Alumni Gifts and Data Analysis

Project Summary

Building off the work of a 2016 Data+ teamYu Chen (Economics), Peter Hase (Statistics), and Ziwei Zhao (Mathematics), spent ten weeks working closely with analytical leadership at Duke's Office of University Development. The project goal was to identify distinguishing characteristics of major alumni donors and to model their lifetime giving behavior.

Themes and Categories
Year
2017
Contact
Paul Bendich
Mathematics
bendich@math.duke.edu

Project Results: Working with a large data set of anonymized alumni donors, the team visualized the distinction between minor and major alumni donors in a number of ways, did an in-depth evaluation of the Development office's current donor affinity metric, and clustered donors into groups by giving behavior. 

Sponsored by Alumni Affairs and Development at Duke.

Click here for the Executive Summary

Faculty Leads: Robert Calderbank, Paul Bendich

Project Leads:

Stephen Bayer

Natalie Spring

Ian Conlon

Project Manager: Sheng Jiang

"Very impressed. Smart kids, driven. Asked some really great questions and showed keen interest in the work and potential application. Beyond the intangible benefit of connecting more with the student experience and academic mission of the university, we benefited from having new eyes on our data, fresh questions and insights, and, ultimately, a starting point for future analysis." — Ian Conlon, Associate Director, Prospect Management and Analytics, Duke Office of Prospect Research, Management and Analytics

Related People

Related Projects

Social and environmental contexts are increasingly recognized as factors that impact health outcomes of patients. This team will have the opportunity to collaborate directly with clinicians and medical data in a real-world setting. They will examine the association between social determinants with risk prediction for hospital admissions, and to assess whether social determinants bias that risk in a systematic way. Applied methods will include machine learning, risk prediction, and assessment of bias. This Data+ project is sponsored by the Forge, Duke's center for actionable data science.

Project Leads: Shelly Rusincovitch, Ricardo Henao, Azalea Kim

Project Manager: Austin Talbot

Aaron Chai (Computer Sciece, Math) and Victoria Worsham (Economics, Math) spent ten weeks building tools to understand characteristics of successful oil and gas licenses in the North Sea. The team used data-scraping, merging, and OCR method to create a dataset containing license information and work obligations, and they also produced ArcGIS visualizations of license and well locations. They had the chance to consult frequently with analytics professionals at ExxonMobil.

Click here to read the Executive Summary

 

Project Lead: Kyle Bradbury

Project Manager: Artem Streltsov

Yueru Li (Math) and Jiacheng Fan (Economics, Finance) spent ten weeks investigating abnormal behavior by companies bidding for oil and gas rights in the Gulf of Mexico. Working with data provided by the Bureau of Ocean Energy Management and ExxonMobil, the team used outlier detection methods to automate the flagging of abnormal behavior, and then used statistical methods to examine various factors that might predict such behavior. They had the chance to consult frequently with analytics professionals at ExxonMobil.

 

Click here to read the Executive Summary

 

Project Lead: Kyle Bradbury

Project Manager: Hyeongyul Roh