This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This course will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.

Reproducible Research

Reproducible Research
This course is part of multiple programs.



Instructors: Roger D. Peng, PhD
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4,186 reviews
What you'll learn
Organize data analysis to help make it more reproducible
Write up a reproducible data analysis using knitr
Determine the reproducibility of analysis project
Publish reproducible web documents using Markdown
Skills you'll gain
Tools you'll learn
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Reviewed on Feb 13, 2020
Highly recommended for beginners to learn the basics of Data Science, Re-producibility and how to write a good report around the analysis done by you as a data analyst.
Reviewed on Aug 20, 2017
I personally got a lot out of this, both from a philosophical perspective and a nuts-and-bolts perspective. And I got to practice a lot of stuff learned on earlier courses.
Reviewed on Dec 15, 2016
You will learn how to use a very valuable tool in this class; its name is R Markdown. Besides Prof. Peng explains very well the importance of reproducible research. Nice course!
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