EXPERT PROBLEM SOLVERS
Meeting the world’s demand for data-driven solutions
Graduate Virtual Open House: October 18–28
Registration is open for the Columbian College of Arts and Sciences (CCAS) Graduate Virtual Open House! The online event includes program-specific information sessions and opportunities to engage with current graduate students, faculty and our admissions team.
The Data Science Program is hosting an information session for prospective graduate students on Wednesday, October 20 at 10 a.m. EDT.
Who We Are
Nearly every profession relies on data to succeed. And with the huge quantities of digital information being collected and exchanged in today’s marketplace, the demand for trained data scientists is higher than ever.
The STEM-designated Data Science Program at GW's Columbian College of Arts and Sciences prepares students to meet that need and enter competitive careers in government, technology, private industry and much more.
Jaquan Outlaw
MS ’18
"The data community is huge here in Washington, D.C. From politics to health to technology, a lot of companies are just so interested in data science."
What We Do
Data science experts learn how to make sense of massive data sets, and they use that information to improve the way we live, work and communicate. Whether forecasting stock market trends, constructing a social media profile for a marketing client or capturing GIS locations for disaster relief, Data Science Program students become adept at meeting today’s most pressing challenges.
Alongside classwork, students strengthen their résumés with the practical knowledge required for data-intensive jobs. The program offers access to partnerships with numerous startups, companies and agencies, connecting students with internships and careers at employers like Amazon, Booz Allen Hamilton, Capitol One, D.C. Government, the National Institutes of Health, Oracle, the U.S. Department of Defense and more.
Tailai Jin (MS '17) led a team of five data science students to participate in the Virginia Datathon. The team analyzed text mining on job descriptions in Virginia cities, and their final product was a job recommendation system based on user-input preferences and keywords.
