Data Analytics

2 credits · Graduate

This course will introduce students to advanced methods for data driven decision making in business including forecasting and evaluating alternative possible strategies. Topics include designing randomized controlled trials in the field, evaluating natural experiments (e.g. differences-in-differences, instrumental variables and regression discontinuity) and machine learning tools for forecasting (e.g. linear regularization, tree models and random forest). The course work will include problem sets evaluating experimental and non-experimental data, writing code and making business decisions using the results.