Data Science for Economists

Hands-on learning with cutting-edge tools and methodologies, from web scraping to satellite imagery analysis.

Author

Julian Hinz

Fields in Kazakhstan.

About

This course introduces economists to modern data science tools and workflows. Over eleven weekly sessions, participants learn to work with large structured datasets, scrape the web for novel data, analyze text and spatial information, apply machine learning methods, and integrate large language models into their research pipelines — all in R.

Each module pairs a lecture introducing research applications with a hands-on coding session where participants replicate the analysis on real data. By the end, you will have a working toolkit that extends well beyond standard econometrics.

Taught at Bielefeld University by Julian Hinz and Irene Iodice.

Course repository: github.com/julianhinz/data-science-for-economists-bielefeld-2026

Schedule — Summer 2026

Date Module Instructor
Apr 15 01 Course Outlook Julian
Apr 22 02 R and the Shell Julian
Apr 29 04 Web Scraping & APIs Irene
May 6 03 Large Structured Data Julian
May 13 06 Text as Data Irene
May 20 07 Satellite Data Julian
May 27 Spatial Data / TBD Irene
Jun 3 08 Time as Data Irene
Jun 10 09 Machine Learning Irene
Jun 17 10 LLMs Julian
Jun 24 — no class —
Jul 1 12 AI-Assisted Research Julian

Modules

  1. Getting Started — course overview, reproducibility, and modern AI tools
  2. Toolkit: R and the Shell — R programming, Unix shell, git, and make
  3. Large Structured Data — data.table, DuckDB, and Apache Arrow
  4. Web Scraping & APIs — rvest, APIs, and the Billion Prices Project
  5. Text as Data — tokenization, tf-idf, and sentiment analysis
  6. Spatial & Satellite Data — sf, terra, nighttime lights, and deforestation
  7. Time as Data — lubridate, event studies, and structural gravity
  8. Machine Learning — regularization, cross-validation, and causal forests
  9. LLMs — using LLMs via APIs, structured outputs, and prompt engineering
  10. AI-Assisted Research — CLAUDE.md, skills, code review, and research life hacks

References and additional resources

Contact