COURSE CURRICULUM

The Master of Science degree in Data Science requires students to complete a combination of core courses, electives, and a culminating experience to total at least 30 credit hours. Core courses focus on advanced database systems design, data mining techniques, big data analytics, applied statistics, computational statistics, and statistical learning. Students then choose 6 credit hours of major electives from a broad array of options, including artificial intelligence, big data management, computational health informatics, econometrics, environmental and public health topics, geographic information science, health informatics, probability and stochastic processes, and quantitative statistical analysis. This allows students to tailor their learning to specialized areas of interest while reinforcing essential data science skills. The program culminates with one of several options: a thesis, a capstone project combined with a graduate internship, or a capstone project with an approved graduate course, ensuring practical application of data science methods and experience in research or industry. This curriculum balances theoretical knowledge with hands-on, real-world problem solving, preparing graduates to leverage statistical, computational, and analytical tools to extract insights from complex datasets. Graduates are equipped to handle large-scale data analysis, develop predictive models, and use advanced visualization techniques, all while addressing challenges across multiple domains, including health, business, environmental science, and technology. The program emphasizes the integration of mathematics, computer science, and domain-specific applications, ensuring students can apply rigorous methods to solve real-world problems. By combining foundational coursework, elective specialization, and a culminating experience, students develop proficiency in critical data science areas and the flexibility to adapt their expertise to evolving industry and research needs. This program cultivates data scientists capable of designing analytical workflows, managing and interpreting large datasets, and providing actionable insights across a wide range of professional contexts.