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
- Using statistical models to assess health outcomes and improve healthcare delivery.
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.
Summary of MS in Biostatistics Program Course Requirements
Prerequisites/Admission requirement (preferred): Calculus (preferably 3 semesters including multivariable), Linear algebra
All students are strongly encouraged to attend the biweekly seminar every semester.
The total credits for MS in Biostatistics will be 43 credits (25 credits of core courses + 12 credits of electives + 6 thesis credits).
The program will provide students:
- advanced coursework in biostatistical modeling, health data computing and health data science
- research experience and a thorough preparation for PhD in Biostatistics
- statistical reasoning and enhanced health data science skills necessary for future careers in academia, research institutions, government agencies, pharmaceutical and biotechnological industries.
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 |