IE 5533: Operations Research for Data Science

3 Credits

This course combines data, modeling, and decision-making to provide students with experience solving practical problems in a variety of application areas, including healthcare and medical decision-making, supply chains and e-commerce, and finance and revenue management. To this end, case studies will be used to illustrate the sequence of problem definition, data analysis, model building, and decision support. The example problems are realistic in terms of size and complexity and the data sets are realistic in that the quality of the data is less-than-perfect. The first part of the course focuses on deterministic models while the second part of the course covers stochastic models. A high-level programming language such as R is used for data manipulation and for predictive analytics. An algebraic modeling language such as AMPL is used for models that require linear/integer programming. The solutions and their sensitivity to changes in parameters are interpreted to aid decision-makers. Throughout the course, the methodologies are kept in perspective with the overall goal of making better decisions.

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A- Average (3.771)Most Common: A (56%)

This total also includes data from semesters with unknown instructors.

71 students
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  • 4.85


  • 4.62


  • 4.62


  • 4.77



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