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Learner Reviews & Feedback for Reproducible Research by Johns Hopkins University

4.6
stars
4,113 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

AA

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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401 - 425 of 580 Reviews for Reproducible Research

By Erik A

Jan 17, 2017

The concept is very important, so it's good that this course is available. The video's are sometimes not that great of quality. It's okay to show recordings of lectures, but the sound is of less quality.

Another thing is that the teacher says a lot of "euhm". I know he cannot help that, but once you notice this it becomes a bit annoying.

By Charles K

Jul 31, 2016

Pretty good course that introduces a lot of useful tools and the concept of reproduciblity. However, it is not quite as applicable as the previous courses for those who are individual contributors in the private sector and rarely have others double check their analyses or need to publish anything.

By Felipe P

Feb 29, 2016

In this course, there was a slide presentation with audio recorded in a classroom. This part of the course should be replaced as soon as possible to offer better experience. As it is presented right now, with a loudy environment, it really doesn't match to the quality of the other courses.

By Werner S

Nov 28, 2016

Besides the "real" scientists like medicine or bioscience, I think the whole community would be better off it everybody would follow the principles laid out here: Do your analysis but make sure that others can follow the rationale and that your steps are documented and thus reproducible.

By Jose P M L

Oct 23, 2020

This is a very useful course, it shows how to search your research in a complete manner. This is very important and even though its an easier course, the idea is important. The audio in the lessons that are taken from professor Peng real classrom is deficient. Its hard to keep track

By Bernie P

Jun 19, 2018

Good theory course. As someone who holds a masters and worked through half of a PhD it wasn't super useful for me personally since I was aware of the power and need for reproducibility. It's not worth while but having more of a business use case for the need might be helpful.

By Gabriel O M

Jun 21, 2021

very nice, I learned new stuff that I didn't know. Very easy to follow and to understand as well. The exercises and projects are really good to practice previous knowledge acquired. Also I'm pretty sure that this course will help me out in my tasks at my current work.

By Camilla H

Sep 27, 2017

The course finally got me to use markdown files which I had dabbled with before. It was nice to cement some knowledge. What I didn't appreciate was the largely redundant video lectures. Some were what seemed to be the same lecture given a year or two apart.

By Kyle H

Feb 5, 2018

A few of the lectures were a bit repetitive if you are taking the full data science specialization. Overall there are some valuable skills and thought patterns that will prove useful if interested in reproducibility and clarity of analysis.

By Mengyin B

Oct 6, 2016

It is about how to make your work available for others and yourself in the future. It is quite refreshing because I have never heard about anything in this course from anywhere else. It is useful for me and hope it will be useful to you.

By Yudhanjaya W

Sep 1, 2017

The lessons on Knitr, Markdown and the case studies dissecting research were useful, but I felt far too little time was spent on examples of implementing reproducible research, and too much time spent talking about its benefits.

By Don M

Dec 10, 2018

Good, but the final project involved too much programming and the size of the data file was unmanageable on my three year old laptop. Could the objectives be met with a smaller data file and less programming?

By Yevgen M

Apr 6, 2017

If you are at university (PhD student, academic, researcher, etc.) then you kind of know most of the "theory". However, practising R was a huge plus (personally, I liked the Week 4 task).

By Yatin M

Jul 22, 2017

Learning Knitr was cool. However, many of the slides were not directly relevant to the course. I think, more rigor can be added, or this course can be merged with one of the others.

By Giovanna A G

Dec 16, 2016

You will learn how to use a very valuable tool in this class; its name is R Markdown. Besides Prof. Peng explains very well the importance of reproducible research. Nice course!

By Kim K

Aug 8, 2018

Very helpful and informative information on how to create reproducible research. The project gives you an opportunity to create reproducible research in the format of a report.

By Antonio C d S P

Feb 3, 2017

While I'm pretty sure this course is VERY important for researchers, it is not very useful for my area (IT) and I would like to know this before taking the course. Thank you.

By Greg A

Feb 22, 2018

This is a necessary evil. You can try to do the other classes in the specialization without it, but learning to use R markdown well is hard with out this or a similar class

By Manny R

Nov 13, 2017

Enjoyed learning about rMarkdown, caching, and RPubs. Was also able to spend time plotting and aggregating data in different ways. Didn't enjoy cleaning data too much :)

By demehin I

May 23, 2016

it shows how to better communicate one analysis and i have learnt a lot from it. the lectures should be updated as some details and figures were irrelevant a this time

By Mikhail S

Feb 6, 2016

First week has an assignment that requires knowledge from the second week. It would be better for the course if both assignments has two weeks for accomplishment.

By Jorge E M O

Jul 21, 2016

The course already needs and actualization, plus they must fix the order of the first assignment. Besides that, this is a really useful and fulfilling course.

By Jo S

Jan 27, 2016

Covers some important and interesting areas and is generally well taught (although the recording quality on the videos varies). Interesting final project!

By Rouholamin R

May 12, 2019

lectures are a little bit theoretical and at some point maybe boring but projects will give you a real experience with data and research reproducibility.

By Kaplanis A

Dec 26, 2016

All in all a great course with very valuable information to make a data scientist better at his job. However it could have been covered in 2 weeks time