Advanced Quantitative Methods:
Description and Prediction
Summer Math Camp
Conceptual mastery of statistical methods for public policy, focusing on application rather than mechanics
A cleaner start to the fall quantitative sequence
Math camp is designed to make the first weeks of the semester less chaotic. The emphasis is practical: data wrangling, visualization, modeling, reproducible reporting, and disciplined AI-assisted workflows.
Format
Four weeks of lesson material, hands-on sessions, and labs built around real policy-data workflows.
Tools
R, tidyverse, ggplot2, Quarto, and selected AI tools used with verification and documentation.
Outcome
Students arrive in the fall with a reusable workflow instead of starting from zero.
Select Academic Term/Year
Teaching Team
- Rony Rodriguez-Ramirez
- Wexner 436. HKS
- rrodriguezramirez@g.harvard.edu
Bluesky
Course Details
- Check schedule page
- August 15-September 4, 2026
- Time: Varies
- Wexner 436. HKS
Contact
The best way to reach me is through email or Slack. I’ll do my best to respond as quickly as possible, but please understand if there’s a bit of a delay—sometimes things get busy! Looking forward to helping out with any questions you have.
What You Build
Quantitative foundations
- data cleaning and reshaping with the tidyverse
- plots that move from exploration to communication
- introductory modeling and interpretation
Workflow habits
- Quarto documents that combine code and explanation
- reproducible lab and problem-set structure
- AI use that is scoped, checked, and documented
Why This Version Matters
The 2026 site is the first version that explicitly treats AI as part of quantitative workflow training rather than an afterthought. The aim is not to replace coding fundamentals. The aim is to help students use tools like Codex, Claude Code, and chat-based assistants without weakening verification or reproducibility.