Online courses in Applied Statistics, Genomics, Bioinformatics, Ecology and Social Sciences
Learn from leading experts, over 350 courses delivered since 2014 across 60 diverse subjects
Understand the data behind the science
Expert-Led Training
Live and recorded access
Beginner - Advanced
Discuss your own data
£450Registration Fee
View DetailsIntroduction to Processing and Analysis of Spatial Multiplexed Proteomics Data (SPMP01) SOLD OUT!
Delivered remotely (United Kingdom) Western European Time, United KingdomLearn spatial multiplexed proteomics data analysis with CODEX, CycIF, and MACSIMA. Master image processing, segmentation, phenotyping, and spatial analysis in R and Python.
£500Registration Fee
View DetailsBayesian Modelling Using R-INLA
Delivered remotely (United Kingdom) Western European Time, United KingdomLearn 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.
£480Registration Fee
View DetailsPython for Biological Data Exploration and Visualization
Delivered remotely (United Kingdom) Western European Time Zone, United KingdomExplore and visualise biological data in Python using pandas and seaborn. Ideal for applied researchers.
£350Registration Fee
View DetailsSingle cell RNA-Seq analysis (SCRN02)
Delivered remotely (United Kingdom) Western European Time Zone, United KingdomLearn single cell RNA-Seq analysis with Seurat, 10x Genomics, and advanced QC methods. Gain cell type-specific insights in this live online course.
£480Registration Fee
View DetailsAdvanced Python for Ecologists and Evolutionary Biologists
Delivered remotely (United Kingdom) Western European Time Zone, United KingdomTake your Python skills further. Learn OOP, testing, and optimisation for complex bioinformatics tasks.
£400Registration Fee
View DetailsCausal Inference for Ecologists (CIFE01) SOLD OUT!
Delivered remotely (United Kingdom) Western European Time Zone, United KingdomCausal Inference for Ecologists is an applied R course teaching researchers how to identify and estimate causal effects in ecological and environmental data.





