This course will introduce prescriptive analytics techniques that are used in solving complex decision problems. The ideas that are presented in this course are motivated by today’s rise of big data analytics and the widespread of data driven decision making. Students will learn how to formulate decision making problems as mathematical models, how to source the data for these models, how to implement these models and solve them, and finally how to interpret the resulting solutions to gain business and managerial insights.
The focus of the course will be on both the practical and the theoretical aspects of prescriptive analytics as well as on basic level computer programming that is needed for data manipulation and model development. Prescriptive analytics (mathematical programming/optimization) is about “doing the best” by finding the optimal way to allocate scarce resources subject to various constraints. Such models are called prescriptive as they mathematically find the best possible solution based on the model’s assumptions.
This course will cover the following topics:
- Formulation of prescriptive analytics models
- Implementation and solution of the models
- Basic computer programming to manipulate the data and construct the models
- Linear programming, integer programming, and optimization under uncertainty
Required