Engineering Data Analytics & Statistics, MSDAS (ESE)
Either a thesis option or a course option may be selected. The special requirements for these options are as follows:
Course Option
The Master of Science in Engineering Data Analytics and Statistics is an academic master's degree designed mainly for both full-time and part-time students interested in proceeding to the departmental full-time doctoral program and/or an industrial career. Under the course option, students may not take Master's Research (ESE 7998) or Master's Project (ESE 7970). With faculty permission, they may take up to 3 units of graduate-level independent study (ESE 5999).
Thesis or Project Option
These options are intended for ESE students engaged in research projects. Candidates for this degree must complete a minimum of 24 units of course instruction and 6 units of Master's Thesis (ESE 7998) or Master's Project (ESE 7970); up to 3 thesis or project units may be applied toward the 15 core electrical engineering units required for the MSEE program. Up to 6 thesis or project units may be applied as electives for the MSEE, MSSSM, and MSDAS programs. Students can take at most 3 thesis or project units in a semester. For the thesis, the student must write a master's thesis and defend it in an oral examination. For the project, the students must complete a paper documenting their work and either present their work orally or at a departmental or school wide poster session.
Degree Requirements
Students pursuing the degree Master of Science in Data Analytics & Statistics (MSDAS) must complete a minimum of 30 units of study consistent with the residency and other applicable requirements of Washington University and the McKelvey School of Engineering and subject to the following departmental requirements:
- Required courses (15 units) for the MSDAS degree include the following:
| Code | Title | Units |
|---|---|---|
| ESE 4170 | Introduction to Machine Learning and Pattern Classification | 3 |
or CSE 4107 | Introduction to Machine Learning | |
or CSE 5107 | Machine Learning | |
| ESE 4150 | Optimization | 3 |
or ESE 5130 | Large-Scale Optimization for Data Science | |
| ESE 5200 | Probability and Stochastic Processes | 3 |
| ESE 5240 | Detection and Estimation Theory | 3 |
| ESE 5971 | Practicum in Data Analytics & Statistics | 3 |
| Total Units | 15 | |
- At least 15 units of the 30 total units applied toward the MSDAS degree must be in ESE courses which, if cross-listed, have ESE as the home department.
- A maximum of 6 credits may be transferred from another institution and applied toward the master's degree. Regardless of the subject or level, all transfer courses are treated as electives and do not count toward the core requirements for the degree.
- All full-time graduate students are required to take Electrical & Systems Engineering Graduate Seminar (ESE 5980) each semester. This course is taken with the unsatisfactory/satisfactory grade option.
- The degree program must be consistent with the residency and other applicable requirements of Washington University and the McKelvey School of Engineering.
- 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 for the degree must be taken with the credit/letter grade option.
Degree Electives
| Code | Title | Units |
|---|---|---|
| Required Electives | 6 | |
| SDS 4020 | Mathematical Statistics | 3 |
| SDS 4061 | Time Series Analysis | 3 |
| SDS 4130 | Linear Statistical Models | 3 |
| SDS 4210 | Statistical Computation | 3 |
| SDS 4310 | Bayesian Statistics | 3 |
| CSE 4102 | Introduction to Artificial Intelligence | 3 |
| CSE 4207 | Cloud Computing with Big Data Applications | 3 |
| CSE 5104 | Data Mining | 3 |
| CSE 5105 | Bayesian Methods in Machine Learning | 3 |
| CSE 5107 | Machine Learning* | 3 |
| ESE 4261 | Statistical Methods for Data Analysis with Applications to Financial Engineering | 3 |
| ESE 4270 | Financial Mathematics | 3 |
| ESE 5130 | Large-Scale Optimization for Data Science * | 3 |
| ESE 5230 | Information Theory | 3 |
| ESE 5510 | Linear Dynamic Systems I | 3 |
- *
This course can be taken as an elective if it is not taken to satisfy a requirement.
Free Electives (up to 6 units)
- Any course numbered 4001 or greater in the Engineering (with the prefix of BME, CSE, EECE, ESE, or MEMS), Physics, Mathematics or Statistics and Data Science department, as electives. Additionally, Finance courses FIN 5017, FIN 5506, and FIN 5370 as well as courses with a DAT designation and number of 5000 or above, except for DAT 5561, may be used as free electives.
- Students may take either ESE 4170 or CSE 4107, but they may not use both as electives for the degree.
- For students who have already taken ESE 3180 and ESE 3190, ESE 5010 may not be used as an elective for graduate credit.
- Undergraduate lab course, research, independent study, senior design or capstone course are not approved 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.