Graduate Certificate in Biostatistics offered by The Ohio State University
- Public University
- 1666 acre campus
- Estd. 1870
Graduate Certificate in Biostatistics at The Ohio State University Overview
Duration | 1 year |
Start from | Start Now |
Total fee | ₹10.35 Lakh |
Mode of learning | Online |
Official Website | Go to Website |
Course Level | PG Certificate |
Graduate Certificate in Biostatistics at The Ohio State University Highlights
- Earn a certificate after completion of course from Ohio State University
Graduate Certificate in Biostatistics at The Ohio State University Course details
Individuals with a bachelor's degree in statistics, mathematics, computer science, biology, public health, or a related field
Evaluate study designs and analytical methods commonly used in public health research
Fit and interpret standard regression models using statistical software
Apply data management techniques to prepare data for statistical analysis
The biostatistics certificate and minor program teaches working professionals and students how to conduct biostatistical analyses and understand study designs in public health research and data analysis
This 14-credit program has a strong focus on biostatistics and public health, critical background in epidemiology and has flexibility in learning statistical software (R, SAS and Strata)
These skills are essential for today’s statisticians, epidemiologists, environmental health scientists and specialists, management analysts and medical scientists
Graduate Certificate in Biostatistics at The Ohio State University Curriculum
PUBHEPI 6410 – 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
PUBHBIO 6250 – 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