MSBA Practicum & BSBA Capstone Projects | GW School of Business

The MSBA practicum and BSBA capstone projects are designed to apply analytics knowledge gained in the classroom to real-world problems.

In these one-semester courses, students work in teams on a sponsor’s project to receive academic credits. External industry sponsors propose the project and work closely with the students over the semester.

How It Works

GW School of Business students conduct a pro-bono project proposed by an industry sponsor. With the help of their industry sponsor and GW faculty mentors, students embark on a full-cycle data analytics project using tools provided by GW and the sponsor. Projects are often based on mock data or publicly available data; students do not need access to proprietary information. Teams of several students are matched to accepted projects, and the industry sponsors meet with the team and provide guidance and feedback for the duration of the project.

These projects are not official internships. Some project sponsors require students to sign non-disclosure agreements (NDAs). In some projects, software licenses or use of premium cloud-based software may be required to conduct in-depth analysis, create complex models, etc. Any costs incurred will be presented to sponsors as a budget before work begins.

How Sponsorship Benefits Your Company

These projects offer:

The opportunity for companies to look at a problem from a fresh perspective.
Access to a small team of junior-level data scientists and developers to prototype a working solution to that problem.
The right to utilize any work product or IP developed by the student team through the project.
Volunteer or professional development opportunities for sponsor mentors.

Acceptance Criteria for Sponsorship

Submitted projects are assessed for feasibility and quality.

Positive considerations for feasibility include:
- Use of public data, or data that is stored and controlled by the sponsor with access provided to students.
- Use of standard computing technologies, or specialized computing technology that is provided by the sponsor.
- Medium complexity and difficulty levels that challenge a student group, but are realistically achievable in the course of a semester.
- Exposure to novel technologies and approaches.
Positive considerations for quality include:
- Polished proposals.
- Proposals that align with industry standards or best practices.
- Proposals that align with ethical and responsible AI practices.

Learning Objectives for Students

After completing these projects, students will be able to demonstrate the following abilities:

Design, develop, and execute a data-driven investigation or prototype.
Frame a data-driven study based on a client’s requirements and the available data.
Identify the value proposition for a project and the appropriate questions that will inform progress.
Document and describe the trade-offs involving the data or technology, its quality and availability, depth of analysis, and available resources.
Convey project deliverables in different modes — one for academic requirements and one for project sponsor.

Student Responsibilities

Engage in regular sessions with the sponsor to thoroughly understand scope of work, including (but not limited to): clarifying specific project requirements, gathering feedback, and discussing progress and any challenges.
If and when the sponsor introduces new topics, methodologies and/or technologies, students must conduct their own research to enhance their understanding.
In challenging situations, students must work collaboratively with the sponsor and make use of any academic resources available to them.
Students are expected to maintain a high level of professionalism throughout the project.
At the conclusion of the project, each team is required to submit a deliverable based on the sponsors’ requirements.

Submit a Spring 2025 Project Proposal

Project sponsor proposals are due by Monday, January 6, 2025. Acceptance decisions will be announced by Friday, January 10.

Propose a Project

Explore Our Fall 2023 MSBA Practicum Projects

The 2023 Fall Analytics Practicum included 12 sponsoring organizations, 13 student projects, and 54 graduate and undergraduate students. Sponsors ranged from some of the largest banks and professional services companies in the world to specialized software providers and D.C.-based contractors and consultants.

Practicum projects focused on timely and meaningful topics such as:

Analysis of telehealth services and social determinants of health
Auditing LLM systems
Building AI-based chatbots
Climate change resiliency
COVID-19 impacts on nursing homes
Cybersecurity analytics
Financial fraud detection
LLM applications in consumer finance
Sales forecasting
Strategic data management initiatives
Tracking and risk-scoring large cargo ships

Nine out of 13 organizations expressed a strong interest in sponsoring again in the future, and three out of 13 organizations noted some interest in sponsoring in future semesters. See below for additional details relating to the 13 projects.

Social Determinants of Health and Access to Credit Impact on Life Expectancy

Congratulations to Danyu Z., Yachen Wu (Kendall Wu), Rodrigo R. Jaar and Swapnil Duggal on an exceptional project with FI Consulting focusing on the impact of social determinants of health and access to credit on life expectancy. The group used open tools like Python, PiML, and Neo4j to train explainable regression and graphical models for complex health issues.

Project sponsor: FI Consulting