Comprehensive on-demand course in data wrangling, visualisation, GLMs, mixed models, and model selection using R. Ideal for researchers in ecology, biology, and social sciences.

Comprehensive on-demand GLM course for ecologists using R. Covers logistic, Poisson, zero-inflated, and mixed models with hands-on examples.

On-demand introduction to linear and generalised mixed models in R. Covers multilevel modelling, nested and crossed designs, random effects, and Bayesian approaches.

Learn the fundamentals of time series analysis in R. Explore stationarity, ARIMA, decomposition, smoothing, and forecasting in this on-demand course.

Master time series analysis and forecasting in R. Learn ARIMA, GAMs, exponential smoothing, Bayesian models, GARCH, and more in this on-demand course.

Data Visualisation in R using ggplot2 – online course covering scatterplots, histograms, bar plots, boxplots, density plots, line plots, heatmaps, maps, and advanced customisation for publication-quality graphics.

Learn machine learning in R with this comprehensive course. Covers clustering, regression, trees, neural networks, and more—fully online and hands-on.

Learn machine learning in R with practical, hands-on instruction. Covers supervised, unsupervised models, interpretability, and evaluation in 40 hours.

Advance your machine learning skills in R with deep learning, Bayesian methods, transformer models, clustering, and anomaly detection in this 28-hour live online course.

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.

Machine learning using Python: supervised and unsupervised learning, neural networks, and practical model building with scikit-learn and TensorFlow.

Machine vision using Python: apply deep learning and computer vision with OpenCV and TensorFlow for real-world image classification and ecological data applications.