GS631 — Data Science for Public Policy (course shell)
GS631 is the on-site course shell for Data Science for Public Policy. This page is the durable URL for syllabus-level material; week-by-week slides and assignments may live in the LMS while framework essays and citeable references stay here.
Course intent
Students should leave able to:
- Frame a policy question as a measurable problem (unit of analysis, counterfactual, ethics).
- Build a reproducible analysis pipeline (ingest → clean → model → communicate) without mistaking tooling for thinking.
- Present results to a non-technical audience with honest uncertainty.
Schedule (outline)
| Block | Topics |
|---|---|
| 1–3 | Measurement, bias, and causal language |
| 4–6 | Data wrangling and exploratory analysis in Python |
| 7–9 | Prediction vs explanation; evaluation and leakage |
| 10–12 | Communication: memos, charts, and critique |
Exact dates and readings are updated each term in the LMS; this shell tracks stable learning outcomes and public references.
Materials policy
- On-site (Tier 1): frameworks, reading lists, and essays meant to be cited.
- LMS / private: problem sets, solutions, and student-specific feedback.
How to cite this page
Use the canonical URL for this shell when referencing the course structure. For individual lecture essays, cite the specific post URL once published under the teaching pillar.
Contact
Course logistics go through the university LMS. For collaboration or guest lectures, use the links on About.