Building a Data Driven Organization
summary
Claims a 150× client growth from a MacBook and a CSV to deployed pipelines, using Python plus concrete tools: Black for deterministic formatting, Airflow for opinionated ETL, and Process Street for checklisted operations. Builds an active‑learning labeling loop, measures success by capturing 80% of sales in the top 20% of scores, targets labeling at 100 rows in 10 minutes, and adds mlflow tracking. Shows which hires and processes scale and which hiring and tooling anti‑patterns fail.
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chapters (10)
- 0:00Introduction
- 1:59Choosing data science tools
- 6:16Frameworks for thinking
- 8:22Ad-hoc to automation
- 16:15Repeatable scalable processes
- 18:54Hiring team dynamics
- 31:26Data-driven non-technical teams
- 33:00Team weaknesses hiring mistakes
- 38:05Startup challenges skin in game
- 41:46Q&A
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