OPERATIONS ANALYTICS
Focuses on the application of analytical methodologies to problems that arise in the context of a company's operations and supply chains. Touches on all three dimensions of analytics (descriptive, predictive, and prescriptive). Emphasis on data and real industry data collected from the university, alumni, and executive board members when possible. Explore, analyze, and utilize such data in a hands-on way, using a variety of software tools. Significantly driven by a set of case problems as opposed to systematic coverage of methodologies.
Prerequisites & course information
Requisites: OTM 300 and ( GEN BUS 306 , ECON 310 , MATH 331 , MATH/STAT 309 , 409, or 431 ), or declared in undergraduate Business Exchange program Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. Recognize patterns and relationships that provide insights into business problems. Audience: Undergraduate 2. Choose and apply data-based forecasting methods, and evaluate the performance of such methods to inform operations decisions. Audience: Undergraduate 3. Engineer spreadsheet models for decision-support leveraging optimization and simulation tools. Audience: Undergraduate 4. Apply critical thinking to complex real-world problems by formulating appropriate questions, making implementable assumptions, and interpreting results of analyses. Audience: Undergraduate 5. Work effectively in teams on analytics projects. Audience: Undergraduate 6. Communicate analytics and insights through presentations and written reports. Audience: Undergraduate 7. Develop basic skills using visualization software (e.g., Tableau or Power BI) and a statistical analysis/programming language (e.g., R or Python). Audience: Undergraduate 8. Develop intermediate skills using Excel. Audience: Undergraduate Learning Outcomes: 1. Recognize patterns and relationships that provide insights into business problems. Audience: Undergraduate 2. Choose and apply data-based forecasting methods, and evaluate the performance of such methods to inform operations decisions. Audience: Undergraduate 3. Engineer spreadsheet models for decision-support leveraging optimization and simulation tools. Audience: Undergraduate 4. Apply critical thinking to complex real-world problems by formulating appropriate questions, making implementable assumptions, and interpreting results of analyses. Audience: Undergraduate 5. Work effectively in teams on analytics projects. Audience: Undergraduate 6. Communicate analytics and insights through presentations and written reports. Audience: Undergraduate 7. Develop basic skills using visualization software (e.g., Tableau or Power BI) and a statistical analysis/programming language (e.g., R or Python). Audience: Undergraduate 8. Develop intermediate skills using Excel. Audience: Undergraduate
Sources
- Course catalogue ↗2026-2027