PRESCRIPTIVE MODELING AND OPTIMIZATION FOR BUSINESS ANALYTICS
Introduction to fundamentals of prescriptive analytics with emphasis on business applications. Modeling and mathematical optimization using Excel and Python. Designing, building, testing, and analyzing models, including sensitivity and risk analysis. Developing and solving optimization models, including linear, integer, and nonlinear problems. Course includes some principles of model-building and fundamentals of optimization theory but emphasizes practical application, hands-on learning, and problem-driven exercises.
Prerequisites & course information
Requisites: Graduate/professional standing Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Identify appropriate modeling, analysis, and optimization methodologies when faced with a concrete problem. Audience: Graduate 2. Leverage spreadsheet software (e.g., Excel) and programming languages (e.g., Python) for modeling and optimization. Audience: Graduate 3. Classify optimization problems (e.g., linear, integer, nonlinear) and explain the challenges of solving different problem types. Audience: Graduate 4. Recognize classic optimization problems (e.g., knapsack, network, traveling salesman) and recall how to formulate them. Audience: Graduate 5. Discuss fundamental optimization theoretical concepts and their practical implications. Audience: Graduate Learning Outcomes: 1. Identify appropriate modeling, analysis, and optimization methodologies when faced with a concrete problem. Audience: Graduate 2. Leverage spreadsheet software (e.g., Excel) and programming languages (e.g., Python) for modeling and optimization. Audience: Graduate 3. Classify optimization problems (e.g., linear, integer, nonlinear) and explain the challenges of solving different problem types. Audience: Graduate 4. Recognize classic optimization problems (e.g., knapsack, network, traveling salesman) and recall how to formulate them. Audience: Graduate 5. Discuss fundamental optimization theoretical concepts and their practical implications. Audience: Graduate
Sources
- Course catalogue ↗2026-2027