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 here. For those admitted during the Fall 20/Spring 21 academic year, the requirements are listed here.  Information regarding the previous allowable electives can be found here.

  • 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:

ESE 417  Introduction to Machine Learning and Pattern Classification  or 
CSE 417T   Introduction to Machine Learning  or 
CSE 517A   Machine Learning
ESE 415 Optimization  or
ESE 513  Large Scale Optimization for Data Science
ESE 520  Probability and Stochastic Processes 
ESE 524  Detection and Estimation Theory 
ESE 527  Practicum in Data Analytics and Statistics 

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

Math 439  Linear Statistical Models

Math 459  Bayesian Statistics

Math 461  Time Series Analysis

Math 475  Statistical Computation

Math 494  Mathematical Statistics

CSE 427S  Cloud Computing with Big Data Applications

CSE 412A  Introduction to Artificial Intelligence

CSE 515T  Bayesian Methods in Machine Learning 

CSE 517A  Machine Learning*

ESE 427    Financial Mathematics

ESE 513    Large-Scale Optimization for Data Science*

ESE 523  Information Theory 

ESE 526  Network Science

ESE 551  Linear Dynamic Systems 1

* This course can be taken as an elective if it is not taken to satisfy 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 (with the prefix of BME, CSE, EECE, ESE, or MEMS), Physics or Mathematics department, 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:

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.