Data Analysis with AI¶
Data analysis with AI covers the use of AI coding assistants and tools for statistical analysis, data exploration, and quantitative research — from summary statistics to complex econometric models.
Context & Background¶
AI tools can assist with every stage of data analysis:
- Exploratory analysis: Generating summary statistics, distributions, and initial visualizations
- Data cleaning: Identifying and handling missing values, outliers, and inconsistencies
- Statistical modeling: Writing code for regressions, panel models, and machine learning
- Results interpretation: Explaining statistical output in plain language
- Visualization: Creating publication-quality charts and tables
Practical Implications¶
- Start with exploration: Let AI generate initial data summaries before diving into analysis
- Verify against known results: Test AI-generated analysis code on data with known properties
- Use AI for iteration: Quickly test alternative specifications and robustness checks
- Don't outsource understanding: You must understand the statistical methods, not just run them
Key Sources¶
- Your CLAUDE.md
- Web Scraping: Claude Code for Economists with Paul Goldsmith-Pinkham (Markus Academy Ep. 162-3)
- The Train Has Left the Station: Agentic AI and the Future of Social Science Research
- Chris Blattman Thread: From Claude Code Skeptic to Power User
- The Shape of AI: Jaggedness, Bottlenecks and Salients