Online Graduate Certificate in Biostatistics

College of Public Health
Advance your knowledge in biostatistics and epidemiology for public health. 

Ohio State’s online Graduate Certificate in Biostatistics offers a unique blend of epidemiology and biostatistics for public health. You will learn survival analysis techniques; assess and improve study designs commonly used in public health research; and interpret standard regression models using industry-standard statistical software. Accredited by the Council on Education in Public Health, this 100% online Graduate Certificate in Biostatistics is designed to equip you with the background in epidemiologic study designs, biostatistics, and statistical computing required for public health data analysis in the public and private sectors.  

Why Choose Ohio State’s Online Graduate Certificate Program in Biostatistics? 

This online graduate certificate offers a rigorous curriculum tailored to applications in population health. You will gain practical hands-on experience by using a wide array of statistical tools such as R, Stata, and SAS to analyze public health data. This will equip you with skills that are highly sought after in job postings for roles like management analysts, medical scientists, statisticians, and epidemiologists. 

Campus Requirements: NONE – 100% online  

Class Format: Asynchronous – you can complete coursework each week on your own schedule.  

Credit Hours Required: 14 

Cost Per Credit Hour: $812.06 per credit hour (includes instructional and general fees). See full breakdown of costs here.

Admission Requirements: Minimum of a bachelor’s degree. GRE not required.   

Time to Completion: 3 semesters, Part-time  

Sample Courses


Principles of Epidemiology

Introduction to the nature and scope of epidemiology; survey of basic epidemiological methods and their application to selected acute and chronic health problems.

Regression Methods for the Health Sciences

Multivariate regression methods on four problems – including logistic regression, count data regression, time‐to‐event analysis, and repeated measures data. Focused on model interpretation, hypothesis testing, confidence interval, confounding, interaction, and model selection. Illustrated with real data sets and analysis assignments.
STA 5730

Introduction to R for Data Science

Introduces underlying concepts of the R programming language and R package ecosystem for manipulation, visualization, and modeling of data, and for communicating the results of and enabling replication of their analyses.

Featured Faculty

Assistant Professor
Assistant Professor

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