Applied Data Science in Economics

Catalog description

Credits 3. 3 Lecture Hours. Introduction to modern data science methods and their application to real-world economic and business decision-making; integrates economic reasoning with computational tools for analyzing and interpreting data; introduces coding and statistical modeling, including resampling, cross-validation and machine learning techniques such as random forests and neural networks; emphasizes how data-driven insights complement economic intuition in organizational and policy contexts; designed for students seeking to apply economic analysis and data skills in professional, multidisciplinary environments; topics and applications vary across instructors. May be taken two times for credit. Prerequisite: Grade of C or better in ECMT 463 .