COURSE CURRICULUM

The B.S. in Data Science is designed for students who plan to pursue advanced degrees and engage in original research in data science, with a strong emphasis on the rigorous mathematical and computational foundations underlying modern data-driven methods. The program requires completion of at least 128 credits, including University Discovery requirements, a set of 20 core major courses, a capstone experience, and an approved minor chosen with advisor guidance. Students must maintain a minimum GPA of 2.0 in all required computer science and mathematics courses, and key foundational courses such as CS 415, CS 416, CS 420, CS 457, and CS 515 must be completed with a grade of C- or higher. Consistent academic progress is expected, and students are allowed up to two repeats of these critical courses, after which they may be dismissed from the program, although reinstatement may be requested after a one-year absence. Transfer students may apply up to 32 credits toward major requirements, excluding Discovery courses. The curriculum integrates extensive study in mathematics, including calculus, multivariable calculus, linear algebra, probability, statistical inference, and mathematical proof, along with advanced computer science topics such as programming, data structures, algorithms, data science fundamentals, statistical learning, machine learning, and database or scalable data systems. Students also complete a capstone project or thesis that emphasizes research and applied problem-solving. In addition, each student must complete a minor in a related field such as applied mathematics, statistics, economics, or artificial intelligence, ensuring interdisciplinary depth. Overall, the program provides a strong theoretical and analytical foundation aimed at preparing students for graduate study, research careers, and advanced roles in data science.