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Learner Reviews & Feedback for Reproducible Research by Johns Hopkins University

4,167 ratings

About the 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 12, 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.


Aug 19, 2020

A very important course that greatly improved my ability to communicate the findings of any sort of data analysis that I perform. This is a critical skill to acquire to "deliver the means."

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451 - 475 of 586 Reviews for Reproducible Research

By shivangi p

Aug 3, 2020

It is a nicely structured course with introduction to R and gives a brief of data science.

By Francisco R

Mar 9, 2019

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

By Korwin A

Feb 6, 2016

Great class with excellent supporting material. A little chaotic, but very good overall.

By Amol M

May 18, 2020

This course provides an easy way out to create reports which can be shared with others.

By Thej

Mar 12, 2019

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

By Jean-Philippe M

Jun 30, 2019

Lack of practical cases. The two cases are not really interesting and lack of details.

By Ян Ш

Jul 16, 2018

The final task can be interpreted too widely. Do I need to pre-clean fuzzy data?

By Freddie K

Apr 16, 2017

Great course! Starting to put pieces from earlier courses together into a whole.

By Tim j

Apr 5, 2017

decent course, it is as long as you make it but start the final project early

By Eduardo S B

Nov 27, 2019

The course is nice. However, I think the last assignment is simply too much.

By Sanjay J

Mar 6, 2017

I think it is one of the easiest and most important courses in Data science.

By Huw H

Oct 30, 2017

An interesting course on a topic that often doesn't get a lot of attention.

By Thomas G

Nov 30, 2016

quite redondant with what was done before but very usefull and clear course

By Pieter v d V

May 20, 2018

Nice to have seen once. Could have been condensed into two or three weeks.

By Herminio V

Sep 13, 2016

Very useful material, and great use for presenting data analysis results.

By Savitri

Jan 28, 2019

Nice and the content of the course will help you a lot to work on

By Ashish S

May 17, 2016

This would be very effective for my personal skill enhancement.

By Ankush K

Jan 8, 2018

It's a great course on a topic that is not addressed enough.

By Kennan Y

Jun 13, 2017

More details are needed about the R/knitr specific details

By Juan G

May 27, 2020

Nice Course, it teaches R Markdown with RStudio and Knitr

By Angel M

Mar 11, 2021

Nice course about how present data and make reports.

By Peter E

Sep 15, 2018

One of Peng's lectures was a little quick and loose

By Zhongrun X

Jul 30, 2016

Good but not that deep. This should be in 2 weeks.

By Christopher G

Aug 31, 2016

Material was very interesting and I learned a lot

By Ray W

Mar 2, 2016

Good to know the principles here. Thanks.