Data Analytics

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