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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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576 - 587 of 587 Reviews for Reproducible Research

By Pratik P

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Feb 2, 2017

Sholdnt be a different course. It shold be very very concise. Not this long.

By Victor M

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Dec 8, 2017

Last two weeks do not teach anything new

By Cyriana R

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Jul 1, 2017

ok, but the focus is too much on knitr,

By Sindre F

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Aug 1, 2016

Useful for academics.

By Avolyn F

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Jun 19, 2019

I was really passionate about the subject matter, but, although I have experience in R, apparently not enough to complete the assignment. Would have liked a little more warning that this would be needed, I was more interested in the topic of Reproducible Research, which while I agree is easier done via code of some kind, shouldn't be a topic specific to R, should be applicable to Python, SQL, whatever.

Might have time to revisit this, but will probably need to take a few more R classes to even be able to complete, likely won't get around to it, but the first 2 weeks were worth the cost of paying for a certificate, I guess.

By Owen D

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Sep 8, 2021

This course could've been condensed into one video and incorporated somewhere else in the specialization. Doesn't seem like the instructor even took the course that seriously despite emphasizing its importance in the first lecture. Some of the lectures sound like they were recorded while the instructor was drinking with his colleagues at a dinner party.

By Joel K

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Feb 1, 2016

The other modules that I have done in this specialisation have been great. The lecturers are insightful and the courses have been at the right pace. This particular module was flat, to say the least. I paid €43 to learn a small amount of markdown syntax, and the quizzes and the weeks didn't even match up!

By matthieu c

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Jun 10, 2017

The course presented an important topic, but it was not new to me. Moreover I believe that the quality of some audio track is not good enough to understand everything the lecturer is explaining. I'm referring to Roger Peng lecture with the students.

By Stefan H

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Jul 1, 2019

Very repetitive in context of earlier introduction to the topic and also throughout the weeks. Generally it doesn't feel there is much of a take-away and not sure it deserves its own course.

By YAN N W T

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Oct 11, 2017

Not much to take in this course comparing to the previous courses. Worst of all video lectures are not well organised.

By Anand M

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May 5, 2017

Too much repetition; one video has been stretched into 10.