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
This course is part of multiple programs.
Instructors: Roger D. Peng, PhD
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(4,174 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
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There are 4 modules in this course
This week will cover the basic ideas of reproducible research since they may be unfamiliar to some of you. We also cover structuring and organizing a data analysis to help make it more reproducible. I recommend that you watch the videos in the order that they are listed on the web page, but watching the videos out of order isn't going to ruin the story.
What's included
9 videos4 readings1 assignment
This week we cover some of the core tools for developing reproducible documents. We cover the literate programming tool knitr and show how to integrate it with Markdown to publish reproducible web documents. We also introduce the first peer assessment which will require you to write up a reproducible data analysis using knitr.
What's included
9 videos1 assignment1 peer review
This week covers what one could call a basic check list for ensuring that a data analysis is reproducible. While it's not absolutely sufficient to follow the check list, it provides a necessary minimum standard that would be applicable to almost any area of analysis.
What's included
10 videos
This week there are two case studies involving the importance of reproducibility in science for you to watch.
What's included
5 videos1 reading1 peer review
Instructors
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Recommended if you're interested in Data Analysis
S.P. Jain Institute of Management and Research
Universiteit Leiden
The University of North Carolina at Chapel Hill
University of Michigan
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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!
Reviewed on Mar 30, 2022
I took this course as part of the Data Science specialization without any real expectation and realized that this subject is probably one of the most important in data analysis.
Reviewed on Feb 2, 2017
While I'm pretty sure this course is VERY important for researchers, it is not very useful for my area (IT) and I would like to know this before taking the course. Thank you.
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