Mark Andrews

I am an associate professor in the Department of Psychology in Nottingham Trent University, where I teach statistics. I also teach training courses in advanced statistics and data science. I have a textbook on data science. My academic background is in computational cognitive science.

Upcoming training courses

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.

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 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 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.