Four programmes, one for each research group. Each is the entry requirement for its group: a student completes the relevant programme first, then joins a project. They exist because nothing equivalent is freely available to most undergraduates here.
Apply to the group whose questions interest you, and take its programme. Each one ends where the group's real work begins — a student who finishes it can be handed a dataset and a supervisor rather than a tutorial.
The Linux command line, R and Bioconductor, then the transcriptomic workflow itself: quality control, differential expression, pathway enrichment, and single-cell analysis. Ends with a full RNA-seq analysis run on a public tumour dataset.
Group →Observational study design, then biostatistics and survey analysis in R — weighting, regression modelling, and the systematic review and meta-analysis methods the group publishes with. Ends with a secondary analysis of a national health survey.
Group →Python, then supervised learning and deep learning on clinical and imaging data with scikit-learn and PyTorch. Validation, class imbalance and interpretability are taught as part of the method, not after it. Ends with a validated, explained prediction model.
Group →Coordinate systems and spatial data handling in R and QGIS, then disease mapping, cluster detection, accessibility modelling and satellite earth-observation data. Ends with a disease map built from Bangladesh surveillance and environmental layers.
Group →Open course materials. Much of what these programmes teach is already online and free to work through before you apply: Bioinformatics Bootcamp, R for Bioinformatics, RNA-seq analysis with R and single-cell RNA-seq with R.
Sessions are hands-on and mostly online, so students outside Dhaka can take part. Every session ends with an exercise on real data, and the material stays available afterwards.
Training is free. It is also not a certificate mill — the programmes are assessed on whether a participant can go on to do the analysis unaided, because that is what joining a group actually asks of them.
University students in life sciences, computer science, medicine, public health, statistics, geography and related fields. No programming background is assumed — each programme starts from the command line or the language it needs, and builds from there.
Take the programme for the group you want to join. Nothing stops a student taking a second one later, and several of our researchers have, but one is what is required to start.
Course notebooks and slides are openly licensed and remain online after each cohort. See learning resources for the databases and tools we teach against, and recorded sessions where available.