Doctor of Philosophy (Mechanical Engineering: ADV DATA SCI) provides students an introduction to the world of data science, giving them the skills to understand a variety of techniques and tools. This option will help to educate and recognize PhD students whose thesis work focuses specifically on building and using advanced data science tools.
The requirements for the Doctor of Philosophy (Mechanical Engineering: ADV DATA SCI) are as follows:
I. Three out of the following four courses
| Course # | Course Name | Credits |
|---|---|---|
| CSE 544 | Principles of data management | 4 |
| CSE 546 or STAT 535 | Machine Learning or Statistical Learning: Modeling, Prediction, and Computing | 4/3 |
| CSE 512 | Data Visualization | 4 |
| STAT 509 or STAT 512-513 | Introduction to Mathematical Statistics or Statistical Inference | 4 |
II. eScience Community Seminar
- 4 quarters of the eScience Community Seminar OR ME Data Drive seminar with Professor Steve Brunton. Seminar credits do not count towards graduation requirements.
III. Fulfillment of the Mechanical Engineering requirements
In addition, all students are required to take at least one additional course in quantitative methods (statistics, applied mathematics, mathematics, or computational science) OR in a methodology directly relevant to their area of focus. Such courses are to be specified in each student’s Individualized Training Plan.
| Course # | Course Name | Credits |
|---|---|---|
| CSE416/STAT416 | Introduction to Machine Learning | 4 |
| STAT 527 | Nonparametric Regression and Classification | 3 |
| STAT 535 | Statistical Learning: Modeling, Prediction, and Computing | 3 |
| STAT 509 | Econometrics I: Introduction to Mathematical Statistics | 4 |
| STAT 512/513 | Statistical Inference | 4 |
| ME/EE 578 | Convex Optimization | 4 |
| ME 599 | Machine Learning Control | 3 |
| ME 599 | Data-Driven Modeling of Dynamical Systems (Manohar) | 3 |
| Amath 515 | Optimization: Fundamentals and Applications | 5 |
| Amath 563 | Inferring Structure of Complex Systems | 5 |
| Amath 582 | Computational Methods for Data Analysis | 5 |
| Amath 583 | High-Performance Scientific Computing | 5 |
Students may not count any course toward both the ME coursework requirements and the Data Science requirements. For example, if students take ME 574 and count it toward the computational or numerical analysis requirement, they cannot use this course to fulfill the Data Science requirement. Students must ensure that there’s a minimum of 9 distinct credits taken for the Data science option.