# 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:

- 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.

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.

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:**

**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*