Invest in yourself by pursuing a master's in engineering data analytics and statistics. Graduate with the knowledge, skills and personal network you'll need to join the next generation of elite data analysts. 

The Master of Science in Engineering Data Analytics and Statistics (MSDAS) is an academic master's degree designed for students interested in gaining advanced expertise in the use and application of cutting-edge software and analytical tools to collect, analyze, model and optimize data. This interdisciplinary field is at the intersection of systems science, mathematics, and computer science and engineering, all of which are required in the rapidly changing world of analytics and data science. 

Employer demand for analytics-enabled graduates continues to grow. Students 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.

Suggested Academic Requirements for Prospective Students

It is recommended that incoming students earn a baccalaureate degree in engineering or another STEM-related degree. In earning that degree, it is recommended that students take the following upper-level courses:

  • 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.

Degree Requirements for Current Students

Students pursuing the Master of Science in Engineering Data Analytics & Statistics (MSDAS) must complete a minimum of 30 units of study (which may include optionally 6 units for thesis) consistent with the residency and other applicable requirements of Washington University and the McKelvey School of Engineering and subject to the following departmental requirements:

  • A minimum of 15 of the total 30 units must be selected from the Degree Requirement list below for core electrical engineering subjects taught by the Department of Electrical & Systems Engineering (ESE)
  • A maximum of 6 units may be transferred from another institution and applied toward the master's degree.
  • The remaining courses in the program, listed in the Degree Electives list below, may be selected from senior or graduate-level courses in ESE or elsewhere in the university.
    • Courses outside of ESE must be in technical subjects relevant to electrical engineering and require the department's approval.
    • Undergraduate Laboratory courses may not be used to satisfy this requirement.
  • Students must obtain a cumulative grade-point average of at least 3.0 out of a possible 4.0 overall for courses applied toward the degree. Courses that apply toward the degree must be taken with the credit/letter grade option.
  • Refer to the University Bulletin for the specific requirements for this degree.

Either a thesis option or a course option may be selected. The special requirements for these options are as follows:

  • Thesis Option: This option is intended for those pursuing full-time study and engaged in research projects. Candidates for this degree must complete a minimum of 24 units of course instruction and 6 units of thesis research (ESE 599); 3 of these units of thesis research may be applied toward the 15 core electrical engineering units required for the MSEE program. Any of these 6 units of thesis research may be applied as electives for the MSEE, MSSSM, and MSDAS programs. The student must write a master's thesis and defend it in an oral examination.
  • Course Option: Under the course option, students may not take ESE 599 Master's Research. With faculty permission, they may take up to 3 units of graduate-level independent study.

Required Courses (15 units) 

Course Number Course Name

ESE 417
CSE 417T
CSE 517A

Introduction to Machine Learning and Pattern Classification  or 
Introduction to Machine Learning or
Machine Learning

ESE 415
ESE 513

Optimization or
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 

Degree Electives (9 units)

Course Number Course Name

Math 439
Math 459
Math 461
Math 475
Math 494

Linear Statistical Models
Bayesian Statistics
Time Series Analysis
Statistical Computation
Mathematical Statistics

CSE 427S
CSE 412A
CSE 515T
CSE 517A

Cloud Computing with Big Data Applications
Introduction to Artificial Intelligence
Bayesian Methods in Machine Learning
Machine Learning*

ESE 427
ESE 513
ESE 523
ESE 4261
ESE 551
Financial Mathematics
Large-Scale Optimization for Data Science*
Information Theory
Statistical Methods for Data Analysis with Applications to Financial Engineering
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.

The following courses are NOT approved by the department as electives. Requests for an exception to this policy may be submitted to the graduate program coordinator with the approval of the student's academic advisor.

Course Numbers Unapproved Electives
CSE 501N, 504N, 505N
CSE 465M
EECE 405, 421, 424, 425
ESE 435, 447, 448, 449, 465, 488
MEMS 405
Undergraduate lab courses
ESE 400, 497498499

Any undergraduate research, independent study, senior design or capstone course