Data+ team members.

Deadline: February 25, 2019

Data+ is a ten-week summer research experience for undergraduates and master’s students interested in exploring new data-driven approaches to interdisciplinary challenges. It is suitable for students at all levels and from all majors.

Students join small teams (a maximum of three undergraduates and one master’s student) and work alongside other teams in a communal environment. They learn how to marshal, analyze and visualize data, while gaining broad exposure to the field of data science.

The program runs from late May through late July each year, with the application deadline in February. Participants receive a stipend. Students come from a variety of backgrounds, majors and levels of experience with coding. Through collaboration, they use data analysis to solve problems across disciplines.

Data+ is offered through the Rhodes Information Initiative at Duke and is part of the Bass Connections Information, Society & Culture theme.

Apply Now for Summer 2019

Data+ 2019 runs from May 28 through August 3, 2019. We are currently accepting applications via this link. The application deadline is February 25, but we will evaluate applications on a rolling basis, so please get your application in as soon as you can.

Participants receive a $5,000 stipend for this full-time research experience, out of which they must arrange their own housing and travel. Participants may not accept employment or take classes during the program; this requirement is strictly enforced and nonnegotiable.

Browse the projects below, and come to the Data+ Information Fair on January 17 at 3:00 p.m. to learn more and meet the project leads.

2019 Projects

Code+

At the fair, students will also have the opportunity to talk with the 2019 project leads for the new Code+ program, and learn more about projects for summer 2019. Code+ projects are paid internships that focus on application/product development, while Data+ projects are stipend-based and focus on learning to marshal, analyze, and visualize data from a wide spectrum of sources.