What it does This repo collects the hands-on teaching materials from a working data science team’s weekly 45-minute learning sessions. Topics span Python basics, statistics, machine learning, NLP, network analysis, visualization, and engineering practices — all presented as notebooks, READMEs, or interactive walkthroughs.

The interesting bit These aren’t polished courses; they’re working notes from practitioners teaching each other. The breadth is unusual — you’ll find count-min sketches next to Flask basics, causal inference beside bash data structures. It’s a rare look at what a team actually decided was worth learning together.

Key highlights

• 50+ topics across Python, stats, ML, NLP, networks, viz, databases, and engineering
• Heavy emphasis on hands-on formats: IPython notebooks, knitr, live coding sessions
• Includes niche practical topics often skipped in formal curricula: jq, Vertica, horizon charts, bandit algorithms
• Originated at Gnip/Twitter Boulder, so social data and streaming contexts appear throughout
• Explicitly welcomes pull requests and reuse for other teams

Caveats

• Quality and depth vary by topic; these are team notes, not vetted courses
• Some links may be stale; the README doesn’t indicate last-updated dates
• “Every week*” comes with an asterisk and guilt trips, so consistency is unclear

Verdict Great for data scientists or Python developers building a self-study curriculum or team training program. Less useful if you need polished, sequential coursework — this is a grab bag, not a degree.