Curriculum
You don't pick a track on day one. Everyone does Challenge 0 first, environment setup, the same for every track, and chooses after that. Each track has its own lead, its own weekly challenges and its own Capstone.
ml
Models, algorithms, prediction
Build models that learn from data and make predictions. You start with the maths that actually matters, then move to training, evaluating and deploying real models.
ds
End-to-end analysis, statistics, storytelling
Take a question from raw data to a defensible answer. Statistics you can justify, analysis you can reproduce, and findings a non-technical audience can act on.
de
Pipelines, infrastructure, the data backbone
Build the plumbing everything else depends on. Ingestion, transformation, orchestration and storage, the work that decides whether analysis is possible at all.
da
Business insight, dashboards, SQL-heavy
Answer business questions with data and make the answer legible. Heavy SQL, dashboards people actually open, and the judgement to know what a number means.