Program Overview

Introduction to the Data Science Program

The Data Science Program at the School of Engineering is designed to prepare students for future careers and/or further study in Data Science. The program is administered jointly by the departments of Computer Science and Electrical and Computer Engineering.

Program Description

Data Science refers to the principles and practices in data analysis that support data-centric real-world problem solving. The Master of Science in Data Science (MSDS) program can be completed on campus or 100% online.

Program Outcomes

The outcomes of the program are that:

• Graduates will demonstrate facility in a variety of data analysis techniques, including machine learning, optimization, statistical decision-making, information theory, and data visualization.
• Graduates will be qualified to engage in interdisciplinary projects with data analytics components, including facility in communicating with engineers, scientists, and computing professionals.

Program Structure

The MSDS 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.

Program Requirements

The MSDS can be completed on a full-time or part-time basis. Prerequisites for the MSDS 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.

Requirements for the Degree

Requirements for the degree include a minimum of 30 semester hour units of study, and must include:

• Required courses:
  • Electrical Engineering 104 or Mathematics 165,
  • Mathematics 166
  • Computer Science 119
  • Computer Science 135
• Three electives that must include one course from each of these three categories:
  • (A) one course in data infrastructure (including Computer Science 112, 115, 116, 117, 118, 120, and 151)
  • (B) one course in data analysis and/or interfaces (including Computer Science 136, 137, 138, 141, 142, 152, 166, 167, 169, 171, 175, 177, 178, 236, 272, 275, and 277; Mechanical Engineering 150; and Civil and Environmental Engineering 187)
  • (C) one course in computational and theoretical aspects of data analysis (including Computer Science 131 and 160; Data Science 153 (or Computer Science 153); Mathematics 123, 125, 126, 133, 153, 155, and 156; and Electrical Engineering 109, 110, 127, 130, 133, and 140).
• Optional: A practice requirement may be fulfilled by (D) a course in the practice of Data Science (Data Science 143 or 154, or Computer Science 169) or a master’s project in Data Science (Data Science 293). The practice requirement may also be satisfied by taking an additional course in categories (A)-(C).
• One more elective from categories (A)-(D) is chosen in consultation with the student’s advisor.
• Courses in the above categories may not be double counted in more than one category.

Sample Program Completion

One way of completing the program is as follows:

Fall Term

• Electrical Engineering 104 Probabilistic Systems Analysis
• Computer Science 135 Introduction to Machine Learning
• Computer Science 119 Big Data
• Data science elective

Spring Term

• Mathematics 166 Statistics
• Data science elective
• Data science elective
• Data science elective

Spring or Summer Term

• Computer Science 154 Special topics in the practice of Data Science
• or Data Science 293/Computer Science 283 Master’s project in Data Science

Academic Departments

The School of Engineering comprises several academic departments, including:

• Biomedical Engineering
• Chemical and Biological Engineering
• Civil and Environmental Engineering
• Computer Science
• Electrical and Computer Engineering
• Mechanical Engineering

Strategic Research Areas

The School of Engineering focuses on several strategic research areas, including:

• Energy, Water, and the Environment
• Human Health and Bioengineering
• Human-technology Interface
• Intelligent Systems
• Learning Science

Centers

The School of Engineering is home to several research centers, including:

• Center for Applied Brain and Cognitive Sciences
• Center for Engineering Education and Outreach
• Center for STEM Diversity
• Center for the Enhancement of Learning and Technology
• Cybersecurity Center for the Public Good
• Initiative for Neural Science, Disease & Engineering
• Institute for Research on Learning and Instruction
• Tufts Gordon Institute
• Tufts Interdisciplinary Advanced Materials Center