This site hosts materials for my Data Science for Development Economics course at the Université Clermont Auvergne. See the Syllabus for an overview of the course's organization, and below for the course outline. Contact me at [email protected] for more information.
All code applications in this course use Python. Follow the course setup guide for instructions on getting set up to work in Python if you are new. If you are a more experienced user, skip to the section indicating what packages are used in the course.
Course outline
- Introduction to Data Science and Python/Jupyter Setup
- Research reproducibility and Python basics
- Data wrangling and visualization
- Generative AI and big data in economics research
- Understanding spatial data and basics of geospatial analysis
- Remote sensing in economics and working with spatial data
- Supervised machine learning
- Unsupervised machine learning
- Text analysis and natural language processing
- Web APIs and web scraping
I also provide an index of potential resources for further learning in each of these topics, including in languages other than Python.
If you want to use these materials, please consider the following citation.