Summer 2022 Capstone Winners

Data science master's students deploy advanced analytics technologies as they meet pressing industry needs.

Written by Philip Baker
Storytelling with Data

As the crowning achievement of the Master of Science in Analytics (MScA) program, the Capstone Project unites students with industry partners to solve real-world analytics problems. At the Summer Showcase, projects spanned industries, data types, and methodological approaches—and two teams stood out for special commendation.

“The quality of the work was truly impressive and exceptional,” said Greg Green, Executive Director for the MScA program. “We are looking forward to staying connected with the graduates and hearing about their next achievements.”

Learn more about the winning projects below.

Analysis of UChicago Patenting and Licensing Data

Linxi Wu, Jianling Hu, Nicholas Petr, Zhiruo (Amy) Zhang

Advisor: Roger Moore

With Roger Moore as their advisor, Wu, Hu, Petr, and Zhang analyzed the University of Chicago’s patenting and licensing data. The University receives royalties on the sales of branded products and reimbursements from licensed intellectual property. In analyzing how the patenting and licensing processes played out over several years, the team observed that the University spends roughly $3 million per year in external legal fees, a significant fraction of which is reimbursed.

“Our analysis revealed that, for the last five years, the University has received over $40M in cumulative licensing revenue,” the team wrote. “We were then able to track these funds as they were divided between the inventors, research support, and the Polsky Center.”

Clustering of Market Index Returns for Portfolio Benchmark Analysis

Robert Scott, Jiaqing Mao, Robert Volgman

Advisor: Jonathan Williams

Advised by Jonathan Williams, Mao, Scott, and Volgman analyzed Northern Trust’s current approach to receiving and benchmarking the price data they receive daily for thousands of market indices. Extremely time-consuming to review by hand, the bank currently benchmarks their clients’ portfolio returns against broad-market indices. The team’s goal was to improve on the portfolio benchmarking.

“We addressed the issue by creating a clustering model for indices,” the team wrote. “With the clusters we can then detect anomalies that negatively impact the benchmarking for client portfolios. We used the k-mean clustering method to separate the indices into 12 clusters, which we then combined with an ARIMA model to create portfolio forecasts.”

Students in a classroom laughing with one another.

Bring About Real-World Data Solutions Tomorrow.

Apply your knowledge to real business problems using real data—and gain the skills you need to master emerging technologies.

Apply Today

Additional Stories