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Learner Reviews & Feedback for Reproducible Research by Johns Hopkins University

4,071 ratings
586 reviews

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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426 - 450 of 568 Reviews for Reproducible Research

By Minki J

Jan 1, 2018

peer assignment is tough, hard and great to learn.

but the course is very general, not that related to the assignment

By Igor T

Feb 26, 2017

Good course. Especially enjoyed final course project. It's really challenging and looks like a real‑life task.

By Mehrdad P

Sep 26, 2019

Course nicely highlighted the importance of reproducible research and the use of markdown and knitr packages.

By Sawyer W

Aug 1, 2017

Good course. Nice overview of concepts of reproduciblity and tools for doing so (sweave, knitr, RPubs)

By Jason C

May 6, 2016

Very good, but maybe not at solid as those before it. Some reproducibility concepts felt a bit vague.

By Nicolás H

Sep 13, 2020

Necesario para conocer, emplear buenas prácticas y darle validez científica a los trabajos realizados

By Asif K

Sep 17, 2018

Very good content and pace. Got good hands on experience, right content and structure of assignments

By Brian F

Jul 8, 2017

Although there is not a lot to this course I like that it covers an area that is often neglected.

By Jeremy J

Sep 11, 2016

Some of the material seems pretty rote but it did introduce some new software and capabilities.

By Luiz E B J

Oct 21, 2019

This is a good course tht open our minds and eyes to the relevance of Reproducible Research.

By Joseph F

Jan 13, 2021

Now I appreciate what is the importance of a reproducible research! Awesome course overall!

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 M R O

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 K

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.