Beginner Python course for biologists. Learn file handling, loops, and bioinformatics-focused coding in Python.

Master Bayesian multilevel models in R with brms. Learn GLMs, priors, spatial/temporal autocorrelation, and species distribution modelling.

Learn Python for data science and statistical computing. Build skills in NumPy, Pandas and visualisation across two days of hands-on training for researchers and analysts.

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

Deep learning course using Python and PyTorch. Learn neural networks, CNNs and transformers through hands-on coding and real data across two intensive training days.

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.

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.

Take your Python skills further. Learn OOP, testing, and optimisation for complex bioinformatics tasks.

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 to analyse animal movement data using spatial methods, home range estimation, interaction metrics and resource or step selection models through hands-on training in R.