📘 340.600 Stata Programming — Coursebook Summary

📘 340.600 Stata Programming — Coursebook Summary#

“Code Together. Think Sharply. Automate Wisely.”

Welcome to a radically collaborative approach to Stata programming—built for doers, thinkers, and tinkerers at the Johns Hopkins Summer Institute. Whether you’re a total beginner or a script-savvy analyst, this coursebook is your interactive launchpad into structured, reproducible, and collective data science using Stata.

🧭 Course Format & Flow#

  • Hybrid Learning: In-person at Wolfe W3031 + live Zoom (encouraged for everyone for screen sharing + peer debugging).

  • Daily Cadence:

    • 💬 Lecture (8:30–10:15 & 11:00–11:50)

    • 🛠 Labs (10:15–10:45 & 11:50–12:20)

    • ☕ Break (10:45–11:00)

  • Grading: Based on daily in-class labs. Optional final assignment. Submit all work on time = A/B guaranteed.

🧠 Pedagogical Themes#

This course is not about memorizing syntax. It’s about thinking in code, debugging as a team sport, and documenting your reasoning.

🔑 Modules at a Glance#

Module

Theme

🧠 Collaborative Stata Programming

Version control, code sharing, GitHub as social workspace

✍️ Macros and the Art of Memory

Local/global macros, memory-efficient automation, symbolic thinking

🔁 Loops and Program Syntax

foreach, forvalues, program blocks—how to iterate cleanly

🗃️ Advanced File Management

Import/export strategies, path hygiene, batch processing

📊 Graphs

Building and customizing graphs from the command line

📌 Quick References

Mini cheat-sheets and code patterns for daily use

🚀 Code, Share, Collaborate#

  • GitHub is your lab notebook + water cooler. Share .do file code wrapped in:

    // Your code here

  • AI tools like ChatGPT are encouraged: Use them for debugging, docstring generation, and logic review—just cite them.

  • Reproducibility is king: Think modular. Comment your code. Share smart.

🧭 Culture + Logistics#

  • 💡 “In-person only” makes debugging harder. Collective learning thrives on screen sharing + GitHub posts.

  • 🎙 Zoom etiquette: Mute during lectures. Post questions in chat or GitHub.

  • 🤝 Academic integrity = collaboration with attribution. Cite what you adapt.

🌱 Why This Matters#

This isn’t just a crash course. It’s a training ground for analytical rigor and automation literacy. You’ll leave with:

  • A working knowledge of Stata’s scripting language

  • Templates and workflows to handle real research projects

  • A GitHub-linked portfolio of labs and examples

  • A stronger programming memory (via macros!)

  • A community you can actually rely on

🔗 Authors#

Vincent Jin & Abimereki Muzaale (2025)

With a course this thoughtfully constructed—equal parts tactical and philosophical—you’re not just learning how to code in Stata. You’re learning how to think like a developer in a collaborative, clinically & policy-relevant research context.

Contents