University at Buffalo School of Engineering and Applied Sciences 57 courses 72 total credits
The Computational and Data Enabled Sciences PhD addresses pressing research challenges across various fields, such as materials modeling and design, natural disasters, smart electric grids, and medical technologies, by integrating large-scale computing and big data analysis. Through the collaboration of faculty members from departments including Mechanical and Aerospace Engineering, Chemical and Biological Engineering, Computer Science and Engineering, Math, Physics, and Chemistry, the program trains scientists to effectively analyze big data and multisource datasets. By combining expertise in computing, data sciences, and domain sciences, students are equipped with the necessary skills to tackle complex problems and solve grand challenges in their respective fields.
| Total Credits | 72 credits |
Data Science
Applied Numerical Mathematics
High Performance and Data Intensive Computing
Data Science
MAE 700 Bayesian Methods in Engineering Applications (3 credits).
Applied Numerical Mathematics
High Performance and Data Intensive Computing
| Code | Course Title | Credits |
|---|---|---|
| CDA 601 | Individual Problems | 1-3 |
| IE 551 | Simul & Stochastic Models | 3 |
| MAE 610 | High Perform Computing 2 | 3 |
| STA 545 | Statistical Data Mining I | 3 |
| STA 546 | Data Mining II | 3 |
Culminating Experience
| Code | Course Title | Credits |
|---|---|---|
| MAE 660 | Dissertation | 1-12 |
| Total Culminating Experience | 0-12 credits | |
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