Profile

Roger D. Peng, PhD

Associate Professor, Biostatistics

Bio

Roger D. Peng is a Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health and a Co-Editor of the Simply Statistics blog. He received his Ph.D. in Statistics from the University of California, Los Angeles and is a prominent researcher in the areas of air pollution and health risk assessment and statistical methods for environmental data. He is the recipient of the 2016 Mortimer Spiegelman Award from the American Public Health Association, which honors a statistician who has made outstanding contributions to health statistics. He created the course Statistical Programming at Johns Hopkins as a way to introduce students to the computational tools for data analysis. Dr. Peng is also a national leader in the area of methods and standards for reproducible research and is the Reproducible Research editor for the journal Biostatistics. His research is highly interdisciplinary and his work has been published in major substantive and statistical journals, including the Journal of the American Medical Association and the Journal of the Royal Statistical Society. Dr. Peng is the author of more than a dozen software packages implementing statistical methods for environmental studies, methods for reproducible research, and data distribution tools. He has also given workshops, tutorials, and short courses in statistical computing and data analysis.

Courses

Statistical Inference

Regression Models

Exploratory Data Analysis

Developing Data Products

Managing Data Analysis

Reproducible Research

JHU

Statistical Inference

Executive Data Science Capstone

Exploratory Data Analysis

The R Programming Environment

Data Science in Real Life

Practical Machine Learning

R Programming

R Programming

Building R Packages

Reproducible Research

The Data Scientist’s Toolbox | 数据科学家的工具箱

Mastering Software Development in R Capstone

Building Data Visualization Tools

Data Science Capstone

Regression Models

Building a Data Science Team

Data Science Capstone

Advanced R Programming

The Data Scientist’s Toolbox

The Data Scientist’s Toolbox

Developing Data Products

A Crash Course in Data Science

Getting and Cleaning Data

Getting and Cleaning Data

The Unix Workbench

Computing for Data Analysis

Practical Machine Learning