M.S. in Data Science students are required to complete a capstone project. Capstone projects challenge students to acquire and analyze data to solve real-world problems. Project teams consist of two to four students and a faculty advisor. Teams select their capstone project at the beginning of the year and work on the project over the course of two semesters. 

Most projects are sponsored by an organization—academic, commercial, non-profit, and government—seeking valuable recommendations to address strategic and operational issues. Depending on the needs of the sponsor, teams may develop web-based applications that can support ongoing decision-making. The capstone project concludes with a paper and presentation.

Key takeaways:

  • Synthesizing the concepts you have learned throughout the program in various courses (this requires that the question posed by the project be complex enough to require the application of appropriate analytical approaches learned in the program and that the available data be of sufficient size to qualify as ‘big’)
  • Experience working with ‘raw’ data exposing you to the data pipeline process you are likely to encounter in the ‘real world’  
  • Demonstrating oral and written communication skills through a formal paper and presentation of project outcomes  
  • Acquisition of team building skills on a long-term, complex, data science project 

Capstone projects have been sponsors by a variety of organizations and industries, including: Capital One, City of Charlottesville, Deloitte Consulting LLP, Metropolitan Museum of Art, MITRE Corporation, a multinational banking firm, The Public Library of Science, S&P Global Market Intelligence, UVA Brain Institute, UVA Center for Diabetes Technology, UVA Health System, U.S. Army Research Laboratory, Virginia Department of Health, Virginia Department of Motor Vehicles, Virginia Office of the Governor, Wikipedia, and more. 

Sponsor a Capstone Project 

View previous examples of capstone projects and check out answers to frequently asked questions. 

What does the process look like?

What is the seminar approach to mentoring capstones?

Do all capstone projects have sponsors?

Why do we have to work in groups?

I don’t like the topic area of the capstone project I received during the algorithm matching. What can I do?

Why don’t we have a say in the capstone topics?

Can I work on a project for my current employer?

If my project doesn’t have a corporate sponsor, am I losing out on a career opportunity?

What is the SIEDS conference and is there an equivalent for the online program?

Capstone Project Reflections From Alumni

Gabriel Rushin, MSDS 2017
Gabriel Rushin, MSDS 2017

“Capstone projects are opportunities for you to deliver valuable, quantifiable results that you can use as a testimony of your long-term project success to the company you work for and other companies in future interviews.” — Gabriel Rushin, MSDS 2017, Procter & Gamble, Senior Machine Learning Engineer Manager

Colleen Callahan
Colleen Callahan, Online MSDS 2021

“For my capstone project, I worked to develop a clustering model to assess biogeographic ancestry, using DNA profiles. I felt like I was finally doing real-world data science and loved working with such an important organization as the Department of Defense.” — Colleen Callahan, Online MSDS 2021, Associate Research Analyst, CNA (Arlington, Virginia)

Capstone Project Reflections From Sponsors

“For us, the level of expertise, and special expertise, of the capstone students gives us ‘extra legs’ and an extra push to move a project forward. The team was asked to provide a replicable prototype air quality sensor that connected to the Cville Things Network, a free and community supported IoT network in Charlottesville. Their final product was a fantastic example that included clear circuit diagrams for replication by citizen scientists.” — Lucas Ames, Founder, Smart Cville

“Working with students on an exploratory project allowed us to focus on the data part of the problem rather than the business part, while testing with little risk. If our hypothesis falls flat, we gain valuable information; if it is validated or exceeded, we gain valuable information and are a few steps closer to a new product offering than when we started.” — Ellen Loeshelle, Senior Director of Product Management, Clarabridge