M.S. in Biostatistics

Biostatistics uses data to improve health care and public health.

As the healthcare system evolves – both in the U.S. and abroad – health analytics is increasingly necessary. Biostatistics research is needed across the field at biomedical research centers, biotech and pharma companies, health information exchanges, hospital collaborations and research firms.

Biostatistics uses the methods of data analytics to study biological trends in the lab, clinical trials and in the community. Biotechnicians use predictive analytics in order to accurately chart public health issues, and interpret trends and factors linked to disease. In our M.S. in Biostatistics program, you’ll become an expert in using statistical theory and software and in building predictive models. At the end of the program, you’ll be prepared to assess health outcomes and develop systems for optimal healthcare delivery.

What is Biostatistics?

Biostatistics use quantitative data to design and interpret experiments, as well as investigate health trends. Data plays a crucial role in the biological sciences and scientific research in general. As such, biostatisticians are needed across the field to help translate complex data into meaningful insight. As a biostatistician, conducting experiments will be a part of your role. Both biostatisticians and healthcare analysts can help senior administrators model trends, project costs, and track key data points.

What Will I Learn in the Biostatistics Graduate Program?

The M.S. in Biostatistics in a quantitative degree that teaches you how to work with large, complex sets of data. Here are just a few of learning outcomes from the program:

Learn data engineering and how to analyze “big data” in health science.
Master quantitative analysis techniques, like regression, modeling and interpretive skills.
Discover how to provide value for clinical and policy decision makers in health care.
Learn decision making based on probability and risk.
Learn to turn data into insight by extracting meaningful info from large datasets.

Participating in Clinical Studies in the Program

In the M.S. program, we aim to provide opportunities for our students to engage with real-world challenges in the public health field. For example, some of our biostatistics students completed a longitudinal panel study during their final semester, to examine the factors associated with cognitive impairment among older adults in US. They also had a chance to present this study at an annual meeting of the American Public Health Association.

M.S. in Biostatistics Program Format

Our 36-credit M.S. program is available both online and on campus, with full-time and part-time options. As a full-time M.S. student (9 credits a term), you can expect to complete the degree in four semesters. The program admits new students during both the fall and spring terms.

Admissions Process

You don’t have to have any particular undergraduate major to apply, but it’s helpful if you have a background in math, chemistry, information systems, or another related STEM field. Our faculty look for candidates with strong quantitative skills, as well as an interest in public health. Learn more about our admissions requirements.

M.S. in Biostatistics Degree Requirements

To complete the degree program, you must take two core courses, eight required courses, two electives, and an advanced M.P.H. course.

Required core courses:

BISM 5001 Introduction to Biostatistics

This course is an introductory graduate course that presents the fundamental statistical approaches employed in clinical research. Lectures cover basic probability, common distributions, samples and populations, interval estimation, and inferential statistical approaches. By reading medical literature, students learn how statistical techniques are applied to clinical data, and practice summarizing and interpreting analytic results.

EPIM 5002 Introduction to Epidemiology

This course introduces students to the principles and practices of epidemiology and provides them a population-based perspective on health and disease. Students learn the basic measurements of frequency and association and the methods employed in describing, monitoring, and studying health and disease in populations.

Required program courses:

BISM 6011 Statistical Modeling

This course provides an in-depth study of some of the most frequently used statistical packages in the health sciences, as well as statistical modeling principles and techniques.

BISM 6031 Intermediate Biostatistics I

This course is the first part of a two-semester sequence. Topics covered during this semester include: descriptive statistics, probability, estimation, hypothesis testing for one and two samples, non-parametric methods and introduction of hypothesis testing with categorical data. Use of statistical packages is recommended, but not required.

BISM 6032 Intermediate Biostatistics II

This course is the second part of a two-semester sequence. Topics covered during this semester include: hypothesis testing with categorical data, multiple and logistic regression, and statistical methods frequently used in epidemiological studies and clinical trials, including life table analysis, logistic analysis, and relative risk assessment with and without covariates. Use of statistical packages is recommended, but not required.

