About

The Data Science and Analytics program provides students with rigorous training in analytical, computational, mathematical, and statistical methods and models to prepare them for careers in data science and analytics.

Students in the M.S. in Data Science and Analytics program build a robust knowledge base and solid foundation in data science and analytics fundamentals, including big data and cloud computing, machine and deep learning, interactive and complex visualization methods, advanced databases, objects, algorithms, and complexity, text mining and natural language processing, and advanced mathematical and statistical modeling. Languages used include R, Python, and SQL. Students also engage in additional and critical skills including decision science, data communication, visual narrative development, teamwork, and complex problem solving techniques. Students who complete the program pursue careers in fields including business intelligence, analytics, and decision making; medical analytics; public policy, government, and political analytics; finance, marketing, and banking analytics; big data infrastructure and cloud computing; global health analytics, and many other data-dependent areas. The data science graduate program also serves as preparation for students who wish to enter a Ph.D. program in Data Science and Analytics, Applied Mathematics, Statistics, Computer Science, or Economics.


Who Should Apply?

Data Science student at an Analytics lecture at georgetown graduate analytics program

This program is appropriate for students who have recently completed degrees with significant mathematical or statistical emphasis, as well as for mid-career professionals who seek professional advancement or a shift in career track. The expected time for completing the degree as a full-time student is two years. By using transfer credit and/or taking summer courses, students may be able to complete the program in three semesters (16 months). Part-time students may take longer (up to three years). Classes are offered in the late afternoon or evening, allowing part-time students to participate fully.