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Learner Reviews & Feedback for Reproducible Research by Johns Hopkins University

4,070 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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476 - 500 of 568 Reviews for Reproducible Research


Jul 8, 2020

great learning experience!!

By Sathiaseelan P

Jun 7, 2018

This Course was really fun.

By Mark S

Nov 9, 2017

Import information to know.

By Mohamad R B R

Feb 18, 2016

Still i​ can used easily.

By Timothy V B

May 19, 2017

Good intro to concepts

By Vadim K

May 26, 2016

Rather general topic

By Sourav B

Jul 12, 2017

informative course

By Jason W

Jan 3, 2017

latex is better :O

By Amit S

Nov 26, 2019

Very Good Content

By Thiago Y

Jun 27, 2021

Very cool course

By Rohit K S

Sep 21, 2020


By Mehul P

Sep 17, 2017

Nice course.

By Abhishek S

Jun 6, 2017

Good course

By Tushar K

Feb 10, 2017

Nice course

By Johnnery A

Nov 22, 2019


By Khobindra N C

May 18, 2016


By Anup K M

Oct 2, 2018


By Greg B G

Sep 21, 2017


By Rajib K

Mar 28, 2017


By Miguel C

Apr 6, 2020

I enjoyed this course, especially the tips we got on how to make our analysis more reproducible and the practice with RMarkdown and RPubs. The lecturer was really knowledgeable and engaging, making it easier to follow the course. The assignments were challenging and allowed me to build on things I had learned in previous courses, especially R skills.

My biggest problem with this course was its repetitiveness. Its content repeats some of what was explained in Data Scientist's Toolbox course, and sometimes it repeated things from previous week of this same course. I think the material can be summarized into just 2 or 3 weeks. I also found the second course project quite exhaustive; it wasn't particularly hard but the data was quite messy so it took a long time to clean, which was boring and tiring but I guess that's part of the data scientist's job.

Overall, I still enjoyed the course and I would recommend it to other people interested in becoming data scientists.

By Christiane H

Dec 8, 2015

Overall a good course for self-study. The assignments in particular are excellent for data cleaning, analysis and interpretation. The quizzes are very basic though and appear to be there only to check if the student has gone through the lectures. The knowledge needed to answer the quizzes and achieve the desired results in the assignments are vastly different and should be addressed.

The case studies at the end are insightful and more use could be made of them in a more advanced course. There is a lot of repetition of concepts throughout the course and this can become distracting. THe format for the lecture videos varies throughout and this inconsistency (along with extreme audio volume changes) also becomes distracting.

Other than that, excellent for driving the need for reproducible research (RR) home, presenting and explaining some tools available to achieve RR and ways of publishing results/reports from these studies.

By James T

Apr 26, 2016

The course was good. I enjoyed it. The biggest problem was the un-moderated participation of at least one other student. This particular student drove the discussion of assignments, leaving little room for others to explore, ask, and answer questions. As far as I know the student was not a mentor/TA, but It would have been most helpful for staff to weigh in on some of the student's post. I really believe the student was feeding his/her ego.

By Olivia U

May 21, 2020

This course comes as the fifth one in the series, but the stuff in there is pretty basic, and we've worked before with R markdown in the series. It's important stuff, but I would put it earlier. The content focuses a bit too much on publishing research, which is surely important in academics, but not so much for people like me who work in regular companies. That's not my favorite course so far.

By Blazej M

Dec 3, 2017

Course assignments are great and one can learn a lot by doing them. One warning: there is no way to finish second assignment in 2 hours as it is specified in the course. It took me almost a week of digging and clearing data! But I enjoyed it.

Video materials are mediocre at best. With the exception of last video on genes sequencing . That one was entertaining.

By Rok B

Jun 17, 2019

Not the most important course in the series, but I give it 3 stars.

Positives, I'm impressed with RMarkdown. It is a handy tool to make reproducible research. I also think the final assignment was very interesting. You can train cleaning data.

Negatives, lectures from weeks 3 and 4. They are poorly recorded and have little to no value for the course