The University of Connecticut’s (UConn) Master’s (MS) in Data Science is an 11-month cohort-based on-campus professional graduate degree. The 30 credit-hour program has an integrated multidisciplinary core of seven courses (18 credits) that provide foundational data science knowledge and skills. Students develop additional domain-focused expertise by choosing from among one of 12 industry-aligned concentrations or by developing an individualized pathway from the extensive number of available elective courses. The program places an extensive focus on data ethics and equity throughout the curriculum. All students are required to complete a 3 credit hour applied industry-based capstone project that builds and showcases data science skills gained through core and elective courses.
Learner and outcome focused, UConn's Master's in Data Science develops core competencies that are aligned with workforce and industry needs.
Upon conclusion of the program, graduates will be able to:
- Utilize proper methods of data collection that support robust inferences and predictions that facilitate decision making
- Use programming and scripting tools to gather, manage, clean, merge, transform, and summarize data from disparate sources,
- Visualize complex data sets to support analysis and prediction and to support decision making by end-users,
- Develop proficiency in modeling approaches and computational statistical learning techniques for associational and causal analysis across domains,
- Develop and use machine learning and artificial intelligence algorithms to make predictions from large, heterogeneous, unstructured data sets,
- Develop proficiency in big data analytics and efficient application of algorithms using cloud computing and be familiar with high-performance computing and out-of-core computing,
- Evaluate and assess the reliability and validity of inferences and predictions,
- Communicate analytic insights across different domains using a variety of data visualization strategies and tools,
- Incorporate best practices for project and data management and documentation in collaborative team environments,
- Evaluate the ethical, legal, and social impacts of the data science process, including considerations of diversity, equity, inclusion, data privacy, data security, and data ownership within a broader social and international context, especially in addressing systemic biases and inequities,
- Integrate domain-specific knowledge through teamwork and cross-domain communication and translation
Designed to be one of the most multi-disciplinary, results driven data science master’s degrees in the US.
UConn offers one of the best-value, applied data science master’s degrees in the US. Students develop advanced knowledge and proficiency in core areas of data science and competencies in specialized domains through focused industry-aligned concentrations and practical hands-on applied learning and capstone projects. Special emphasis is placed on data ethics and equity. Courses are taught by UConn’s distinguished faculty from five Schools and Colleges including the School of Business, School of Engineering, College of Liberal Arts and Sciences, College of Agriculture, Health, and Natural Resources, and the Neag School of Education.
UConn’s Master’s in Data Science is an 11-month cohort based full-time in-person program and is eligible for F-1 and J-1 visa sponsorship. Courses are offered on the University of Connecticut’s Storrs, CT USA Campus. This program is eligible for the STEM OPT extension that affords certain F-1 graduate students an opportunity to apply for a 24-month extension of their post-completion optional practical training (OPT). The program does not offer graduate assistantships and scholarships at this time.
An MS in Data Science From a Top-Ranked (R1) University
The University of Connecticut is ranked in the Top 25 Public Schools by US News & World Report and is accredited by the New England Association of Schools and Colleges.
UConn's MS in Data Science Graduate Program offers twelve concentrations ranging from Biostatistics to Cybersecurity to Business Data Science.