The graduate programs in Biostatistics offers comprehensive course work leading to a Master of Science (Sc.M.); a Master of Arts (A.M.) degree for students in the 5th-year Master's program and Brown's Open Graduate Education Program; and the Doctor of Philosophy (Ph.D.) degrees. The Ph.D. program is intended to enable graduates to pursue independent programs of research.
Full details for the Biostatistics Doctoral Program can be found at https://www.brown.edu/academics/public-health/biostats/academics/doctoral-program.
The Sc.M. program provides training for application of advanced methodology in professional and academic settings. The Department of Biostatistics offers a 5th-Year Master's (A.M. degree) which is available to Brown Undergraduates. Required courses for the Biostatistics Master's degree program are listed below. Additional details can be found on the Department's webpage: https:\\brown.edu\biostatistics
For more information on admission and program requirements, please visit https://sph.brown.edu/admission-aid
The graduate programs in Biostatistics are designed to provide training in theory, methodology, and practice of statistics in biology, public health, and medical science. The programs provide comprehensive training in theory and methods of biostatistics, but is highly interdisciplinary and requires students to acquire expertise in a field of application.
Requirements for the ScM
| Required Courses -ScM (7 biostatistics plus PHP 2000) | ||
| STAT 2515 | Fundamentals of Probability and Statistical Inference | 1 |
| or STAT 2520 | Statistical Inference I | |
| STAT 2514 | Applied Generalized Linear Models | 1 |
| STAT 2516 | Applied Longitudinal Data Analysis | .5 |
| STAT 2517 | Applied Multilevel Data Analysis | .5 |
| STAT 2550 | Practical Data Analysis | 1 |
| STAT 2560 | Statistical Programming with R | 1 |
| STAT 2610 | Causal Inference and Missing Data | 1 |
| STAT 2650 | Statistical Learning and Big Data | 1 |
| PHP 2000 | Foundations of Public Health (Online) | 0 |
| Electives (3 Courses) | 3 | |
| Statistical Electives | ||
| Causal Inference | ||
| Bayesian Statistical Methods | ||
| Statistical Inference II | ||
| Linear Models | ||
| Analysis of Lifetime Data | ||
| Generalized Linear Models | ||
| Statistical Methods in Bioinformatics, I | ||
| Graduate Independent Study and Thesis Research | ||
| Design of Experiments | ||
| Simulation Models for Public Health Decision Making | ||
| Epidemiology Electives | ||
| Introduction to Methods in Epidemiologic Research | ||
| Foundations in Modern Epidemiologic Methods | ||
| Intermediate Methods in Epidemiologic Research | ||
| Programming and Data Science Electives | ||
| Methods in Informatics and Data Science for Health | ||
| Machine Learning | ||
| Deep Learning | ||
| Design and Analysis of Algorithms | ||
| Computational Molecular Biology | ||
| Algorithmic Foundations of Computational Biology | ||
| Total Credits | 10 | |
Requirements for the AM
| Required Courses -AM (4 biostatistics plus PHP 2000) | ||
| STAT 2515 | Fundamentals of Probability and Statistical Inference | 1 |
| STAT 2514 | Applied Generalized Linear Models | 1 |
| STAT 2550 | Practical Data Analysis | 1 |
| STAT 2560 | Statistical Programming with R | 1 |
| PHP 2000 | Foundations of Public Health (Online) | 0 |
| Electives (4 Courses) | 4 | |
| Statistical Electives | ||
| Clinical Trials Methodology | ||
| Applied Longitudinal Data Analysis | ||
| Applied Multilevel Data Analysis | ||
| Bayesian Statistical Methods | ||
| Statistical Inference II | ||
| Linear Models | ||
| Analysis of Lifetime Data | ||
| Generalized Linear Models | ||
| Causal Inference and Missing Data | ||
| Statistical Methods in Bioinformatics, I | ||
| Statistical Learning and Big Data | ||
| Graduate Independent Study and Thesis Research | ||
| Design of Experiments | ||
| Simulation Models for Public Health Decision Making | ||
| Epidemiology Electives | ||
| Introduction to Methods in Epidemiologic Research | ||
| Foundations in Modern Epidemiologic Methods | ||
| Intermediate Methods in Epidemiologic Research | ||
| Programming and Data Science Electives | ||
| Methods in Informatics and Data Science for Health | ||
| Machine Learning | ||
| Deep Learning | ||
| Design and Analysis of Algorithms | ||
| Computational Molecular Biology | ||
| Algorithmic Foundations of Computational Biology | ||
| Total Credits | 8 | |