Advanced Prescriptive Analytics: From Data to Impact

Catalog description

This course provides an in-depth focus on prescriptive analytics, which involves the use of data, assumptions, and mathematical modeling of real-world decision problems to ascertain and recommend optimal courses of action. Starting from conceptualization of the problem, to using theory for translational modeling and techniques, to computational solving, and finally interpretation – likely in an iterative manner – students will gain knowledge of tools and practical skills in transforming real-world decision problems into actionable insights. Advanced topics in the prescriptive analytics domain will be covered, such as the use of integer variables to represent important logical constructs, using nonlinear functions to represent real-world decision aspects, the incorporation of stochasticity and uncertainty, and corresponding solution methods. Real-world problems will be selected from a variety of contexts that may include capacity management, data science, finance, healthcare, humanitarian operations, inventory management, production planning, routing, staffing, and supply chain. Recommended Background: An introductory level of exposure to prescriptive analytics or linear optimization, such as can be found in OIE 2081, MA 2210, or MA 3231. Suggested Background: Note the mathematical foundations of some of the optimization techniques in this class are in MA 3231. Students might also benefit from MA 3233. Note: Students cannot take both OIE 4420 and OIE 4430 for credit.