Decision Analytics for Business and Policy

94-867 (Heinz) / 19-867 (Engineering and Public Policy) / 12-768 (Civil and Environmental Engineering)

(This course has recently been redesigned.)

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 operations 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 contextual optimization, optimization under uncertainty, to dynamic learning and optimization. We motivate our technical discussions by a rich set of applications, such as logistics planning, resource allocation, and health care. 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. Coding will be done in Python for the most part. Students will use a range of modern optimization and machine learning packages, as well as AI co-pilot.

Prerequisites: (1) An introductory course in management science / operations research, (2) intermediate Python programming skills, (3) basic understanding of data and machine learning, and (4) mathematical maturity.

Decision Making Under Uncertainty

95-760 (Heinz)

This course provides an introduction to modeling and computational methods used by policy-makers, managers and analysts to support decision-making. The first half of the course focuses on deterministic optimization, and covers linear programming, network optimization and integer programming. The second half of this course introduces risk and uncertainty, and includes methods to characterize uncertainty and methods to optimize decisions under uncertainty. Examples are drawn from a variety of domains where these decision-making methods can provide value for business and policy, such as transportation, energy, health care, logistics, manufacturing, supply chain management, etc.

The readings, lectures, recitations, homework assignments and exams will help students develop modeling skills, computational skills and analytical skills. Modeling skills involve translating a problem into a well-defined mathematical framework, using little more than pen and paper. Computational skills involve solving models in Excel. Analytical skills involve critically interpreting a model and translating results into insights for decision-making.