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Learner Reviews & Feedback for Reproducible Research by Johns Hopkins University

4.6
stars
4,173 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

AP

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.

RR

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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376 - 400 of 587 Reviews for Reproducible Research

By Luis M M R

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Mar 4, 2018

very good

By Veronica F V M

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Aug 1, 2017

Muy bueno

By Ahmed M S K

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Jun 20, 2017

Excellent

By 刘博

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Mar 2, 2017

good work

By Carlos R

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Dec 26, 2016

Excelente

By saroj r

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May 14, 2016

i like it

By A E M A

•

Aug 1, 2024

شكرا لك

By 杜冈桃

•

Oct 7, 2017

Perfect

By Sanjay B

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Oct 27, 2020

Great.

By Medha B

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Oct 18, 2020

Great.

By Adán H

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Nov 6, 2017

thanks

By zhao m

•

Nov 1, 2016

good.

By Manoj K

•

Aug 31, 2016

Great

By CHANDAN K S

•

Nov 13, 2020

nice

By �SADHARAN G

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Jul 17, 2020

good

By Rizwan M

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Sep 5, 2019

good

By SriHari a

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Apr 21, 2019

Good

By Amit K R

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Nov 27, 2017

Good

By Jay B

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Aug 24, 2017

Good

By Yi-Yang L

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Apr 10, 2017

Good

By Oleksandr F

•

Nov 24, 2016

Nice

By 朱荣荣

•

Mar 11, 2016

good

By Meidani P

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Dec 3, 2021

-

By Suriya

•

Feb 24, 2018

O

By Marat G

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Mar 22, 2017

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