MSDS_460_DecisionAnalytics
This course covers fundamental concepts, solution techniques, modeling approaches, and applications of decision analytics. It introduces commonly used methods of optimization, simulation and decision analysis techniques for prescriptive analytics in business. Topics explored include: linear programming, network optimization, integer linear programming, goal programming, multiple objective optimization, nonlinear programming, metaheuristic algorithms, stochastic simulation, queuing modeling, decision analysis, and Markov decision processes. Through individual and team projects and discussions, a contextual understanding of techniques useful for managerial decision support is developed. This is a problem and project-based course and utilizes Python, Gurobi optimization package, R, ASPE Excel that culminates in a team course project requiring problem formulation, literature review, model formulation, and programming to obtain results.
** Brandon is currently in this course and will be updating completed course work as the term progresses. Be sure to check back for updates.