Regression analysis is a widely used quantitative method applicable across many sectors. This session covers the theory and application of linear and generalized linear regression models, model validation and diagnostics, Poisson regression for count data, and best practices for communicating results, all with hands-on exercises in R.

Uncertainty visualisation is essential throughout data analysis, from exploring variability to communicating estimate distributions. The session will introduce the concept of uncertainty, general techniques for visualising it, and creative methods for representing uncertainty in spatial data using R.

This session is designed for R users interested in turning their standalone code into shareable R packages but have not yet ventured into package development. Key topics include structuring R packages, managing dependencies with usethis and devtools, documenting code with roxygen2, testing with testthat, sharing packages using Git and GitHub, and an introduction to advanced steps like continuous integration, R Universe deployment, and website creation with pkgdown.

Many organisations increasingly rely on time series analytics to understand and forecast changes in their data. This session covers using the tidyverse for time series data manipulation, visualising and identifying common patterns with ggplot2, applying statistical models with fable for forecasting, and evaluating forecasting performance to ensure reliable results.

This session provides an introduction to efficiency and productivity analysis methods for researchers and practitioners. Key topics include the theoretical foundations, assumptions, use cases, and differences between Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA), as well as hands-on examples in Python covering model specification, estimation, and interpretation.

Recent advances have made it easier to integrate fast C++ code with the convenience of R for data analysis. Key topics in this session include working in RStudio, ensuring object compatibility between R and C++, using basic algorithmic structures and functional programming, extending R packages with C++ code, and applying these methods through hands-on exercises in linear algebra, maximum likelihood estimation, and Gibbs sampling.

This workshop introduces Quarto, an open-source tool for creating reproducible academic documents and presentations. Key session topics include writing reports and journal articles with templates, building code-integrated slide decks, managing bibliographies, embedding code and figures, formatting tables and equations, and generating outputs in formats like HTML and PDF.

Effective figure design is crucial for clear scientific communication but is often neglected in academic training. This session covers core graphic design principles, cognitive load, visual hierarchies, common communication pitfalls, and a practical process for building and improving scientific figures.