Master of Science in Analytics
The data science capstone project is a degree requirement for students in our Master of Science in Analytics program. To complete them, students work in teams of two or three with a faculty advisor and a business partner to use data analytics to solve a key business problem. Teams may identify their own issue or pick one supplied by MScA industry research partners. The project is designed for both students and capstone partners to gain experience working on real-life data- and analytics- related challenges.
MScA Capstone Project Learning Objectives
- Develop the ability to design an analytics research project
- Frame a business problem in a way that can be addressed using analytics
- Identify the analytics tool or algorithm that will address this problem
- Develop a methodological framework to produce a practical solution
- Implement the analytics methodology
- Communicate the findings of the research effectively in both written and oral presentations
Students who have enrolled and/or completed six courses in the program may start working on their capstone projects. Once they have chosen their project and it has been approved, students make contact with a company representative who explains the full context of their business challenge.
Next, the students prepare a proposal to serve as a blueprint for their project. Each team will scope out its project, analyzes data, and produces findings in both written and oral form over the course of two quarters (for those enrolled in the twelve-course curriculum) or three quarters (for those enrolled in the thirteen-course curriculum).
At Capstone Showcase events, students present their findings to a panel of experts who evaluate and test each project and choose a “Best in Showcase.”
COPD Readmission and Cost Reduction Assessment
UChicago Analytics students built data models and evaluated them across different frameworks. They determined that the resulting model is capable of rank-ordering readmission risk and allowing for flexibility in applying interventions to prevent readmission.
An NFL Ticket Pricing Study: Optimizing Revenue Using Variable And Dynamic Pricing Methods
UChicago Analytics students found a way for an NFL team to implement ticket pricing that responds to changing factors and gives the team the chance to fill more seats.
Using Image Recognition To Identify Yoga Poses
Master of Science in Analytics students built an app that uses a one-step neural network to examine images of yoga poses and recognize the poses in order to provide feedback to the app's yoga-practicing user.
Using Image Recognition to Measure the Speed of a Pitch
One capstone team developed an app that applied image recognition algorithms to measure the speed of a pitched baseball. Their app captured video, isolated the pitched ball, calculated the velocity of the pitch, and displayed this measurement so that users would be able to measure the speed of a pitch with their smartphones.
Real-Time Credit Card Fraud Detection
Credit card fraud puts consumers' identities at risk while credit card providers are forced to cover fraudulent charges. A team of analytics students carefully studied this problem: they created synthetic data that represented a large population of credit card users and were able to build a model that catches credit card fraud in real time.
Interested in Becoming an Industry Research Partner?
Get in touch with us to submit your idea for a collaboration or ask us questions about how the partnership process works.