Data Science

The Data Science master's program trains students to use statistics, data visualization, and machine learning to analyze and understand the world around them. The master's program is jointly administered by the Department of Computer Science and the Department of Electrical and Computer Engineering.

Program Highlights

The Data Science master's program is built upon a disciplinary core of statistics and machine learning, with depth provided by courses in each of the following categories:

• Data infrastructure and systems: those systems and strategies that are core to interacting with data, including computer networks, computer security, internet-scale systems, cloud computing, and others.
• Data analysis and interfaces: those components of computing concentrated around effective human interaction with computers, including human-computer interaction, graphics, visualization, and others.
• Computational and theoretical aspects of data science: mathematical foundations, including information theory, signal and image processing, and numerical analysis.
• Practice of data science: examples of effective use of data science in practice, including case studies and applications of data science principles to real-world problems.

Graduate Cooperative Education (Co-Op) Program

The School of Engineering's Graduate Cooperative Education (Co-Op) Program provides students with the opportunity to apply the theoretical principles they have learned in their coursework to real-world engineering projects. Gain up to six months of full-time work experience, build your resume, and develop a competitive advantage for post-graduation employment. Learn more about the Co-Op Program.

Career Outcomes

The Data Science program at Tufts prepares students to address real-world problems with data-centric insights.

Students engage in a variety of data analysis techniques, including machine learning, optimization, statistical decision-making, information theory, and data visualization. Graduates of the program go on to work on interdisciplinary projects with data components including communicating with engineers, scientists, businesses, computer scientists, and medical professionals.