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.

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.

Requirements for Degree

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

Probability & Statistics, Computation & Machine Learning, Optimization, Applications Core 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.

Degree Requirements (15 units)

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

All full-time graduate students are required to take ESE 590 Electrical & Systems Engineering Graduate Seminar each semester. This course is taken with an unsatisfactory/satisfactory grade option.

Refer to the University Bulletin for the specific requirements for this degree.

Degree Electives (9 units)

Students must take at least three electives from the following list:

Course Numbers Unapproved Electives
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.

Degree 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:

Course Numbers Unapproved Electives
CSE 501NCSE 504NCSE 505N
CSE 465M
EECE 405, EECE 421, EECE 424EECE 425
ESE 435ESE 447ESE 448ESE 449ESE 465ESE 488
MEMS 405
Undergraduate lab courses
ESE 400ESE 497ESE 498ESE 499 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.