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
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Refer to the University Bulletin for the specific requirements for this degree.
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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.
* If not used to satisfy core requirement
1 Math 494 was a requirement for the degree for students who entered the program prior to the Fall 2020 semester.
2 ESE 529 allowed as a substitution of ESE 524 for students entering the program prior to Fall 2020.
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.
