Delivered remotely (United Kingdom)

Machine Learning for Ecological Time Series is an applied R course teaching ecologists how to analyse, model, and predict ecological time series data.

Learn to analyse ecological field data with detection error using R. Work with point counts, ARU data, N-mixture models, distance sampling and time-removal methods.

Learn deep learning in R using the torch ecosystem. Build MLPs, CNNs and transformer models through hands-on coding and gain practical skills for real research workflows.

Interactive Data Applications with Shiny is a practical R Shiny course for researchers focused on building, customising, and deploying interactive web applications from data analyses.

Learn Bayesian modelling with the R-INLA package. Build, fit, and interpret INLA models, define priors and latent effects, and apply INLA to real data in a five day course.

Bayesian Statistical Modelling with Stan and brms is an advanced R course for researchers covering Bayesian model building, diagnostics, and interpretation using Stan and brms.

Machine Learning for Time Series is a practical Python course teaching how to model, analyse, and forecast time series data using machine learning methods.

Learn spatial multiplexed proteomics data analysis with CODEX, CycIF, and MACSIMA. Master image processing, segmentation, phenotyping, and spatial analysis in R and Python.