COURSE CURRICULUM

The Master of Science (MS) in Data Science at Wichita State University is a 30-credit graduate program designed to build strong expertise in machine learning, artificial intelligence, statistical modeling, and applied data analytics for high-demand careers in technology and research. The program includes a structured set of core courses that establish foundational and advanced skills, including Perspectives on Data Science (CS 746), Introduction to Business Analytics (BSAN 775), Introduction to Data Analytics (MATH 746), Machine Learning (CS 770), and a Capstone Project in Data Science (CS 896), which provides a culminating applied experience integrating theory with real-world problem solving. Beyond the core, students complete 9 credit hours of data science electives selected from advanced topics such as artificial intelligence, deep learning, neural networks, computer vision, image analysis, graph algorithms, database systems, and robotics-oriented AI, allowing specialization in cutting-edge computational areas. In addition, students take 6 credit hours of discipline electives that may include courses in statistics, regression analysis, Bayesian methods, data visualization, database management, big data analytics, sports analytics, engineering analytics, or prescriptive analytics, depending on career goals and advisor approval. The program emphasizes flexibility while maintaining a strong technical foundation, ensuring students gain both theoretical understanding and practical implementation skills. Students are encouraged to consult the graduate coordinator before selecting electives to ensure alignment with academic and career objectives, and substitutions are only allowed under strict approval conditions. The program does not permit research thesis credits such as CS 891, CS 892, or CS 893, reinforcing its applied, coursework-and-project-focused structure. Overall, the MS in Data Science prepares graduates for careers in machine learning engineering, data science, AI development, analytics consulting, and research roles, while also providing a strong foundation for further doctoral studies in computational and data-driven fields.