Title: Practice and formative deliverable
Course: Advanced Data Science: A Graduate Course > Section: Module 1: Acquisition and the reproducible workflow

Practice and formative deliverable

The practice items below are formative and ungraded. They build intuition before the graded assessment. The formative deliverable is checked for completion and returned with feedback, and it earns completion points on submission, credited on completion rather than scored for correctness.

Work through four short hands-on run-throughs that each rehearse one acquisition mode you will combine in the assessment.

1. Complete the query chapters of Select Star SQL through joins and aggregation; keep the exact text of two queries you write, because you will reuse that pattern to pull from the database source later.
2. In Hoppscotch, send a GET request to the JSONPlaceholder /posts endpoint, then to /posts?userId=1, and inspect the response headers and status codes; note how query parameters change the returned set.
3. Still in Hoppscotch, page through /comments using the _page and _limit parameters against JSONPlaceholder and record how many total records you retrieve versus how many exist, so you can feel where a naive single call would truncate the data.
4. Practice responsible scraping in the quotes.toscrape.com sandbox: first read its robots.txt, then extract the quotes and authors from the first two pages only, adding a polite delay between requests and a descriptive user-agent.
5. Open a reproducible notebook environment on Binder from a small public repository, run it top to bottom, and confirm you get identical output; this is the environment model your own deliverables must match.

◎ Formative deliverable: A short reproducible notebook that pulls the same small dataset three ways (a flat file, a SQL query, and an API call) and, for each, records source, retrieval time, and parameters in a provenance cell. It is checked for reproducibility (a peer or the instructor runs it from a clean environment and gets the same results) and for whether the three pulls actually reconcile; feedback is returned before the summative opens, and it earns completion points on submission, credited on completion rather than scored for correctness.

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