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Python Programming & AI
Fundamentals with Agentic
Empirical Research
(StatsPAI / Stata / R)
From coding to causal inference to automated research
Instructor
Bryce Wang (XING)
Stanford REAP Team
Course Info
Syllabus · Structured Roadmap
IPython Programming
- Python fundamentals and advanced use
- Web scraping (requests / BeautifulSoup)
- pandas data cleaning and processing
- Visualization (matplotlib / seaborn)
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IIAI Fundamentals
Machine learning sklearn / neural networks PyTorch
- ML foundations and modeling (sklearn)
- Classification / regression / clustering / dimensionality reduction
- Neural-network foundations (PyTorch)
- Hands-on model training and evaluation
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IIICausal Inference and Machine Learning
RCTs and quasi-experiments / ML + Stata · R
- Causal-inference foundations and potential outcomes
- RCT design and analysis
- Quasi-experimental methods (DID / PSM / IV / RD)
- Synthetic control (SCM / Synth / SDID / GSC)
- ML causal inference (DML / Causal Forest)
- Applied empirical cases in Stata / R
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IV✦Agentic Empirical Research
MCP / Skills / StatsPAI / automated research
- LLM and Agent foundations
- MCP protocol and tool calling
- Skills for task automation
- StatsPAI for empirical research
- End-to-end research: topic → data → analysis → paper
IIII
Tools and Platforms
VSCode
Jupyter Notebook
Python
Stata
R
Git
Codex
Claude Code
Course Highlights
✔Structured roadmap
✔Applied workflow
✔Interdisciplinary toolkit
✔Fast research output
Highlights