Reproducible Research

Johns Hopkins University

Learn the concepts and tools behind reporting modern data analyses in a reproducible manner. This is the fifth course in the Johns Hopkins Data Science Specialization.

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.

Syllabus

In this course you will learn to write a document using R markdown, integrate live R code into a literate statistical program, compile R markdown documents using knitr and related tools, and organize a data analysis so that it is reproducible and accessible to others.

Recommended Background

R Programming, Data Scientist's Toolbox

Course Format

There will be weekly video lectures,  quizzes, peer assessments.

As part of this class you will be required to set up a GitHub and an RPubs account. GitHub is a tool for collaborative code sharing and editing. RPubs is a web site for publishing data analyses from R. During this course and other courses in the Specialization you will be submitting links to files you publicly place in your GitHub account as part of peer evaluation. If you are concerned about preserving your anonymity you will need to set up an anonymous GitHub and RPubs accounts and be careful not to include any information you do not want made available to peer evaluators.

FAQ

Will there be more Data Science Specialization sessions after December 2015?
Yes, the specialization is moving to the new Coursera platform in January 2016.

Will my current Data Science Specialization progress carry over to the new platform?

Yes, the certificates you earned in the current platform will still be valid after the move to the new platform in January 2016.

How do the courses in the Data Science Specialization depend on each other?

We have created a handy course dependency chart to help you see how the nine courses in the specialization depend on each other.

Will I get a Statement of Accomplishment after completing this class?

Free statements of accomplishment are not offered in this course. If you are not enrolled in Signature Track, participation and performance documentation will be reported on your Accomplishments page, but you will not receive a signed statement of accomplishment.

What resources will I need for this class?
A computer is needed on which the R software environment can be installed (recent Mac, Windows, or Linux computers are sufficient).

How does this course fit into the Data Science Specialization?

This is the fifth course in the Specialization. We strongly recommend that you first take The Data Scientist's Toolbox and R Programming before taking this course.
会期:
  • 2015年12月07日, 4 星期
  • 2015年11月02日, 4 星期
  • 2015年10月05日, 4 星期
  • 2015年9月07日, 4 星期
  • 2015年8月03日, 4 星期
  • 2015年7月06日, 4 星期
  • 2015年6月01日, 4 星期
  • 2015年5月04日, 4 星期
  • 2015年4月06日, 4 星期
  • 2015年3月02日, 4 星期
  • 2015年2月02日, 4 星期
  • 2015年1月05日, 4 星期
  • 2014年12月01日, 4 星期
  • 2014年11月03日, 4 星期
  • 2014年10月06日, 4 星期
  • 2014年9月01日, 4 星期
  • 2014年8月04日, 4 星期
  • 2014年7月07日, 4 星期
  • 2014年6月02日, 4 星期
  • 2014年5月05日, 4 星期
  • 2014年4月01日, 4 星期
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  • 语言: 英语 Gb

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