Kang Ni, Math/Econ major, Kehan Zhang, Econ/Stats/ major, and Alex Hong, spent ten weeks investigating a large collection of grocery store transaction data. They worked closely with Matt Harding Behavioral Economics and Healthy Food Choice Research Center. (BECR Center).
Project Results
The team used clustering techniques to identify distinct sets of products, a good first step towards developing a customer preference index.
Download the executive summary (PDF).
Disciplines Involved
L-R: Kang Ni, Alex Hong, Kehan Zhang
Project Team
Undergraduates: Alex Hong, Kehan Zhang, and Kang Ni
Client: Matt Harding, Director, BECR Center
Project Mentor: Ya Xue, Data Scientist, BECR Center
United Nations Sustainable Development Goal 7 calls for universal access to affordable, reliable, sustainable, and modern energy. Researchers and practitioners around the world have responded to this call by producing a wealth of energy access data. While many data gaps still exist, are we capturing the fullest potential from the information and research we do have, and what it tells us about how to accelerate energy access? Power for All’s Platform for Energy Access Knowledge (PEAK) is an interactive knowledge platform designed to automatically curate, organize, and streamline large, growing bodies of data into digestible, sharable, and useable knowledge through automated data capture, indexing, and visualization. A team of students led by Rebekah Shirley will consult with Power for All to creatively visualize PEAK’s library, and to explore machine learning and natural language processing tools that can enable auto-extraction and visualization of data for more effective science communication.
Are there relative value opportunities in the global corporate bond markets?
A team of students will work with Professor Emma Rasiel to understand whether an analysis of credit spreads on bonds issued by international firms in multiple countries over time can shed light on potential arbitrage opportunities. The team will have frequent opportunities to interact with analytics professionals at a leading financial advisory and asset management firm.
A team of students will consult with a leading financial advisory and asset management firm that is seeking to understand how big data can shed light on the secondary market for construction machinery. Students will explore a combination of publicly-available datasets that describe the used-machinery market and its potential implications as an indicator for the business cycle. There will be frequent interactions with analytical professionals from the firm.
