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

Faculty: Conrad Miller

Course Date & Time

Mondays, 4-6 pm (Pacific Time in the US), no class on Sept 2