Machine Learning for Predictive Analytics in Economics

Catalog description

This course introduces students to modern empirical tools used in economics, business, and data-driven decision-making. Students learn how to manipulate data, explore patterns, build predictive models, and apply machine learning methods to real economic and business problems. By the end of the course, students will be able to: (1) Clean, transform, and prepare datasets using Pandas; (2) Conduct effective exploratory data analysis; (3) Understand the predictive modeling workflow and evaluate model performance; (4) Build regression and classification models using linear and tree-based methods; (5) Apply unsupervised learning to uncover structure in data; (6) Work with time series data and construct simple forecasting models; (7) Communicate empirical findings clearly in economic or business terms; and (8) Complete end-to-end applied machine learning projects. This course is an excellent complementary course to Econometrics. Econometrics focuses on causal inference and estimation, while this course focuses on data handling, exploratory analysis, predictive modeling, and artificial intelligence (AI) and machine learning (ML). Together, they provide students with a complete empirical skill set for research, analytics, and graduate study. The course is ideal for students pursuing analytical roles, research assistant positions, consulting, finance, economics graduate school, or data science--oriented career paths. Prereq: ECON 102 and ( ANTH 319 or OPRE 207 or SOCI 307 or STAT 301 or STAT 312 ).