Master of Science in Business Analytics and AI | Blended Learning & Capstone

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Blended Learning and Capstone

The MS in Business Analytics and AI program is based on a blended learning approach combining in-person classroom time and independent study.

Modules are divided into three phases: pre-module, module, and post-module.

Pre-module: Students are provided with pre-work for each upcoming course, including things like: Readings Tutorials Pre-recorded talks. Module: Students attend in-person sessions with the other members of the cohort and complete 1 course each 2-3 days. Post-Module: Students work through post-module work, integrating the new concepts they have learned and completing assessments, group projects, exams, etc.


Pre-module: Research Phase
This begins 4-6 weeks prior to the in-class teaching session. Students prepare for the module by completing qualitative and quantitative assignments that include readings and cases, tutorials and diagnostics, and exercises and written assessments. Completion of the pre-module material ensures a more rigorous and productive classroom experience. All learning materials will be posted to our online Learning Management System.

Module: In-Person Class Sessions
This will be an intensive, full-time period of rigorous in-class learning to absorb the advanced material and actively collaborate with your peers and faculty. Each module contains special events, guest speakers, and other opportunities to engage with your MSBAi cohort and alumni community. Students are required to attend class during normal business hours each day with some additional evening engagements.

Post-module: Application
The post-module is designed as the implementation phase of the module. Students are challenged to apply the material and concepts covered during the pre-module phase and residential period to solidify their learning. Deliverables are in the form of case studies, written assignments, projects, and group work.

Throughout the pre- and post-module phase, students will be part of study groups that encourage collaboration and peer-to-peer learning.

Capstone

The MSBAi Capstone is an integrative year-long team project that gives students the opportunity to demonstrate an understanding of the core competencies taught throughout the program and apply them to real business concerns. The result is a unified and practical case presentation on a topic of the group's choosing.

Capstone work runs throughout the duration of the program, starting in Pre-Module 1. Capstone includes deliverables assigned from specific courses, as well as a charter, executive summary, first draft, and final draft. Students will practice presenting Capstone materials at various stages throughout the year leading up to the final presentation in Module 6. 

Project Framework

  • Each group will consist of 4–5 participants from diverse backgrounds, encouraging a broader understanding of business analytics.
  • The project will span the course of the program, enabling participants to enrich their projects with learning from each module.
  • The final deliverables for the project consist of a paper and final presentation. 

 

How Capstone Projects and Teams are Formed

The Capstone begins with students developing and pitching their own project ideas, often inspired by business challenges, professional experiences, or areas of personal interest. During the opening module, students share their ideas with classmates and have opportunities to connect with others who share similar interests.

From there, students form teams of 4–5 participants and work together to refine their project ideas, considering factors such as business impact, data availability, and feasibility. Each team develops a project charter outlining its proposed approach.

Throughout the program, teams work closely with faculty advisers who provide guidance and feedback as students apply what they are learning in the classroom to their projects. The Capstone is a required credit bearing year-long course experience that culminates in a final written report and presentation.

Featured Capstone Projects

Watts the Problem

This capstone examines price volatility across California counties to assess whether grid pressures create disproportionate burdens on vulnerable populations. The project builds a scalable, policy-relevant data infrastructure using entirely public sources and open-source tools.

View Capstone Project

DriftBreaker

DriftBreaker implements a discrete-time survival analysis framework for consumer credit default prediction. The framework integrates macro-economic indicators through an EWMA overlay, enabling the model to adapt predictions based on prevailing economic conditions. 

View Capstone Project

Examples of Past Projects

In addition to our featured Capstones, view additional projects from the Class of 2026 below.

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