Statistical Computing

Public health and healthcare delivery is constantly inundated with data from sources, including clinical trials, observational and longitudinal studies, genomics, and more. Having a firm grasp of the statistical computing skill-set can help you analyze this information effectively, and translate the flow of data into meaningful information that can inform decisions to improve the health of populations.

Featured Programs

Essentials of Biostatistics with JMP® (SI 19)

Online 40 Hours Begins June 10, 2019 Registration Closed
Taught By
Lisa Sullivan, PhD, Associate Dean for Education, Professor of Biostatistics, BUSPH

Get a comprehensive introduction to the use of biostatistics in the field of public health, while learning to compute and interpret descriptive and inferential statistics using SAS JMP®. (Program typically completed in 4 weeks.)

Introduction to SAS (SI 19)

On Campus 20 Hours June 17-19, 2019 Registration Closed
Taught By
Carly Milliren, MPH, Teaching Professional, Biostatistics, BUSPH

This program introduces students to statistical computing with focus on the SAS package. Emphasis is on manipulating data sets and basic statistical procedures such as t-tests, chi-square tests, and correlation.

Systematic Reviews for Public Health and Biomedical Research (SI 19)

On Campus 20 Hours June 10-12, 2019 Registration Closed
Taught By
Ludovic Trinquart, PhD, Assistant Professor, Biostatistics, BUSPH
Michael P. LaValley, PhD, Professor, Biostatistics, BUSPH

Systematic reviews (SR) are increasingly used to inform clinical and public health practice. SR seek to collate all evidence that fits pre-specified eligibility criteria in order to address a specific research question.

Meta-Analysis for Public Health and Biomedical Research Using R (SI 19)

On Campus 14 Hours June 13-14, 2019 Registration Closed
Taught By
Ludovic Trinquart, PhD, Assistant Professor, Biostatistics, BUSPH
Michael P. LaValley, PhD, Professor, Biostatistics, BUSPH

Meta-Analysis is the gold standard statistical approach to combine the results of multiple studies and to examine sources of heterogeneity and potential biases.