MS in Data Analytics & Statistics

The MS in Data Analytics and Statistics (MSDAS) is an academic master's degree designed for students interested in learning statistical techniques necessary to make informed decisions based on data analysis. It is aimed at harnessing the ever increasing amounts of data now available to gain new insights. Data analysts utilize machine learning and statistical tools to approach these problems. This interdisciplinary field is at the intersection of systems science, computer science and engineering, and mathematics, all of which are required for the goal of developing the skills to gather, process, analyze, model and optimize the resulting solutions.

This program has coursework broken up into four primary focus areas: mathematical probability and statistics, computational tools and machine learning, optimization methods, and applications. These courses are split between requirements in these four areas, as well as electives chosen from these areas.

Students have gotten internships or upon graduation have gone to work in industry as researchers, analysts and software engineers at companies such as; Amazon, Bayer, Bosch, Citigroup, Deloitte Consulting LLP, The Federal Reserve, and GE.

Requirements for Degree

• Refer to the University Bulletin for the specific requirements for this degree. For students admitted prior to Fall 20, the requirements are listed in the 19-20 University Bulletin. 

• The MSDAS degree requires 30 units, which may include optionally 6 units for thesis.

The following courses from the key areas of emphasis in the program that are required or allowed as electives are listed below.

Probability & StatisticsComputation & Machine LearningOptimizationApplicationsCore Courses

Required Courses (15 units) for the MS degree include the following:

At least three electives (9 units) from the following list:

* This course can be taken as an elective if it is not taken to statisfy a requirement. 

Free Electives (up to 6 units)

Students may  take up to 6 units of free electives.Any course numbered 401 or greater in the engineering, physics or mathematics departments, excluding the exceptions listed below, are approved by the department as electives.  In addition courses from the business school with a DAT designation and number of 500 or above may also be used as free electives.

These courses are NOT approved by the department as electives:

• Any general engineering course
  • CSE 501N, CSE 504N, CSE 505N
• Undergraduate lab courses
  • CSE 465M
  • EECE 405, EECE 421, EECE 424, EECE 425
  • ESE 435, ESE 447, ESE 448, ESE 449, ESE 465, ESE 488
  • MEMS 405
• Any undergraduate research, independent study, senior design or capstone course
  • ESE 400, ESE 497, ESE 498, ESE 499

Requests for exceptions to this policy may be submitted to the department chair with the approval of the academic advisor of the student.

Suggested Academic Requirements for Prospective Students

• A baccalaureate degree in engineering or STEM related degree is strongly encouraged, but not necessarily required.
• The following courses which form the foundation for the upper level courses required for the degree are highly recommended:
  • Calculus Sequence and Differential Equations
  • Probability and Statistics
  • Matrix Algebra
  • Introductory Computer Science
• More advanced topics in Computer Science such as Data Structures are also helpful, but may be added after admission to the program.
• Knowledge of a scientific or quantitative social science field is encouraged but not necessary for success in the program.