Bridging Operations Research and Data Science in an Undergraduate Curriculum

Abstract

This paper presents the design and implementation of a project-based undergraduate course that integrates systems modeling and operations research to prepare students for data-driven decision-making. Developed within an applied mathematics program, the course addresses a curricular gap between statistical modeling and optimization, equipping students with practical skills in variable selection, causal reasoning, model inference, and mathematical programming. Putting emphasis on interpretability and communication, the course challenges students to build models that explain system behavior, not just make predictions, and to translate findings into actionable insights for diverse stakeholders.Students engage with real-world data through assignments in sports analytics, personalized nutrition planning, and an integrative final project on basketball roster optimization. They learn to use tools such as MATLAB and AMPL, applying machine learning methods, sensitivity analysis, and linear/integer programming to authentic problems. By combining subject-matter expertise with data analysis and optimization, the course fosters the development of broadly transferable skills in applied modeling. The course structure and assignments are adaptable to a wide range of undergraduate programs in mathematics, data science, and engineering.