Data Science, M.S. (Online)
This is a non-thesis program, requiring 30 credit hours and a culminating experience that demonstrates the student’s ability to apply data science concepts to solve a complex, relevant real-world problem. Students are able to choose from areas of interest such as business, biology or the environment.
If you are in need of additional basic skill building before starting the program, URI Online offers two bridge courses that can be taken in the summer or fall.
Ten Required Courses
DSP555: Multivariate Statistical Learning for Data Science
(3 crs.) Multivariate data organization and visualization, multivariate distributions, tests of hypotheses on mean vectors, multivariate regression and classification, penalized regression, tree-based methods, principal component analysis, clustering, cross-validation, and bootstrapping.
DSP556: Machine Learning for Data Science
(3 crs.) Survey of traditional and newly developed machine learning techniques from an applied perspective, with emphasis on applications to a variety of domains.
DSP562: Data Analytics & Visualization
(3 crs.) Basic principles and approaches to data visualization, as well as essential methods for data acquisition, cleaning, and aggregation, using Python programming language and Tableau, a powerful, interactive data visualization application.
DSP563: Applied Mathematics in Data Science
(3 crs.) Introduction to mathematical foundations necessary to effectively study problems in data science and machine learning. Use linear algebra and optimization to pose and solve modern problems leveraging data from diverse applications.
DSP565: Computational Statistics
(3 crs.) Statistical methods using R: Empirical Bayes, James-Stein, regression trees, EM algorithm, Jackknife and bootstrap, cross validation, large-scale hypothesis testing, false discovery rate, sparse modeling, LASSO, random forest, boosting, deep learning
DSP566: Advanced Topics in Machine Learning
(3 crs.) Students build a thorough understanding of state -of-the art ML algorithms, including techniques that enable understanding of when and why machine learning works in order to design such algorithms.
DSP567: Database Concepts, Cloud Computing, and Big Data
(3 crs.) Foundational and recent inventions in data management research. Topics include, indexing, query processing, query languages, NoSQL databases, spatial databases, data warehousing, business intelligence, and big data frameworks.
DSP568: Data Science for Business
(3 crs.) Apply Data Science techniques to business functions, including decision-making, finance, accounting, marketing, operations management, information technology, human resources, etc. Includes data visualization and machine learning in a business context.
DSP569: Applications of Data Science in Biological Science
(3 crs.) Use data science techniques in data-enabled research in a topic within the biological and life sciences.
DSP557: Interdisciplinary Data Enabled Research/Capstone
(3 crs.) Students apply theoretical knowledge acquired during the Data Science Certificate program to a project involving actual data in a realistic setting. A Team-based capstone data project will provide real-world experiences of data-driven research for students.
Get in touch.
URI Online Student Support Center
401.874.5280
Program Director
Nancy Eaton, Professor & Director of Data Science
401.874.4107
neaton@uri.edu