Data Science, Econometrics, and Research in the Age of AI

Abstract

This seminar introduces a practical way to use artificial intelligence for data science, econometrics, and research. It opens with the production-versus-verification trade-off: as AI makes producing code, text, and results almost free, the binding constraint shifts to verification. It then presents three tools and the discipline to use them well — NotebookLM, which grounds AI in your own sources for protected, interactive learning; Google Colab, a cloud notebook for interactively exploring data with AI support; and GitHub, which makes AI-assisted research transparent and auditable by changing the unit of verification to small, reviewable commits and diffs. The closing principle is that AI augments human judgment rather than replacing it — production is cheap, so verification becomes the scarce skill.

Carlos Mendez

Associate Professor of Development Economics

My research interests focus on the integration of development economics, spatial data science, and econometrics to better understand and inform the process of sustainable development across regions.