// course
Python Analytics Sprint
Pandas workflows, reproducible notebooks, and chart ethics for operational metrics.
Data Evening cohort · 6 weeks ₫7,800,000 (info only)
Overview
Emphasizes tidy inputs, documented assumptions, and reviewable notebooks your PM can open without fear.
Included modules
- Notebook linting habits
- Parquet vs CSV tradeoffs with samples
- Visualization guardrails for skewed data
- Unit tests for transformation helpers
- Peer review of narrative captions
- Export pipelines to CSV + dashboard handoff
- Ethical framing for small samples
Outcomes
- Deliver a reviewed notebook pack
- Write a data dictionary for stakeholders
- Present one chart with explicit caveats
FAQ
Comfort with descriptive statistics; linear algebra not required.
Participant notes
Python Analytics Sprint made me annotate every merge step; tedious at first, now standard on my team.
Week three skew discussion changed how we label funnel charts.