MS in Data Science

Data science is an emerging field that aims to draw actionable conclusions from data. It uses techniques and theories from the broader areas of statistics, computer science, and mathematics. Its applications are in many fields, including business, engineering, natural sciences, social sciences, humanities, and healthcare.

The explosion of data in today’s world is rapidly shaping the landscape of our lives. This has led to an urgent need to process a massive amount of information and obtain meaningful insights. Data scientists are trained to meet such challenges. Through a structured curriculum that provides foundational knowledge as well as application skills, students in GW's MS in data science program learn how to confront the most complex problems faced by both government and private industry using data-driven decisions.

The program provides a deep foundation in statistical analysis and programming. It also offers knowledge of the application domain, in addition to project management skills. Program graduates apply data science techniques to the solution of real-world problems, communicate findings, and effectively present results using data visualization tools.

Developed through a collaborative effort between the Departments of Statistics, Mathematics, Physics, Economics, Geography and Political Science, the MS in data science gives students cutting-edge tools for analyzing big data and teaches them how to extract the insights that are changing the way we live, work, and communicate.

This is a STEM-designated program.

Capstone Project

As a culmination of the master’s program, students enroll in a three-credit capstone course and spend their final semester applying the skills and knowledge they learned in data analysis. For the capstone, students work in groups on a practical application of data science principles. Capstone team projects are chosen in consultation with the course instructor.

Course Requirements

The following requirements must be fulfilled:

30 credits, including 18 credits in required courses and 12 credits in elective courses.

Required

• DATS 6101Introduction to Data Science
• DATS 6102Data Warehousing
• DATS 6103Introduction to Data Mining
• DATS 6501Data Science Capstone
• DATS 6202Machine Learning I: Algorithm Analysis
• DATS 6401Visualization of Complex Data

Electives

• 12 credits in elective courses in DATS numbered 6000 or above.

Learning Objectives

Students who complete the MS in Data Science are equipped to apply data science techniques to solve real-world problems, communicate findings and effectively present those findings using data visualization tools.

Specifically, students graduate with:

• Thorough working knowledge of statistical data analysis techniques
• Experience with data-mining software tools
• Experience with cutting-edge tools and technologies to analyze big data
• Practical skills for visualizing and transforming data
• Communication skills and working effectively in teams

Focus Areas

Both the master’s degree and the graduate certificate program combine courses from four areas:

• Methods: Basics of data management and data analytics; deep expertise in the programming languages essential for data science, including Python, JavaScript, and R
• Applications: Elective courses in data science applied to a specific knowledge domain, such as astrophysics, political science, and geography
• Skills: Teamwork, project management, and communication skills
• Technology: Hands-on exposure to data and visualization software and languages