Columbia University Data Science Institute is pleased to announce a new initiative under the Data Science Institute (DSI) Scholars Program: “Data for Good” undergraduate scholars who will work on projects with societal impacts. For the program’s inaugural term, Spring-Summer 2019, we are calling for applications from Columbia and Barnard undergraduate students.
Click here to apply.
Faculty Advisors
- Professor Tian Zheng
- Dr. Vincent Dorie
- Department/School: Data Science Institute
Project timeline
- Spring: 03/25/2019 - 05/30/2019
- Number of hours per week of research expected: 5-10
- Summer: 06/03/2019 - 07/26/2019
- Number of hours per week of research expected: 35
Candidate requirements
- Skill sets: data cleaning, R or Python, data visualization, data mining
- Student eligibility (as of Spring 2019): freshman, sophomore, junior
- International students on F1 or J1 visa: eligible
The selected “Data for Good” undergraduate scholars will work with DSI research scientists and faculty members on data science projects with societal impacts. We are looking to fill four part-time positions (5-10 hours a week with a stipend of $2,500) for Spring 2019, which may be extended to a Summer 2019 full time research internship (35 hours a week for 8 weeks, with a stipend of $5,000). Housing is not included.
In addition to working on the “data for good” research project, student research interns will be invited to participate in the following DSI scholars activities: 1) bootcamp on Data Science Research skills; 2) Scholars in Data Science weekly meetup series; 3) Fall 2019 DSI Scholars Research Poster Social. See http://bit.ly/DSI-scholars2019 for more information.
The Data Science Institute encourages women and underrepresented minorities to apply to this program.
Key dates (The following time table is subject to adjustment.)
- March 8th, 2019 - Student applications due.
- March 15th, 2019 - Email offers of internship are expected to be sent to students.
Please email questions to dsi-scholars@columbia.edu.
Click here to apply.