University of California Riverside Online Master of Science in Engineering – Data Science Specialization, California

Why University of California Riverside?

The online Master of Science in Engineering – Data Science Specialization offered by the University of California Riverside combines graduate-level training in engineering management with education in the fundamental tools and techniques of data science. The University of California Riverside,  a large public research university in Riverside, California, requires applicants to this program to have an undergraduate degree in engineering or a related field such as physics or math. Applicants are also required to have work experience as an engineer. GRE or FE scores are required, and a minimum undergraduate GPA of 3.40  is expected. The GRE or may be waived if applicants have at least two years of work experience in professional engineering.

Program Snapshot

University Name University of California Riverside
Location Riverside
State California Grade B+
Name of Degree Master of Science in Engineering – Data Science Specialization from the University of California – Riverside
School or Department Administering Program Engineering
Credits 36
Cost per credit $833.33
Tuition for Entire Program $30,000
Test Requirements
  • GRE or FE
  • waived with at least two years engineering work experience
Minimum GPA 3.40
Prerequisite Courses and Skills
  • Bachelor’s degree in engineering or a related field such as physics or math
  • Required to have work experience as an engineer
Campus Visit Required? No
Typical Time to Complete One year
When Can Students Start the Program? Summer, Fall, Winter, Spring
Program Concentrations Data Science is a concentration within the MS in Engineering degree
Synchronous Classes Yes
Required Courses
  • All of the following:
    • Engineering in the Global Environment
    • Technology, Innovation, and Strategy for Engineers
    • Introduction to System Engineering
    • Principles of Engineering Management
  • and 4 of the following:
    • Statistical Computing
    • Statistical Data Mining Methods
    • Machine Learning
    • Computer Graphics
    • Data Mining Techniques
    • Database Management Systems
    • Information Retrieval and Web Search
    • Advanced Image Processing
    • Advanced Computer Vision
    • Foundations of Applied Machine Learning
    • Application of Visualization in Data Science
    • Introduction to Applied Data Science
    • Professional Project Design 1-4
Does the Program Include a Capstone? Yes
Does the Program Include a Practicum or Internship? No
Other Features that Make the Program Unique
  • UCR Data Science Center for collaborative research
  • 4 one credit capstones
Program Objectives (quoted directly from the program) none provided
Program Description This 36 credit program is aimed at professional engineers who want to pursue careers in data analytics and data science as these fields relate to engineering. Students complete a full Master’s degree in engineering, focusing on skills needed for managers and subject specialists. In addition, they learn the fundamentals of statistical methods and database management and pursue electives in image processing, project design, information retrieval, and other topics. Data Science is a concentration within the Masters of Engineering program.

What will I Learn?

The UC Riverside program prepares professional engineers with prior work experience for the next stage of their careers working on data-intensive engineering problems. Therefore applicants can expect their classmates to have a high degree of math and computer science skills, and students jump directly into higher-level topics in engineering, management, and data science. All students in the master’s program must complete four core courses: Engineering in the Global Environment, Technology, Innovation and Strategy for Engineers, Introduction to System Engineering, and Principles of Engineering Management. To complete their program, all students must take Professional Project Design 1-4, a suite of four one-credit capstone courses teaching them to design and execute a real-world project.

Students who wish to pursue the Data Science specialization then choose from a large suite of approved electives to complete their degree. All data science students must take four courses from among choices focusing on Statistical Computing, Statistical Data Mining Methods, Machine Learning, Computer Graphics, Data Mining Techniques, Database Management Systems, Information Retrieval, and Web Search, and others. A particular strength of the Riverside program is the field of image processing, and two courses are offered in image processing and computer vision. Students may also take in-depth courses on machine learning, applied data science, and data visualization.

How much will the program cost?

Students finish their degree within one year and pay $833.33 per credit for a total of 36 credits, or just under $30,000

How does the program fit into my life?

All courses are asynchronous, meaning that instructors create learning materials such as lectures, activities, quizzes, assignments, and group discussions in weekly modules, and students are responsible for completing the module on their own schedule within the given week.  The program can be completed in one year. Students can begin the program in Summer, Fall, Winter, or Spring semester.


The UC-Riverside program is definitely appropriate for all aspiring data scientists, but for those with a background in engineering, it offers a comprehensive and rigorous curriculum. The program is one of the most selective in the field in terms of undergraduate GPA and background requirements, and those that do succeed in being admitted can expect to learn with a cohort of experienced and committed classmates. Students will also have access to research expertise at UC Riverside, including the UCR Data Science Center.

A key advantage of the program is the fast time to completion within one year.  Because 12 credits of the program are required courses on general topics in engineering, applicants should be aware that they may not be able to focus so deeply on data science topics as they may be able to in programs devoted entirely to data science. The program will also not be appropriate for applicants with less academic preparation or who desire a broad introduction to the use of data science in organizations outside of the engineering context.