MSDS 460-DL Decision Analytics.

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. Students explore 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. Students develop a contextual understanding of techniques useful for managerial decision support. They implement decision-analytic techniques using state-of-the-art analytical modeling platforms. This is a problem and project-based course. Prerequisites: MSDS 400-DL Math for Modelers and MSDS 401-DL Applied Statistics with R.

Students benefit by taking the Python Learning Studio and MSDS 430 Python for Data Science prior to taking this course.

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