Capstone Projects in Data Science - Spring 2025 / Summer 2025

The University of Virginia
Charlottesville, Virginia, United States
Associate Professor
4
Timeline
  • February 4, 2025
    Experience start
  • June 1, 2025
    Progress report
  • August 1, 2025
    Final Deliverable Submission
  • August 9, 2025
    Final Presentations
  • August 16, 2025
    Experience end
Experience
3/3 project matches
Dates set by experience
Preferred companies
United States
Any company type
Any industries

Experience scope

Categories
Data visualization Data analysis Data modelling Project management Data science
Skills
programming languages and tools data analysis and manipulation data visualization machine learning algorithms deep learning and neural networks predictive modeling feature engineering data engineering python (programming language) project management
Learner goals and capabilities

The University of Virginia School of Data Science seeks proposals from academic, commercial, healthcare, nonprofit, and government organizations interested in sponsoring student capstone projects. Capstones are consulting projects that allow students to gain real-world experience in analysis, machine learning, and data engineering. Sponsors are vital partners in these projects, providing a clear objective, data sets, guidance, and orientation to the teams. Capstone projects allow sponsors to explore critical business questions while contributing to the development of the data science workforce.


Capstone projects challenge UVA Data Science students to explore, integrate and analyze data to solve real-world problems. Each team of 3-5 students is advised by a faculty member who has experience in the techniques and data types specific to the project. This collaborative model, delivered in partnership with Riipen, supports employers to build their talent pipeline, tackle immediate business challenges, expand their capabilities, and enhance their brand presence.


Each capstone cycle runs for two semesters, where the first semester typically has students holding discovery meetings with the sponsor and faculty mentor, forming a project plan, onboarding, and reviewing the data. The second semester has students getting into high gear on the project. If you have a bigger project that requires two full semesters of work, you can indicate this here.


All members of the project teams will have completed courses in computational methods for data science, machine learning, and exploratory data analysis, and will possess skills in programming in R and Python. During the program, students also learn feature engineering, data cleaning and wrangling, deep learning, data ethics, project management, team building, technical writing, and oral communication.


UVA policies state that student work performed in a class or capstone is the intellectual property of the learner, not the university or the sponsor.  Sponsors wishing to modify these terms must request that learners sign an IP agreement.  The University can not assign IP it does not own. Sponsors wishing to ask students to sign over IP must provide the agreement language by the contract review deadline.


If you have questions or need help drafting your project content, please review our FAQs or email us at DataCapstones@virginia.edu.

Learners

Learners
Graduate
Intermediate, Advanced levels
10 learners
Project
100 hours per learner
Educators assign learners to projects
Teams of 4
Expected outcomes and deliverables

Sponsoring organizations gain valuable insights about their operations in tangible reports and other deliverables. Each project has multiple deliverables:

  • A project plan detailing the scope of the problem and plan of work.
  • Progress reports
  • Data and code artifacts (e.g., github repo)
  • The project ends with a paper and a technical presentation.


These deliverables ensure the quality of the work and provide a vehicle for the students to communicate their results to the sponsors and to contribute to the knowledge and understanding of the data science community.

Project timeline
  • February 4, 2025
    Experience start
  • June 1, 2025
    Progress report
  • August 1, 2025
    Final Deliverable Submission
  • August 9, 2025
    Final Presentations
  • August 16, 2025
    Experience end

Additional company criteria

Companies must answer the following questions to submit a match request to this experience:

  • Q1 - Checkbox
     *
  • Q2 - Checkbox
     *
  • Q3 - Text long
    Please provide a brief description of the data set learners will be working with. Include information on the size of the data set and how you will share it.  *
  • Q4 - Text long
    Are the learners required to use any particular platform, environment, language, etc. to work with the data? If yes, please provide specifics below.  *
  • Q5 - Multiple choice
    Are the tools and technologies used as part of this project available as free or open-source software?  *
    • Yes
    • No
  • Q6 - Multiple choice
    Can the products generated through this project be governed by an Open or Creative Commons license?  *
    • Yes
    • No
  • Q7 - Multiple choice
    Do you envision this project requiring two full semesters of work from the students?  *
    • Yes
    • No
  • Q8 - Text long
    Are there any constraints on data handling? Is the data proprietary?  *
  • Q9 - Multiple choice
    Is a data use agreement required?
    • Yes
    • No
  • Q10 - Text long
    Does the data contain any personally identifiable information (PII) or protected health information (PHI)? Any other sensitive information? Any information governed by defense or export controls restrictions?  *
  • Q11 - Text long
    What, if any, data security specifications will be required of any device on which this data is stored/analyzed?
  • Q12 - Document
    Has the data been reviewed by an IRB? Has it undergone any other ethical or legal review? Please provide a copy of approval document or explain why such review has not occurred.
  • Q13 - Multiple choice
    Do learners have to be US citizens?  *
    • Yes
    • No
  • Q14 - Multiple choice
    Do learners/mentors have to possess any kind of security clearance or undergo a background check?  *
    • Yes
    • No
  • Q15 - Document
    Will learners be required to sign any agreement to access the data? For example IP release or NDA? If so, please attach a copy.