Decision Analytics for Business and Policy


Units: 12


Most courses under the name of “analytics” are about making predictions (i.e., data analytics).
However, predictions alone are not sufficient to solve many real world problems. We need to use predictions
and other information as input to ultimately produce decisions. This course focuses on the science of making
good decisions.

This course assumes intermediate python experience and prior exposure to management science and opera-
tions research topics. It focuses on modeling frameworks and computational tools to address complex decision-
making problems that arise in policy and business. The course is organized by technical topics, from single-stage
optimization, to multi-stage optimization, to dynamic learning and optimization. We motivate our technical
discussions by a rich set of applications. Given the fast pace of this course, we expect students to take an active
learning role by participating in hands-on modeling and computational exercises throughout the semester. Cod-
ing will be done in Python for the most part. Students will use a range of modern optimization and machine
learning packages, and also develop their own algorithms in learning and optimization.

Learning Outcomes


Prerequisites Description

(1) An introductory course in management science / operations research, (2) intermediate Python
programming skills, (3) basic data and mathematical maturity.