Fight Public Health Hazards with Advanced Data Analysis
Defend the well-being of diverse populations by analyzing geographic, demographic, and biological information to solve critical problems in public health fields and medical and pharmaceutical industries.
The MS in Biostatistics program provides you with a focused graduate curriculum that includes coursework in various methods of advanced mathematical analysis that can be readily applied to epidemiologic research.
Work alongside renowned faculty as you design clinical experiments, examine big data, identify trends, and perform statistical analyses that lead to improved public health outcomes, health services, and health policies.
Program of Study
Courses below reflect those commonly taken by students in this program. Your advisor will recommend courses based on your background and professional goals.
- Introduction to the Theory of Statistics I and II
- Methods of Data Analysis I and II
- Computer Programming and Data Management
- One or more of the following:
- Introduction to Bayesian Inference
- Design of Experiments
- Sample Survey of Methodology
- Analysis of Categorical Data
- Introduction to Stochastic Processes (biometry focus)
- Supporting Electives (up to 12 credits)
- Master's Seminar in Biostatistics (required)
Take three extra courses in biology, epidemiology, and public health subjects that interest you.
Learn first-hand from faculty who actively address public health issues facing New York State (NYS). More than 75% of faculty in the School of Public Health hold appointments with the NYS Department of Health, which provides many research opportunities in the labs of the internationally recognized Wadsworth Center.
You may also work with biotechnology firms located on UAlbany’s Health Sciences Campus and with faculty in the University’s Cancer Research Center, Prevention Research Center, Public Health Preparedness Center, Cardiac Quality Improvement Initiative, and Center for Health Workforce Studies.
Many UAlbany biostatistics graduates are employed with the NYSDOH, Environmental Protection Agency (EPA), Centers for Disease Control and Prevention (CDC), Department of Labor (DOL), and other government agencies.
Others have pursued careers at companies such as General Electric (GE), GlaxoSmithKline, Center for Applied Genomics and Technology, DuPont Corporation, and Memorial Sloan-Kettering Cancer Center.
Learning objectives that UAlbany students are expected to attain through their course of study within their academic program.
- Describe the roles biometry and statistics serves in the discipline of public health.
- Describe basic concepts of probability, random variation and commonly used statistical probability distributions.
- Describe preferred methodological alternatives to commonly used statistical methods when assumptions are not met.
- Distinguish among the different measurement scales and the implications for selection of statistical methods to be used based on these distinctions.
- Apply descriptive techniques commonly used to summarize public health data.
- Apply common statistical methods for inference.
- Apply descriptive and inferential methodologies according to the type of study design for answering a particular research question.
- Apply basic informatics techniques with vital statistics and public health records in the description of public health characteristics and in public health research and evaluation.
- Interpret results of statistical analyses found in public health studies.
- Develop written and oral presentations based on statistical analyses for both public health professionals and educated lay audiences.
- Capability to build statistical model over real health data.
- Estimate and compare efficiency of models.
- Use statistical software to analyze health –related data.
- Be able to explain the advantages of a Bayesian data analysis.
- Perform univariate data analysis for continuous and categorical variables.
- Interpret inferential findings within Bayesian thinking (e.g. credible intervals, hypothesis testing).
- Conduct inference via posterior simulation and simulations tool.