BISM 6048 Survival Analysis

This course focuses on application of the analysis of time to event data. Topics covered include: introduction to survival analysis, lifetime distribution, censoring, parametric models, non-parametric methods and proportional hazards model. Applications to medical sciences will be discussed.

BISM 6050 Mathematical Statistics I: Probability

This course provides a comprehensive treatment of the fundamental concepts of probability theory. Covered topics related to probability theory include probability, random variables, distribution, probability and density functions, mathematical expectation, functions of random variables, and sampling distributions.

BISM 6050 Mathematical Statistics I: Probability

BISM 6051 Mathematical Statistics II: Inference

This course focuses on topics related to statistical inference and applications. These include point estimation, hypothesis testing, non-parametric statistics, linear models, and analysis of variance.

BISM 6092 Introduction to SAS Programming for Data Management and Analysis

The primary focus of this course is to tach the application of basic SAS programming skills to data management and analysis. In addition, the course will expose students to a range of computing techniques in the management, organization, analysis and presentation of health science data.

BISM 8001 Survey Sampling and Data Analysis

This course examines the method employed in designing and analyzing complex surveys. It explores the major sampling designs and estimation procedures such as simple and stratified random sampling one-state and two-stage cluster sampling, and variance estimation in complex sample surveys. Students use existing datasets and statistical packages to acquire hands-on experience analyzing data from complex surveys.

Elective Courses (select two courses to complete 6 credits):

BISM 6052 Introduction to Clinical Design

This course is designed to provide an overview of randomized clinical trials. Topics include randomization, sample size and power, reliability of measurement, the parallel-groups design, factorial designs, blocking, stratification, analysis of covariance, the cross-over study, Latin square and repeated measures.

BISM 6053 Large Observational Data Analysis

This course covers the complex survey design analysis methods used to analyze large survey datasets such as National Health and Nutrition Examinations Survey and National Health Interview Survey. Topics include the skills of data management, statistical programming, exploratory data analysis, developing statistical models, model checking, statistical simulation and sensitivity analysis.

BISM 7091 Directed Research in Biostatistics

Directed Research provides the opportunity for students to explore a special topic of interest under the direction of a faculty member. An opportunity for advanced study and research in an area chosen by the student in consultation with the professor is provided. Students are also given opportunities to work on special problems in biostatistics.

BISM 7094 M.S. Thesis

The master’s thesis will include independent research and integration of skills acquired by the student through coursework. It also provides evidence of student’s ability to carry out independent biostatistical investigation and to present the results in clear and systematic form. The process includes formulation of research questions, methods to carry out the inquiry and presentation of results of the research. Students must maintain regular contact with their Program and Thesis Advisors during their thesis work.

Advanced M.P.H. Course

You must take a 3 credit advanced M.P.H. course, chosen in consultation with your faculty advisor.

Biostatistics Careers

An M.S. in Biostatistics typically leads to a career as a biostatistician or healthcare analyst. A biostatistician’s role usually involves scientific research and a public health focus. In comparison, healthcare analysts are more involved with healthcare administration, and areas like patient claims and healthcare costs.

While biostatisticians don’t work as lead researchers, they do provide important support on research projects. Biostatisticians work with researchers to ensure that the research questions, research design and statistics are appropriate.

Both career paths are heavily focused on data analysis and monitoring trends that influence high-level decision making. As such, biostatisticians and healthcare analysts usually work closely with public health leaders or administrators.

A biostatistician can earn $104,000 per year in the New York area, and can expect very high job growth (33%) over the next ten years (Indeed, bls.gov).

Healthcare analysts can also look forward to high job growth, especially as tech innovation makes this role common in most healthcare settings. Healthcare analysts make an average salary of $83,000 in New York, according to salary.com. Although it’s possible to be hired for this position with only a Bachelor’s degree, a M.S. degree will help you secure a higher salary and more senior role.