Module 02
Python and engineering practice
Write Python other people can run: clean data structures, vectorised pandas, tests, and a project layout that survives handoff.
Outcome
What you will be able to do
You can turn a notebook experiment into a tested, importable Python package with reproducible dependencies.
Lessons
Work through these in order
- 01Python built for data workThe subset of Python that actually shows up in data science code: comprehensions, unpacking, generators, and the cost of mutability. 25 min
- 02pandas that scalesVectorised operations, correct joins, groupby-agg, and the memory habits that stop a 5 GB CSV from killing your kernel. 30 min
- 03SQL and relational thinkingWindow functions, CTEs, and the join semantics that decide whether your metric is right or quietly double-counted. 28 min
- 04From notebook to packageProject layout, virtual environments, pytest, and pre-commit — the mechanics that make your work reproducible by someone else. 26 min
Assessment
Module quiz — 70% to pass
8 questions mixing concept checks and short code-output problems. Graded on the server with per-question explanations afterwards, unlimited retakes, and a badge with a verification code the moment you pass.