| This course extends the foundations of data-driven decision-making by applying mathematical models to complex, real-world, interdisciplinary problems with greater analytical depth and rigor. The course emphasizes careful interpretation of results and effective communication of technical concepts to both technical and general audiences. Students engage a broad array of mathematical concepts while developing creativity, judgment, and critical thinking in data-rich environments. Specific topics may include advanced data analytics, classification and prediction methods, optimization, linear algebra (matrices, vectors, eigenvalues), and network science topics. Students use technology to visualize data, explore and validate competing models, and perform computationally intensive analyses, enabling deeper focus on modeling decisions, interpretation, and responsible use of quantitative methods in decision-making contexts. *This is a pilot course and must undergo review by the Curriculum Committee NLT AY27-2 to continue.* |