R Foundations for Health Data Science
Your first reproducible analysis in 12–15 hours. The R/RStudio environment, dplyr, tidyr and clinical ggplot2. Output: a script of your own that you can show on Monday.
Clinical Analysis and Visualisation with R
Turn your Excel file into a report that impresses the committee. Clinical EDA, tables with gtsummary, advanced ggplot2, reporting with Quarto and an introduction to Shiny.
Applied Clinical Biostatistics with R
From the p-value to real clinical benefit. GLM, AUROC, calibration, Decision Curve Analysis and model documentation (model cards).
Survival and Time-to-Event Analysis with R
Times, events and censoring, without the fear. Kaplan–Meier, Cox, hazard ratios, competing risks and TRIPOD-compliant reporting.
Foundations of Clinical Biodata
Introduction to biodata, basic computational epidemiology, applied biostatistics
Master's in Biodata Science Applied to Medicine and Health
The flagship programme. Six modules: data hygiene, predictive modelling, causality, survival analysis, reproducibility and a final project.