MS in Data Science

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), administered jointly by the departments of Computer Science and Electrical and Computer Engineering, prepares students for future careers and/or further study in Data Science.

Outcomes of the program are that:

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

The MSDS is built upon a disciplinary core of statistics and machine learning, with depth provided by courses in each of the following categories:

  1. 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.
  2. Data analysis and interfaces: those components of computing concentrated around effective human interaction with computers, including human-computer interaction, graphics, visualization, and others.
  3. Computational and theoretical aspects of data science: mathematical foundations, including information theory, signal and image processing, and numerical analysis.
  4. 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.

The MSDS is a one-year program that may be completed either in 9 or 12 months of study. 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 include a minimum of 30 semester hour units of study, and must include Electrical Engineering 104 or Mathematics 165, Mathematics 166, Computer Science 119 and Computer Science 135. Three electives must include: (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); and (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, 127, 130, 133, and 140). 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. 

One way of completing the program is as follows:


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


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


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