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Learner Reviews & Feedback for Reproducible Research by Johns Hopkins University

4,163 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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501 - 525 of 586 Reviews for Reproducible Research

By Rohit K S

Sep 21, 2020


By Mehul P

Sep 17, 2017

Nice course.

By Ezzeldin A H

Feb 15, 2022

thanks alot

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

By Shengyu C

Mar 21, 2016

The content is good but the contents have a lot of overlap. The instructors are definitely knowledgeble about the material and clear about the presentation but a lot of the same matierial are repeated throughout the course unintentionally. Think the insturctors just thought it was enough to throw a bunch of things together and called it the day.

By George A

Feb 6, 2016

To be honest, I couldn't realize why this had to be an entire seminar on its own. Apart from that, in some videos the audio quality was rather poor and the instructor seemed to have caught some cold or something. Although the topic of the course is interesting and significant, I think that do far it is the least engaging of the specialization...

By Robert R

Apr 25, 2016

Course is generall very good and lots of fun!

2 things i would change:

... the Assignment 1 is too early in the course or the Lectures are disordered, but I needed the second and third week material to do the first weeks assignment.

... Add Jupyter Notebooks in the Specialization in addition to Knit-R

By Jaromír S

Jul 6, 2017

It helped me to organize my research stuff better. But it's too long course for subject which is not enough for 4 weeks. So, it's relaxing topic. It would be enough to do just 1 peer assignment to learn everything. Most of it I knew, but there is always something new which I have learned.

By Michalis F

May 21, 2016

Too expensive for the material it provides; it is helpful and necessary but this course can be summarised in 1-2 lectures. There is a very good lecture from an external speaker,which was very good and funny (at least i found it funny) and i didn't realise that it was 30 mins long.

By Fabiana G

Jun 23, 2016

Course content is okay - there was some repetition of topics throughout the weeks. As the other first courses in the specialization, students would benefit tremendously if the instructors were a bit more active - the course feels out of date and abandoned.

By Rafael S

Oct 27, 2017

This was, by far, the hardest course in the specialization until now. Not because of its dificulty per se, but because it was too boring, there where very little practical exercises, and I just had to gather all my willpower to get to the end of this one.

By Moshe P

May 21, 2019

The course seems to be based on lectures recorded at different times. Some points discussed are repetitive. the quality of content is good though. I believe the whole material may have to be updated and, potentially, re-recorded.

By Ekta A

Feb 23, 2018

Most of the knowledge one needs can be perceived till week 2 only. Week 3 is a complete repetition of previous 2 weeks. While week 4 offers case studies which I feel are not much important. But overall the experience was good.