Master of Science in Biomedical Data Science and Modeling
The overall objective of the Master of Science in Biomedical Data Science and Modeling is to provide education and training relevant to professional employment in computational biomedical engineering. Special emphasis is placed on principles of mathematical modeling, machine learning, biostatistics, and bioinformatics. The student must have a minimum 3.0/4.0 GPA in an engineering or science bachelor’s program to be admitted. Candidates should have prior coursework that demonstrates proficiency in math and computer science.
Curriculum
| Requirement | |
| Minimum Credits Required | 32 |
| Maximum 400-Level Credit | 12 |
| Minimum 500-Level Credit | 20 |
| Maximum Transfer Credit | 9 |
| Code | Title | Credit Hours |
|---|---|---|
| Required Courses | (21) | |
| BIOL 550 | Bioinformatics | 3 |
| BME 500 | Introduction to Biomedical Engineering (In Fall 2021, we will change credit hours of BME 500 from 3 to 2) | 3 |
| BME 522 | Mathematical Methods in Biomedical Engineering | 3 |
| or BME 422 | Mathematical Methods for Biomedical Engineers | |
| or CHE 439 | Numerical and Data Analysis | |
| or CHE 535 | Applications of Mathematics to Chemical Engineering | |
| BME 533 | Biostatistics | 3 |
| or BME 433 | Biomedical Engineering Applications of Statistics | |
| or CHE 426 | Statistical Tools for Engineers | |
| or MATH 425 | Statistical Methods | |
| or MATH 476 | Statistics | |
| BME 553 | Advanced Quantitative Physiology | 3 |
| or BME 453 | Quantitative Physiology | |
| BME 560 | Methods in Biomedical Data Science | 3 |
| ECE 566 | Machine and Deep Learning | 3 |
| Elective Courses | (12) | |
| Select 2 courses from the following list (6 credits) plus an additional 6 credits of Math/Life Science/Eng courses recommended from this list. Other courses may be selected with adviser approval prior to course registration. | 12 | |
| Genetics for Engineering Scientists | 3 | |
| Population Genetics | 3 | |
| Introduction to Molecular Imaging | 3 | |
| Neuroimaging | 3 | |
| Reaction Kinetics for Biomedical Engineering | 3 | |
| Quantitative Neural Function | 3 | |
| Advanced Mass Transport for Biomedical Engineers | 3 | |
| Special Problems | 1-6 | |
| Advanced Data Mining | 3 | |
| Deep Learning | 3 | |
| Interactive and Transparent Machine Learning | 3 | |
| Machine Learning | 3 | |
| Applied Optimization for Engineers | 3 | |
| Statistical Signal Processing | 3 | |
| Mathematical Modeling (or) | 3 | |
| Statistical Learning (or) | 3 | |
| Data Preparation and Analysis (or) | 3 | |
| Computational Mathematics I (or) | 3 | |
| Finite Element Methods in Engineering | 3 | |
| Engineering Analysis I | 3 | |
| Engineering Analysis II | 3 | |
| Computational Fluid Dynamics | 3 | |
| Applied Computational Statistics for Analytics | 3 | |
| Total Credit Hours | 33 | |
