Doctor of Philosophy (Mechanical Engineering: Data Science): students will receive credentialed training in the analysis of large datasets. The goal of this option is to educate all students in the foundations of data science, so they may apply those methods and techniques in current research. The PhD Data Science is designed for students with little or no background in data science, computer science or coding.
The requirements for the Doctor of Philosophy (Mechanical Engineering: Data Science) are as follows:
I. Courses from three out of four of the following areas
1. Software development for data science
Recommended courses
| Course # | Course Name | Credits |
|---|---|---|
| CSE 583 | Software Development for Data Scientists | 4 |
| AMATH 583 | High Performance Scientific Computing | 5 |
| ME 574 | Introduction to Applied Parallel Computing for Engineers | 3 |
2. Statistics and machine learning
Recommended courses
| Course # | Course Name | Credits |
|---|---|---|
| CSE416/STAT416 | Introduction to Machine learning | 4 |
| AMATH 582 | Computational Methods for Data Analysis | 5 |
| AMATH 563 | Inferring Structure of Complex Systems | 5 |
| AMATH 515 | Fundamentals of Optimization | 5 |
| ME/EE 578 | Convex Optimization | 4 |
| ME 599 | Machine Learning Control | 3 |
| CSE 599U | Reinforcement Learning | 4 |
| STAT 527 | Nonparametric regression and classification | 3 |
| CSE 546/STAT 535 | Machine Learning also serves for the “Advanced Data Science Option” |
4/3 |
| STAT 509 | Introduction to Mathematical Statistics also serves for the “Advanced Data Science Option” |
4 |
| STAT 512-513 | Statistical Inference also serves for the “Advanced Data Science Option” |
4 |
| ME 599 | Data-Driven Modeling of Dynamical Systems (Manohar) | 3 |
| CSE/AMATH 579 | Intelligent Control Through Learning and Optimization | 3 |
3. Data management and data visualization
Recommended Courses
| Course # | Course Name | Credits |
|---|---|---|
| CSE 414 | Introduction to Database Systems | 4 |
| CSE 412 | Introduction to Data Visualization | 4 |
| HCDE 411/511 | Information for Visualization | 4 |
| BIOEN 420 | Medical Imaging | 4 |
| BIOEN 451/551 | Optical Coherence Tomography | 4 |
| BIOEN 546 | Fundamentals of Biomedical Imaging | 4 |
| CSE 544 | Principles of DBMS also serves for the “Advanced Data Science Option” |
4 |
| CSE 512 | Data Visualization also serves for the “Advanced Data Science Option” |
4 |
4. Department specific requirement
If listed above, then course doesn’t count twice
Recommended courses
| Course # | Course Name | Credits |
|---|---|---|
| CSE 455 | Computer Vision | 4 |
| EE/CSE 576 | Computer Vision | 3 |
| ME/EE 578 | Convex Optimization | 4 |
| ME 599 | Machine Learning Control | 3 |
| ME 574 | Introduction to Applied Parallel Computing for Engineers | 3 |
| CSE/AMATH 579 | Intelligent Control Through Learning and Optimization | 3 |
II. eScience Community Seminar
- 2 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
Students pursuing the Advanced Data Science Option must complete the above Data Science requirements, which feature a minimum of 9 distinct credits where they would otherwise complete electives in the general degree requirements; all other degree requirements are the same as the general Mechanical Engineering degree.
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. Some classes in the Data Science Option will meet this course requirement, including (though not limited to STAT 416 (4cr), STAT527 (3cr), STAT535 (3cr), STAT509 (4cr), STAT512 (4cr), STAT513 (4cr), AMATH 515 (5cr), AMATH563 (5cr), AMATH582 (5cr), AMATH583 (5cr), etc.
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.