Comparing the Exploration of Academic Majors at Duke

Project Summary

Over ten weeks, Math/CompSci majors Benjamin Chesnut and Frederick Xu joined forces with International Comparative Studies major Katharyn Loweth to understand the myriad academic pathways traveled by undergraduate students at Duke. They focused on data from Mathematics and the Duke Global Health Institute, and worked closely with departmental leadership from both areas.

Themes and Categories
Interdisciplinary Inquiry
Data+
Year
2017
Contact
Paul Bendich
Mathematics
bendich@math.duke.edu

Project Results: The team created interactive Tableau visualizations of academic and demographic data within Mathematics and Global Health. Working in MySQL, Python, and R, they clustered students into different groups based on math courses taken. They also performed feature selection to understand the key differences between Global Health majors and minors, as well as analyzing the main pathways into the Global Health program.

Partially sponsored by the Department of Mathematics and by the Duke Global Health Institute.

Click here for the Executive Summary

Faculty Leads: Jonathan Mattingly, David Toole

Project Manager: Zhennan Zhou

Related People

Related Projects

Neural Network-Based Self-Adjusting Computational Processors

A team of students, led by Electrical and Computer Engineering professor Vahid Tarokh, will develop methods to improve the efficiency of information processing with adaptive decisions according to the structure of new incoming data. Students will have the opportunity to explore data-driven adaptive strategies based on neural networks and statistical learning models, investigate trade-offs between error threshold and computational complexity for various fundamental operations, and implement software prototypes. The outcome of this project can potentially speed up many systems and networks involving data sensing, acquisition, and computation.

Project Leads: Yi Feng, Vahid Tarokh

Computational Approaches to the History of Cartography

A team of students will explore new ways of reading pre-modern maps and perspectival views through image tagging, annotation and 3D modeling. Each student will build a typology of icons found in these early maps (for example, houses, churches, roads, rivers, etc.). By extracting, modeling, and cataloging these features, the team will create a library of 2D and 3D objects that will be used to (a) identify patterns in how space and power are represented across these maps, and (b) to create a model for “experiencing” these maps in 3D, using the Unity game engine platform. This is a combined Data+ / Bass Connections project that will instruct students in qualitative and quantitative mapping techniques, basic 3D modeling and the history of cartography.

Project Lead: Philip Stern, Ed Triplett

Project Manager: Sam Horewood

Data Science for Retention of College Women in Tech

A team of students will explore ways in which data science can help support the mission of Rewriting the Code, a national non-profit organization dedicated to empowering a community of college women with a passion for technology.

In particular, students will perform statistical analyzes of past survey data, build out interactive dashboards that help visualize trends in student experience, and help design future survey questions.

Project Lead: Sue Harnett

Faculty Lead: Alexandra Cooper

Project Manager: Imari Smith

See all Research