ECON 284 - Data Carpentry for Economists

Semester Offered: Spring
1 unit(s)

Economists increasingly work with large, messy, and unstructured datasets—from census microdata and government APIs to online experiments and text scraped from job listings. Data Carpentry for Economists introduces students to the computational and organizational tools needed to transform raw data into reproducible economic evidence. Students learn principles of clean coding, data wrangling, and workflow design in R and Python, with applications drawn from real-world economic contexts such as labor markets, geographic inequality, and text-based measures of sentiment or discrimination. The course emphasizes good coding habits, transparent documentation, and reproducible analysis, while introducing intermediate-level computational methods (bootstrapping, power analysis, text mining, web scraping, geospatial analysis, and parallel programming). The course deliberately stops short of formal econometrics—no regressions or hypothesis tests—so that students can first master the mechanics of data before modeling relationships. Kyle Coombs.

Prerequisite(s): ECON 102, CMPU 100 and ECON 203 or MATH 240.

Two 75-minute periods.

Course Format: CLS