Python for Data Analysis & Database Connectivity Project

Start with PROJECT_OVERVIEW.md for what you're building and why. This file is where the step-by-step setup lives.

Due: 1 week, run as a sprint — 5 build days, plus a required share-out session scheduled after. See CHECKLIST_TIMELINE.md for the day-by-day pace and the full submission checklist.

This repo is a GitHub template — a starting point, not something you edit directly on Marcy's copy of it.

Getting started

Before you do anything else: click "Use this template" on this repo's GitHub page (not "Fork") to create your own copy. See GETTING_STARTED.md — git setup (tested again this module, less hand-holding than Module 0), uv environment setup, and reconnecting to your Module 3 database (with a real fallback if it doesn't exist anymore).

What to do

See starter/ for everything you'll fill in:

• db_connect.py — given connection mechanics for both psycopg2 and SQLAlchemy; imported directly into analysis.ipynb, not run on its own.
• analysis.ipynb — the graded hands-on notebook, in two parts: (1) connect both ways, load into pandas, produce real summary statistics; (2) pull at least two of your own Module 3 tables in raw, and do the real relational work — joining and a group-level comparison across a categorical variable — yourself in Python (pandas or polars, your choice) instead of more SQL; find and resolve at least 2 real data-quality issues in your own data along the way, justify each choice, output a clean CSV.
• reflections.md — two sections: your real psycopg2-vs-SQLAlchemy comparison, based on what you actually experienced running both; and the AI-assisted-coding exercise — draft a connection function with a free-tier AI assistant, then find and document a real bug or inefficiency before trusting it. First formally graded AI-assisted-coding work in the program — see the file for exactly what's expected.

CHECKLIST_TIMELINE.md has the suggested day-by-day pace and the full sequenced checklist. Commit incrementally — after your connection works, again after the AI-review exercise, again after the pandas join/cleaning work — not one commit at the end.

Where's the exact bar for "done," and what are the optional stretch goals? This repo (your own copy) doesn't include MVP.md (your Minimum Viable Product — the required baseline) or ABOVE_AND_BEYOND.md on purpose. Ask your instructor for the link to this template's project-scope branch, or check the checklist your instructor shares through the classroom.