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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
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.
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
- Undergraduate lab courses
- Any undergraduate research, independent study, senior design or capstone course
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.
- Financial Information
- International Students
E60-505 - Communication tools for Academic and Professional Success
McKelvey School of Engineering requires all incoming international students who submit a TOEFL or IETLS score or has not obtained a minimum of 3 years of education in the U.S. to take a course in communication. This new course was first offered in Fall of 2018. This course does not cost extra for full-time students and is not counted toward the degree or the GPA.