Introduction to Decision Analytics

3 credits · College of Engineering catalogue

Advisory Prerequisite: Undergraduate calculus, probability, statistics, and linear algebra; basic programming skills (Python or similar). Enforced Prerequisite: Graduate Standing. (3 credits) A foundational and application-driven introduction to mathematical modeling for decision-making. Formulate, analyze, and solve a wide range of real-world optimization problems that arise in logistics, operations, energy, health care, and service systems are developed. Topics include basics of linear and integer programming, dynamic programming, basic probability, Markov chains, queueing theory, and decision analysis. Modeling and algorithmic thinking are both emphasized, with practical experience using modern solvers.