Teaching
Courses and workshops on economics, econometrics, data science, and R programming.
Impact-evaluation methods for public policy and development economics — RCT, IV, regression discontinuity, difference-in-differences, event studies and matching — each paired with a canonical paper and applied in R.
Data analysis and machine learning for business decision-making: R and the tidyverse, exploratory data analysis, clustering, decision trees, and AI tools.
A hands-on R programming seminar: programming fundamentals and data structures, projects and version control with Git, data wrangling and dataset joins, visualization with ggplot2, loops and the apply family, web scraping, spatial (GIS) data, regressions, text mining, and reproducible reports with R Markdown.
Applied big data and machine learning for real-estate problems — predicting housing prices from large and spatial data — covering the full prediction workflow and model evaluation in R.
Graduate course on big data and machine learning for applied economics: the prediction workflow, overfitting and cross-validation, regularization, web scraping, Bayesian sampling methods, and spatial data and models.