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Reproducible Research, Johns Hopkins University

3,108 ratings
444 reviews

About this Course

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....

Top reviews


Feb 13, 2016

My favorite course, at least it gives me an argument why scripted statistics is awesome and can be applied to a number of data related activities. Recycling chunks of code has proven useful to me.


Jun 23, 2017

Of course, I liked this course. There was even an extra non-graded assignment. Plus two graded assignments. Quality instruction videos and lots of practice. Everything a learner needs.

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429 Reviews

By Sri Hari

Apr 21, 2019


By carlos j martinez

Apr 12, 2019

Great course, good lectures. I learned a lot of usable skills.

By Naren Rajiv Bakshi

Apr 08, 2019

Would definitey recommend this, it covers an important aspect of research for Data Scientists.

By Andrew

Apr 07, 2019

One of my favorite courses in the specialization so far.

By Fidel Serrano Candela

Mar 20, 2019

Very good course

By Paul Ringsted

Mar 13, 2019

Along with the principles of "reproducible research", the primary tool introduced in this course is knitr to produce reproducible research papers and Rpubs for publishing papers. I think this specialization covers RMarkdown 3 different times. Assignments were good, at this stage you start to produce proper papers on an analysis topic which is very much needed before hitting the statistics/regression lectures; however this material can be compressed and needs to be combined with the 9th course which covers Rmarkdown/RStudio again.

By Thej Kiran Ravichandran

Mar 12, 2019

Nothing serious in this course! Rmd is a good tool to work with! and get familiar with!

By Francisco Miguel Rivas Ortega

Mar 09, 2019

It was very useful for me, now I know the importance of making data analysis reproducible.

By Matthew Stetz

Mar 05, 2019

I often feel like people completely ignore the "science" aspect of data science (read any data science career question on quora for example). This course does an excellent job of introducing key aspects of the scientific method that you might not have encountered if you've never done an experiment before. The final project is a lot of work (mostly data cleaning) but very fun and informative.

By Glenn Walters

Mar 04, 2019

Favorite course so far. Really enjoyed working on the projects. They were very helpful in helping to reinforce the material.