MS in Biostatistics
Advancing public health, medicine, and biology through statistics.
Biostatistics is among the fastest growing careers in the US. Biostatistics is for data analysis with a focus on human health. We develop and apply statistical and computational methods to advance public health and understand human biology. We work in areas such as clinical trials, observational studies of human disease, statistical genetics, and health policy.
Combine your love of math and science to improve the health and well-being of individuals and communities! Opportunities await you in academia, government, and the private sector.
With a degree in Biostatistics you can work in many areas of application. Some our faculty’s areas of expertise include:
- Tracking epidemics
- Keeping superbugs out of hospitals
- The risk of heart disease or breast cancer
- Estimating maternal mortality globally
Check us out! Our faculty work with the U.S. government, major hospital systems, and global organizations on projects ranging from COVID-19 Forecasting to international HIV intervention to breakthrough metabolomics research examining the risk of chronic health conditions like heart disease and breast cancer.
Benefits list
Interested in Flexible Learning?
We have many courses that are offered both in-person and remotely via Zoom. These include courses required for our MS program. These include office hours and teaching help for on-campus and remote students. See a list of our graduate courses here.
Please contact: Elizabeth Cook, edcook [at] umass [dot] edu (edcook[at]umass[dot]edu)
Collaborative Research
Biostatistics faculty foster a rich environment for transdisciplinary research collaborations. Areas of expertise include Bayesian methods, biomarker discovery, causal inference, machine learning, observational studies, randomized trials, and time-to-event outcomes.
CEPH Accreditation
Every program in the School of Public Health and Health Sciences is fully accredited by the Council of Education for Public Health, a key benefit of attending UMass.
Featured classes
In this course, students learn how to construct Bayesian models to relate (potentially complex) data to scientific questions, to fit such models fitting using statistical programs (R and STAN), to interpret model results, and check model assumptions.
In this course, we apply the tools of mixed models to examples like longitudinal repeated measures and clustered randomized trials. Students use R and SAS software to hone their skills in application of the methods and interpretation of results.
Featured faculty
Raji Balasubramanian
Associate Chair, Department of Biostatistics and Epidemiology
Metabolomic studies, pediatric HIV studies, measurement error in self-reported outcomes, study design, high-dimensional data and analysis of biological networks
Zhengqing Ouyang
Focus on statistical genomics and bioinformatics; data integration; network modeling; manifold learning; predictive learning.
Chi Hyun Lee
Focus on complex survival data analysis, biased sampling design, biomarker identification and evaluation.
Jing Qian
Focus on survival analysis under complex sampling; biomarker evaluation and risk prediction; analytic methods for high-dimensional data.
Nicholas Reich
Director, COVID-19 Forecast Hub
Director, Influenza Forecasting Center of Excellence
Focus on forecasting, machine learning, time series, infectious disease modeling, cluster-randomized trials.
Application information & deadlines
Prospective students must apply first through the SOPHAS site and then the UMass Graduate School Supplemental Application, which will be emailed to you upon completion of the SOPHAS application.
Application Deadline
Applications for the fall term are due by Feb. 1.
Biostatistics
Advancing public health, medicine, and biology through statistics