The Master of Science (MS) in Biomedical Data Science and AI is a 37-credit-unit program that offers excellent training in informatics, data science, and biostatistics for students who earned undergraduate or higher degrees with majors in Mathematics, Statistics, Computer Science, Biomedical Engineering, or other related fields. It prepares graduates for rewarding employment in academia and industry and for further graduate studies.
It offers three distinct concentrations, allowing students to tailor their training to align with their interests and career goals. After completing a shared first semester of foundational coursework, students select one of the following tracks to dive deeper into advanced topics: 1) Biostatistics; 2) Biomedical Informatics; or 3) Data Science. Each concentration equips students with the tools and perspectives needed to lead in the rapidly evolving landscape of healthcare, research, and artificial intelligence. Whether the student's passion lies in clinical applications, statistical research, or AI innovation, our program provides a robust and flexible pathway to success.
All students will participate in a practicum project that will have an option to transition into a thesis pathway. Each option requires a two-semester commitment in which students will earn 3 credit units per semester. We require a minimum of 150 hours per semester for the practicum and thesis components to ensure that students gain substantial and meaningful hands-on experience.
Core Courses (16 credit units)
| Code | Title | Units |
|---|---|---|
| BDSAI 5001 | R for Biomedical Sciences | 1 |
| BDSAI 5002 | Python for Biomedical Sciences | 1 |
| BDSAI 5003 | Introduction to Biomedical Data Science | 2 |
| BDSAI 5004 | Introduction to Biomedical Informatics | 2 |
| BDSAI 5005 | Fundamentals of Biostatistics | 2 |
| BDSAI 5006 | Research Design for Biomedical Data Science | 2 |
| BDSAI 5301 | Seminar Series for Biomedical Sciences | 1 |
| BDSAI 5302 | Journal Club for Biomedical Sciences | 1 |
| BDSAI 5303 | Scientific Communication for Biomedical Sciences | 2 |
| BDSAI 5304 | Ethics for Biomedical Data Science | 2 |
| Total Units | 16 | |
Concentration Courses (6 credit units)
Biostatistics
| Code | Title | Units |
|---|---|---|
| BDSAI 5131 | Survival Analysis | 3 |
| BDSAI 5132 | Statistical Analysis of Longitudinal Data | 3 |
Biomedical Informatics
Students choose two of the following courses:
| Code | Title | Units |
|---|---|---|
| BDSAI 5121 | Electronic Health Records: Foundations | 3 |
| BDSAI 5122 | Electronic Health Records Analytics | 3 |
| BDSAI 5123 | Advanced Multi-Omic Modeling and Systems Analysis | 3 |
Data Science
| Code | Title | Units |
|---|---|---|
| BDSAI 5111 | Data Visualization | 3 |
| BDSAI 5112 | Advanced AI Topics in Biomedical Informatics: Deep Learning, Transformers, and Large Language Models | 3 |
Experiential Learning (6 credit units)
| Code | Title | Units |
|---|---|---|
| BDSAI 5901 | Biomedical Data Science and AI Practicum | 3 |
| BDSAI 5902 | Biomedical Data Science and AI Mentored Research | 3 |
Elective Courses
All students will complete 9 credit units of electives. This structure provides flexibility, enabling students to customize their educational path while still benefiting from a broad knowledge base. Electives allow students to tailor their education to their specific interests in advanced topics within their field. Students receive guidance for making elective choices that best suit their study area.
Academic Policies
Academic policies for I2DB degree programs can be found in the Student Handbook.
Prospective Students
Those interested in applying or who would like more information may contact the program recruiter.
Washington University School of Medicine
Institute for Informatics, Data Science and Biostatistics (I2DB)
660 S. Euclid Ave., MSC 8067-0013-05
St. Louis, MO 63110-1093