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:
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 412A CSE 427S CSE 514A CSE 515T CSE 517A
|
Introduction to Artificial Intelligence Cloud Computing with Big Data Applications Data Mining 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.
Students may take either ESE 417 or CSE 417T, but they may not use both as electives for the degree.
For students who have already taken ESE 318 & 319, ESE 501 may not be used as an elective for graduate credit.
Additionally, 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, 497, 498, 499
|
Any undergraduate research, independent study, senior design or capstone course |