Open learning tracks you can start immediately
These resources are freely accessible learning tracks designed around real analytical workflow: how data is structured, what to check, what to trust, and how to interpret results.
Data Science
Start with Foundations, then expand into visualization, machine learning, and databases as you build depth.
Build core skill in data workflow: setup → load → clean → explore → visualize → summarize.
Clean plots, interpretation, and communication for analytical work.
Applied ML workflows with evaluation logic and interpretation.
Schema thinking, clean SQL, joins, and analytics-ready datasets for reporting and ML.
Applied Bioinformatics
Downstream analysis and interpretation tracks (R-first) with real research-style outputs.
Diversity, ordination, comparisons, and interpretation using tables plus metadata (R-first).
QC logic, contrasts, differential expression, and interpretation for expression data (R-first).
QC, association testing, and interpretation for GWAS workflows (R-first).
QC, clustering, dimensionality reduction, marker logic, and interpretation (R-first).