MIT Sloan Analytics Capstone

A 7-month experiential AI/ML/optimization project where MIT Master of Business Analytics (MBAn) students collaborate with companies to tackle real business challenges.
 

• Team Structure: 2 MBAn students + 1 MIT faculty advisor
• Timeline:
  • Spring: 12 weeks, part-time (remote)
  • Summer: 10 weeks, full-time (on-site/hybrid/remote)
• Deliverables: 30-page report, code handover, final presentation & poster at MIT Capstone Showcase

All of our projects follow the same timeline, in line with our academic year. Our cycle begins in January when all of our partners pitch their project to the students, and end in August at the Capstone Showcase.

Key Dates for Capstone 2027

• Nov 1, 2026: Capstone Project Submissions Due
• Nov 5, 2026: Capstone Fall Preview Night (In Person)
• Jan 14, 2027: Company Pitch Day (In Person)
• Feb 16, 2027: Projects begin
• Aug 20, 2027: MIT Analytics Capstone Showcase

We’re in the business of analytics, so it won’t surprise you that we have an algorithm! We allow both student teams and company partners to rank their top choices to find the best possible matches for all.

The financial cost per project is $65,000 (1-year or 3-year term). This funding covers your team of two students for 7 months, as well as support from the faculty adviser and our dedicated staff. The majority of this investment goes directly to our students in the form of tuition and travel assistance.

The company receives full IP rights. 

We welcome multi-year projects that student teams advance a little at a time. We also welcome multi-year contracts with a new project each year. We even have a few great perks that come with longer term contracts that we think you’ll really love.

Yes! We have several long-term partners who host two projects each year. The only limit is what you are able to imagine, and of course, the data to make it work. Our team will work with you to scope more than one potential project if you’d like.

We work with companies across industry verticals and functions, and prioritize problems that require advanced depth of analysis, and have ensured availability of rich data at the outset of the project (whether internal or 3rd party). We also prefer projects that  have high business relevance and value, and a commitment from the company to mentor the students and support their progress.