The Biostatistics Specialty Track builds on the core curriculum in the Master in Public Health (MPH) program to offer students a practical foundation in biostatistics through courses in statistical inference, probability, multivariable models, analysis of longitudinal and time to event (or survival) data, genetics, and statistical computing. This foundation is meant to be a springboard for launching a successful career in clinical research, for both clinical researchers seeking quantitative skills, and those seeking careers as biostatisticians.
We offer a practical foundation in biostatistics through courses in important areas such as statistical inference, probability, multivariable models, analysis of longitudinal and time to event (or survival) data, genetics, and statistical computing. We intend this foundation to be a springboard to launching a successful career in clinical research, for both clinical researchers seeking quantitative skills and those seeking careers as biostatisticians. Students in this track complete a capstone project as their culminating experience.
Please note that while the MPH degree is typically a two-year undertaking, students in the Biostatistics Specialty Track who enroll in the spring terms should allow more than two years to complete the MPH program.
To make sure our students develop the skills necessary to be successful and productive in the field of public health, and especially in the area of biostatistics, we have developed a list of skills and content areas for the students in this specialty track:
- Demonstrate ability to apply biostatistics and engage in collaborative public health research.
- Apply the necessary quantitative, logical, and computational skills to successfully collaborate within clinical research teams.
- Translate clinical questions into statistical hypotheses.
- Effectively summarize public health data using both numerical and graphical techniques.
- Use basic probability concepts and optimal study designs.
- Devise effective means of data collection.
- Develop analytical strategies that take account of the specific qualities of data to be analyzed, sources of variation, and assumptions required.
- Interpret quantitative results and their implications for public health.
- Effectively communicate complicated statistical concepts and results to clinical colleagues and community partners.