Program Overview

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Overview of the Data Science Master's Program

The Data Science master's program at Tufts University trains students to use statistics, data visualization, and machine learning to analyze and understand the world around them. This 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: 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: 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. This program allows students to gain up to six months of full-time work experience, build their resume, and develop a competitive advantage for post-graduation employment.

Program 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.

Application Requirements

Prerequisites for the Data Science master's program include a Bachelor of Science degree in a science, technology, engineering, or mathematics (STEM) field. Applicants with Bachelor's degrees in non-STEM fields may begin study with a Certificate in Data Science that, in an additional term, gives the applicant a sample of the program.

• Application Fee
• Resume/CV
• Personal Statement
• Transcripts
• Three letters of recommendation
• Official GRE scores (if applicable)
  • GRE General Test scores are not required for applicants who will have received a degree from an institution located in the U.S. or Canada by the time of enrollment. GRE scores are required for all other applicants.
• Official TOEFL, IELTS, or Duolingo test scores (if applicable)
  • Competitive scores for the Department of Computer Science are:
    • TOEFL Total: 100
      • Reading: 26
      • Listening: 26
      • Writing: 22
      • Speaking: 25
    • IELTS Total: 7.5
      • Minimum of 7.0 for each subscore
    • Duolingo Total: 120
      • Literacy: 125
      • Conversation: 120
      • Comprehension: 135
      • Production: 105
• Portfolio (optional)

Tuition and Financial Aid

The university recognizes that attending graduate school involves a significant financial investment. The team is available to answer questions about tuition rates and scholarship opportunities.

Career Outcomes

• Average Salary: $108K+
• Projected Job Growth: 35%
  • Sources: Average salary and projected job growth statistics are from the U.S. Bureau of Labor Statistics Occupational Outlook Handbook.

Faculty

Alva Couch

Associate Professor, Computer Science

• Research/Areas of Interest: data science, software systems engineering, performance analysis, system, network, and data management

Shuchin Aeron

Professor, Electrical and Computer Engineering

• Research/Areas of Interest: Machine Learning, Statistical Signal Processing, Information Theory, Optimal Transport

Ankit Bhardwaj

Assistant Professor, Computer Science

• Research/Areas of Interest: low-latency and highly scalable datacenter systems

Lenore Cowen

Professor, Computer Science

• Research/Areas of Interest: computational molecular biology, data science, graph algorithms, network science, discrete mathematics

Michael Hughes

Wittich Family Assistant Professor, Computer Science

• Research/Areas of Interest: Machine learning: probabilistic models, Bayesian inference, variational methods, time-series analysis, semi-supervised learning; Clinical informatics: electronic health record analysis

Liping Liu

Associate Professor, Computer Science

• Research/Areas of Interest: Machine Learning, Data Science, Deep Learning, Generative Models, Time Series, Graph Learning

Donna Slonim

Professor, Computer Science

• Research/Areas of Interest: data science, algorithms for analysis of biological networks, gene and pathway regulation in human development, algorithms for precision medicine, computational approaches to pharmacogenomics and drug discovery or repositioning

Related Programs

Data Science (Online)

Master's

• Average Duration: 12 - 24 months
• Commitment Options: Full-time, Part-time (Daytime), Part-time (Evenings)
• Format: Online
• Credits: 32
• Application Deadlines:
  • Fall: Aug 1
  • Spring: Dec 1 (Final Extended)
  • Summer: Apr 1

Data Science

Certificate

• Average Duration: 12 - 24 months
• Commitment Options: Full-time, Part-time (Daytime)
• Format: On-campus
• Credits: Varies
• Application Deadlines:
  • Fall: Jun 1
  • Spring: Dec 15 (domestic only)

Artificial Intelligence

Master's

• Average Duration: 18 - 24 months
• Commitment Options: Full-time, Part-time
• Format: On-campus
• Credits: 30
• Application Deadlines:
  • Fall: Jan 15
  • Spring: Dec 1 (domestic only)