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Learner Reviews & Feedback for Reproducible Research by Johns Hopkins University

4.6
stars
4,168 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

IM

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Without taking this course wouldn't have fully understood the importance of reproducible research in data science. Thank you so much. I recommend this course for all data scientists.

MF

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I took this course as part of the Data Science specialization without any real expectation and realized that this subject is probably one of the most important in data analysis.

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101 - 125 of 586 Reviews for Reproducible Research

By Nino P

May 24, 2019

To be a data scientist you must use RMarkDown. Here you learn how to use it. A must do course for data scientists and highly valuable.

By Keidzh S

Apr 24, 2018

Thank you so much. Representatives lessons in my opinion very effective. I learn so much about html and markdown files in this course.

By Leo F

Feb 28, 2017

One of my favourites. The course is easy to follow and the idea of having a self-contained and reproducible document is very powerful.

By Luz M S G

Oct 6, 2020

It was a good experience. The final project has been the most challenging that I have had in the specialization, but I learned a lot.

By Arjun S

Aug 27, 2017

Great stuff. Glad to have the course make us create an Rpubs profile and publish research. Recommended strongly for data scientists

By Daniel C J

Nov 14, 2016

Great course. A must for every analyst for its simple tips on reproducibility, which can go a very very long way at work or school

By Omar N

Nov 8, 2018

Really good module/course, gives you a glimpse into real world implementation of data science and the challenges involved with it.

By Thomas

Jan 19, 2020

Very practical and knowledge learned can be applied into my works as auditors. This can benefit any fields involving using data.

By Don J

Jan 22, 2018

These are important skills for a data scientist and I'm glad there is a full 4-week course dedicated to reproducible research.

By Richmond S

Sep 29, 2016

I struggled in getting the final project right but it helped me understand the course better. Thumbs up reproducible research

By PRAKASH K

Jul 13, 2020

I strongly recommend this course ,it focuses on reproducible research which is equally an important aspect of data analysis.

By Glenn W

Mar 4, 2019

Favorite course so far. Really enjoyed working on the projects. They were very helpful in helping to reinforce the material.

By Mathew E

Mar 30, 2021

This course has been an eye-opener for me and going forward, it would be an indispensable tool in my research activities.

By Amanyiraho R

Jan 13, 2020

Very interesting and tackles a very important issue that Data scientists always miss-out, reproducibility of your project

By Azat G

Jan 24, 2019

Amazing course, it introduced the concepts of reproducibility which is used to provide scientific fairness, transparency.

By Anusha V

Jan 3, 2019

Excellent Course - particular useful for anyone doing research and performing any kind of analysis on the observed data.

By Adrielle d C S

Apr 3, 2016

Muito completo. Inglês claro. Muitos exemplos. Chega a ser repetitivo em algumas aulas mas, antes sobrar do que faltar!

By Krishna B

May 30, 2017

towards the end of week 1 lectures we can see all the parts of this specialization coming together in a very nice way!

By Monica Z

Dec 11, 2020

Very challenging. However, every step in this specialization improves my knowledge and the way of solving problems.

By Prem S

Aug 2, 2017

Nice course,especially it gives you a general idea and foundation on r markdown files if you already know R studio.

By Federico A V R

Jul 27, 2017

This topic is relevant to the field, yet few institutions offer courses on it. Great knowledge, highly recommended.

By Lee Y L R

Feb 1, 2018

Clear sharing of the importance of having proper documentation of data analysis process to enable reproducibility.

By Ann B

Mar 14, 2017

I think this topic is sometimes overlooked, but very necessary. This course did a good job of covering the topic.

By Emily S

May 17, 2016

I think this is an essential course that more people should take. Reproducibility is a huge issue in many fields.

By Deleted A

Oct 7, 2019

I really appreciated the topics covered in this course. Is a wonderful follow-up to the Exploratory Data course.