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

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 mechanistic species distribution and ecological niche modelling with NicheMapR in R. Hands-on live online course with microclimate modelling.

Species Distribution and Ecological Niche Modelling is an applied R course teaching standard workflows for building, evaluating, and interpreting SDMs.

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

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 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.

Species Distribution and Ecological Niche Modelling is an applied R course teaching standard workflows for building, evaluating, and interpreting SDMs.

Stable Isotope Mixing Models in R is an applied course teaching ecologists how to use SIBER, SIAR, and MixSIAR for dietary analysis.

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.

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.

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 ENM and SDM modelling in R. Apply tools like Maxent and Biomod2 to predict species distributions and environmental niches.

Visualise spatial data in R using the tmap package. Learn to create static and interactive maps, customise layouts, and publish high-quality visualisations.

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

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

Turn spatial data into clear, publication-quality maps using advanced cartography in R.

Learn spatial ecological data visualisation in R. Master mapping, remote sensing, publication-quality graphics, accessible colour scales, and reproducible workflows.