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 ESE 7998 Masters Research or ESE 7970 Masters Project. With faculty permission, they may take up to 3 units of graduate-level independent study (ESE 5999 Independent Study).
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 ESE 7998 Masters Research or ESE 7970 Masters Project; 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 | |
- 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 ESE 5980 Electrical & Systems Engineering Graduate Seminar . This course is taken with the Pass/No Pass 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 | |
| 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 |
| SDS 4020 | Mathematical Statistics | 3 |
| or SDS 5020 | Mathematical Statistics | |
| SDS 4130 | Linear Statistical Models | 3 |
| or SDS 5130 | Linear Statistical Models | |
| SDS 4155 | Time Series Analysis | 3 |
| or SDS 5155 | Time Series Analysis | |
| SDS 4210 | Statistical Computation | 3 |
| or SDS 5210 | Statistical Computation | |
| SDS 4310 | Bayesian Statistics | 3 |
| or SDS 5310 | Bayesian Statistics | |
- *
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 higher in the Engineering (with the prefix of BME, CSE, EECE, ESE, or MEMS), Physics, Mathematics, or Statistics and Data Science department, as electives. In addition, Finance courses FIN 5017 Quantitative Risk Management, FIN 5506 Fintech: Methods and Practice, and FIN 5370 Advanced Derivative Securities may be used as free electives.
- Students may take either ESE 4170 Introduction to Machine Learning and Pattern Classification or CSE 4107 Introduction to Machine Learning, but they may not use both as electives for the degree.
- For students who have already taken ENGR 3180 Engineering Mathematics A, ESE 3180, ENGR 3190 Engineering Mathematics B, ESE 3190, and ESE 5010 Mathematics of Modern Engineering I may not be used as electives for graduate credit.
- Undergraduate lab courses, research, independent study, senior design, and capstone courses 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.