Masters (MS) in Data Science Graduate Degree Program
A 30-credit career-focused data science master’s degree with in-person and online program options.
Labor Insight™: Source: Burning Glass Technologies. Labor Insight™. 2022.
The University of Connecticut offers both on-campus and online Master of Science in Data Science degree programs designed to provide students with a comprehensive education in data analysis and its application to multiple industry sectors and fields of practice. The program places 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.
Online MS in Data Science Program
The online MS in Data Science program at UConn provides students the flexibility to study from anywhere, anytime, and at their own pace (part-time) while still receiving access to the same curriculum as the on-campus program. Focus on courses covering foundational data science knowledge, as well as discipline-informed, industry-aligned specialty electives.
On-Campus MS in Data Science Program
The on-campus MS in Data Science program at the University of Connecticut is an 11-month, cohort-based, full-time program that provides students with a multidisciplinary, 30-credit, career-driven curriculum focused on foundational data science knowledge and skills. This program is eligible for F-1 and J-1 visa sponsorship and is a STEM OPT approved program.
UConn’s MS in Data Science does not offer graduate assistantships or scholarships at this time.
Two Great MSDS Options - Same Great Outcome
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 Master's in Data Science, graduates will be able to:
- Identify and collect appropriate data to support robust inferences and predictions to facilitate decision making.
- Gather, manage, clean, merge, transform, and summarize data from disparate sources using programming and scripting tools.
- Visualize complex data sets to support analysis and prediction and to support decision making by end-users.
- Conduct associational and causal analyses using modeling approaches, computational statistical learning techniques, and best practices across domains.
- Apply machine learning and artificial intelligence algorithms to large, heterogeneous, unstructured data sets to achieve optimal accuracy levels on test data.
- Perform big data analytics through the efficient use of algorithms, high-performance computing, and out-of-core computing in cloud computing environments.
- Evaluate 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 and cross-domain collaboration to produce more accurate modeling, analysis, and predictions.
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.
Data Science Master's Core Courses (21 credits)
Core Courses:
- GRAD 5100* - Fundamentals of Data Science (3 credits) NEW
- STAT 5405 - Applied Statistics for Data Science (3 credits)
- STAT 5125 - Statistical Computing for Data Science (3 credits)
- CSE 5819 - Introduction to Machine Learning (3 credits)
- CSE 5713 - Data Mining and Management (3 credits)
- OPIM 5501 - Data Visualization and Communication (2 credits)
- EPSY 5641 - Research Design and Measurement for Data Science (2 credits)
- ARE 5353 - Data Ethics and Equity (2 credits)
*Subject to Graduate Executive Council Approval
Data Science Master's Core Electives (6 credits) — Choose 2 courses. In-person at the Storrs campus unless (otherwise specified)
Most Popular Electives:
- STAT 5825 - Applied Time Series (3 credits)
- STAT 5845 - Applied Spatio-Temporal Statistics (3 credits)
- EPSY 5643 - Text Analytics (3 credits)
- BIST 5625 - Introduction to Biostatistics (3 credits)
- CSE 5800 - Bioinformatics (3 credits)
- CSE 5850 - Introduction to Cyber-Security (3 credits)
- OPIM 5512 - Data Science using Python (online) (3 credits)
- OPIM 5604 - Predictive Modeling (online) (3 credits)
Other Specialty Electives:
In-person at the Storrs campus unless (otherwise specified). Enrollment in specialty electives must be approved by the student’s academic advisor. Many specialty elective courses require prior discipline-specific academic experience and/or academic prerequisites.
- STAT 5415 - Statistical Methods for Data Science (3 credits)
- STAT 5665 - Applied Multivariate Analysis (3 credits)
- STAT 5675 - Bayesian Data Analysis (3 credits)
- BIST 5615 - Categorical Data Analysis
- BIST 5645 - Concepts and Analysis of Survival Data (3 credits)
- BIST 5815 - Longitudinal Data Analysis (3 credits)
- OPIM 5502 - Big Data Analytics with Cloud Computing (online) (3 credits)
- OPIM 5509 - Introduction to Deep Learning (online) (3 credits)
- OPIM 5511 - Survival Analysis with SAS (online) (3 credits)
- OPIM 5512 - Data Science Using Python (online) (3 credits)
- CSE 5312 - Architecture of Internet of Things (3 credits)
- CSE 5500 - Algorithms (3 credits)
- CSE 5825 - Bayesian Machine Learning (3 credits)
- CSE 5815 - Systems Biology: Constructing Biological Knowledgebase (3 Credits)
- CSE 5840 - String Algorithms and Applications in Bioinformatics (3 Credits)
- CSE 5860 - Computational Problems in Evolutionary Genomics (3 Credits)
- CSE 5299 - Computer Networks and Data Communication (3 credits)
- CSE 5300 - Advanced Computer Networks (3 credits)
- CSE 5304 - High-Performance Parallel Computing (3 credits)
- CSE 5309 - Networked Embedded Systems (3 credits)
- CSE 5852 - Modern Cryptography: Foundations (3 credits)
- CSE 5854 - Modern Cryptography: Primitives and Protocols (3 credits)
- EPSY 6611 - Hierarchical Linear Modeling (3 credits)
- EPSY 6615 - Structural Equation Modeling (3 credits)
- NRE 5525 - Remote Sensing of the Environment (online) (3 credits)
- NRE 5585 - Python Scripting for Geospatial Analysis (online) (3 credits)
- NRE 5215 - Introduction to Geospatial Analysis with Remote Sensing (online) (3 credits)
- NRE 5545 - Quantitative Remote Sensing Methods (online) (3 credits)
- NRE 5560 - High Resolution Remote Sensing: Application of UAS & LiDAR (online) (3 credits)
- NRE 5235 - Remote Sensing Image Processing (online) (3 credits)
Culminating Capstone Project (3 credits)
- GRAD 5800 Applied Capstone in Data Science (3 credits)
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 Master's in Data Science, graduates will be able to:
- Identify and collect appropriate data to support robust inferences and predictions to facilitate decision making.
- Gather, manage, clean, merge, transform, and summarize data from disparate sources using programming and scripting tools.
- Visualize complex data sets to support analysis and prediction and to support decision making by end-users.
- Conduct associational and causal analyses using modeling approaches, computational statistical learning techniques, and best practices across domains.
- Apply machine learning and artificial intelligence algorithms to large, heterogeneous, unstructured data sets to achieve optimal accuracy levels on test data.
- Perform big data analytics through the efficient use of algorithms, high-performance computing, and out-of-core computing in cloud computing environments.
- Evaluate 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 and cross-domain collaboration to produce more accurate modeling, analysis, and predictions.
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.
Concentrations
UConn's MS in Data Science Graduate Program offers twelve concentrations for advanced students ranging from Biostatistics to Cybersecurity to Business Data Science. Many students choose not to pursue a concentration.
Interested in Creating Your Own Track?
You have options to create a track to suit your specific needs.
