Train the next generation of leaders in health data science with enhanced public health data analysis skills
The MS in Biostatistics program is designed to equip students with the biostatistical and analytical skills necessary to interpret and conduct research in the public health, and biomedical fields. The program emphasizes biostatistical methodology as well as practice of biostatistics and data science in public health.
Perfect for those interested in
- Analyzing health trends and outcomes in areas like chronic diseases and environmental health.
- Collaborating with researchers in various medical and scientific fields.
- Designing research studies and collecting and analyzing data from medical experiments
- Machine learning and big data computing
- Using statistical models to assess health outcomes and improve healthcare delivery.
- Receiving thorough preparation for a doctoral program
Career Paths
Graduates from this program will work as:
- Data scientists and analysts
- Statisticians
- Consultants
Program Overview
See all Epidemiology and Biostatistics graduate student resources.
The program will provide students:
- Understand the theoretical foundations of biostatistical methods.
- Critically review scientific literature and evaluate appropriateness of statistical methods and their applications.
- Conduct advanced statistical inferences that are appropriate to specific study designs and data structures.
- Use statistical analytical software to perform advanced statistical procedures and demonstrate skills in public health data management.
- Prepare written reports of statistical analyses for journal publication, grant applications, and review by regulatory agencies.
- Gain methodology research experience or collaborative experience in applied biostatistics.
The total credits for the MS in Biostatistics will be 43 credits (25 credits of core courses + 12 credits of electives + 6 thesis credits).
Sample Full-Time Course Sequence (2 years without summer courses)
Fall Semester 1 (12 credits)
| COURSE | TITLE | CREDITS |
|---|---|---|
| EPIB650 | Biostatistics I | 3 |
| EPIB610 | Foundations of Epidemiology | 3 |
| EPIB697 | Public Health Data Management | 3 |
| EPIB667 | Applied Machine Learning with Python | 3 |
Spring Semester 1 (10 credits)
| COURSE | TITLE | CREDITS |
|---|---|---|
| EPIB651 | Applied Regression Analysis | 3 |
| EPIB653 | Applied Survival Data Analysis | 3 |
| ELECTIVE | Selected with Advisement | 3 |
| SPHL601 | Core Concepts in Public Health | 1 |
Fall Semester II (12 Credits)
| COURSE | TITLE | CREDITS |
|---|---|---|
| EPIB652 | Categorical Data Analysis | 3 |
| EPIB655 | Longitudinal Data Analysis | 3 |
| ELECTIVE | Selected with Advisement | 3 |
| ELECTIVE | Selected with Advisement | 3 |
Spring Semester II (9 Credits)
| COURSE | TITLE | CREDITS |
|---|---|---|
| EPIB799 | Master's Thesis Research | 6 |
| ELECTIVE | Selected with Advisement | 3 |