I am also exploring private-sector positions in the Bay Area. To support this transition, I have been building a series of short, self-directed courses to refresh and develop skills relevant to the roles I'm targeting — policy researcher, applied policy economist and data scientist positions at technology platforms.
The courses below were built using Claude Code. Each course is a set of xaringan slide decks with worked examples, R/Python/SQL exercises, and interview-style practice questions, all threaded through industry-related applications.
ML Refresher: Discrimination and Fairness — Bias-variance tradeoff, linear models, model evaluation, fairness frameworks, and the impossibility theorem. Every module pairs an ML concept with a discrimination scenario.
Discrimination in Labor Economics Refresher — Becker, Phelps, audit studies (Ge et al. 2016), Oaxaca-Blinder decomposition (Cook et al. 2021), algorithmic audits, and the practitioner playbook.
Intro to Experiments in Industry — Potential outcomes and the experimental ideal, SUTVA and interference, cluster/switchback designs, regression adjustment and CUPED, ITT vs LATE, and external validity.
Intro to SQL — SELECT through window functions, drilled on a synthetic ride-sharing schema. Five modules of interview-style questions designed to be answerable from a cold start in under two minutes each. One pager.
Python for an R User — The pandas + statsmodels stack, framed as a dplyr-to-pandas translation. Covers data wrangling, joins, OLS, A/B test inference, and difference-in-differences, with end-to-end interview scenarios.