Title: Watch: The reproducible acquisition workflow
Course: Advanced Data Science: A Graduate Course > Section: Module 1: Acquisition and the reproducible workflow

Watch

▶ Recorded lecture (~25 min) + slides ↓:
- The data-science lifecycle, and why acquisition and provenance sit at its foundation.
- What reproducibility actually requires: version-controlled code, pinned dependencies, seeded randomness, and a separation of code (committed) from data and secrets (never committed).
- Provenance as a first-class artifact: lineage, retrieval timestamps, and the datasheet idea that every field should be traceable to where and when it came from.
- The four acquisition modes compared: flat files, SQL databases, REST APIs, and web scraping, and the failure mode each one hides.
- The ethics of scraping: reading and honoring robots.txt, respecting rate limits and crawl-delay, and understanding Terms of Service and the difference between “technically reachable” and “permitted.”
- How a moving target (a paginated, rate-limited API whose schema drifts) breaks naive pulls.

“Advanced Data Science by Michelle Blomberg is licensed under CC BY-NC-SA 4.0.”