COURSE CURRICULUM

The Data Science degree program requires a minimum of 52 credits, with at least 30 to 32 of those being upper-division credits, and 8 supporting credits, totaling 60 credits overall. The core curriculum provides a comprehensive foundation in both theoretical and applied aspects of data science. Required courses include an introduction to data science, data wrangling, mathematics for machine learning, and machine learning itself. Students also explore ethical and practical dimensions of technology, and complete their studies with a capstone project that integrates their learning in a real-world application. Additional foundational courses in statistics, discrete mathematics, and advanced computer programming using Python ensure students are well-equipped with technical skills.Students must also select 16 elective credits from a wide range of courses across disciplines, allowing for customization based on interests and career goals. Options span computer science topics such as algorithms, robotics, operating systems, and cybersecurity, as well as mathematics courses including calculus, linear algebra, graph theory, probability, and statistics. There are also electives in business, economics, communication, philosophy, political science, and sport management, reinforcing the interdisciplinary nature of data science. These electives allow students to apply data skills in various domains such as business analytics, marketing research, econometrics, and sport analytics.To support the major, students must complete introductory programming and applied calculus or calculus I. This comprehensive structure prepares graduates to manage and analyze complex datasets, apply machine learning models, and draw meaningful insights across multiple industries. The program balances technical rigor with ethical considerations and real-world application, ensuring students graduate with not only strong data science skills, but also the ability to communicate results and drive informed decision-making.