshop on Bayesian data analysis. The course begins with an introduction to R for data processing and analysis. It then delves into the principles of Bayesian data analysis and its differences from classical approaches. The final day focuses on the theoretical and practical aspects of Bayesian inference, with a special emphasis on Bayesian linear models.

Prior Exposure Bayesian Data Analysis Workshops: April, 2016

30 March - 1 April, 2016

This is a three-day workshop that introduces Bayesian data analysis. The course covers the use of R for data processing and analysis, the basics of Bayesian data analysis, and the theoretical and practical aspects of Bayesian inference, with a focus on Bayesian linear models.

Prior Exposure Bayesian Data Analysis Workshops: September, 2015

22-23 September, 2015

This two-day workshop covers advanced topics in Bayesian data analysis, including multilevel models and probabilistic mixture models. The first day focuses on multilevel linear and generalized linear models using the MCMCglmm R package and JAGS. The second day delves into probabilistic mixture models, nonlinear regression, and the theoretical basis of MCMC samplers, primarily using JAGS.

Prior Exposure Bayesian Data Analysis Workshops: April, 2015

31 March - 1 April, 2015

This is a two-day workshop on Bayesian data analysis. The course provides a comprehensive introduction to Bayesian data analysis and its differences from classical approaches. The second day focuses on the theoretical and practical aspects of Bayesian inference, with a special emphasis on Bayesian linear models.

An introduction to the Python programming language for beginners

September 2, 2014

This workshop aims to introduce Python programming for cognitive psychology research. Over the past decade, Python has developed into a scientific and numerical computing environment, comparable to Matlab, R, and Mathematica. This tutorial will highlight Python’s potential applications and advantages in cognitive psychology research.

Python for Cognitive Science

This workshop introduces Python programming for cognitive science research. Over the past decade, Python’s scientific and numerical libraries have developed to rival products like Matlab. The tutorial advocates Python as the principal programming language in cognitive science research.