Big Data (Econ 1660)
What do you need to add to data in order to learn from it?
The spread of information technology has lead to the generation of vast amounts of data on human behavior. This course explores ways to use this data to better understand the societies in which we live. The course weaves together methods from machine learning (OLS, LASSO, trees) and economics (reduced form causal inference, economic theory, structural modeling) to work on real world problems. We use these problems as a backdrop to weigh the importance of causality, precision, and computational efficiency.

Targeted at CS/economics upper level undergraduate and graduate students.
Course materials

Student feedback:
  • "The machine learning portion of the class was phenomenal. Best class experience I've had at Brown period... [The competitions] encouraged me to go and learn to improve myself, not to learn because I was told to. As a result. I spent an absurd amount of time on them."
Media coverage:

Development Economics (Econ 2520; PhD)