• data_science_degree_draft_1.png
  • big_data_long_term_news_template.png
  • sample_projects.png


The M.S. in Data Science program provides a strong foundation in the science of Big Data and its analysis by gathering in a single program the knowledge, expertise, and educational assets in data collection and management, data analytics, scalable data-driven pattern discovery, and the fundamental concepts behind these methods. Students completing this regular 2 year (31 credits) master's program will learn the state-of-the-art methods for treating Big Data, be exposed to the cutting edge methods and theory forming the basis for the next generation of Big Data technology, and will complete a project demonstrating that they can use the fundamental concepts to design innovative methods for new application areas arising from business, government, security, medicine, biology, physical sciences, and the environment.

The Post-Baccalaureate Certificate is a rigorous program consisting of 4 courses (12 credits) providing a solid conceptual foundation to Data Science. This program is designed for those who want to learn the fundamentals of Big Data analytics as part time students while working full time, as well as those who are looking for a short 1 year program as full-time students.

News iconNews

The Data Science Poster Fair will highlight the Capstone Projects being carried out by our M.S. students as part of their degree. This will be a good opportunity to see how Data Science can be applied to a wide range of topics, including social web, biology, medicine, statistical modeling, and... Read more »

M.S. in Data Science student Ryan Chan has been awarded the John T. Riedl Memorial Graduate Teaching Assistant Award for 2018. The award recognizes graduate... Read more »

Data Science student Akhil Bhargava received the best poster award at this year’s Data Science M.S. Poster Fair.  He was honored for his poster, “Fully Convolutional Network in Performing... Read more »

On June 5, the UMN REU Program on big data will kick off, providing 10 students the opportunity to collaborate with CS&E faculty on active research. Participants will be closely mentored by these faculty, and students will be provided with supplemental activities to support their research... Read more »