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Learner Reviews & Feedback for Reproducible Research by Johns Hopkins University

4.6
stars
4,045 ratings
577 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

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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526 - 550 of 559 Reviews for Reproducible Research

By Raul M

Jan 16, 2019

Many times the course goes over the same topic over and over.

By Manuel E

Apr 29, 2019

Good - Makes you assimilate the concept and work on it

By Francesca A

Oct 23, 2016

That's the best course of the entire specialisation.

By Steph L

Jan 26, 2021

The guidelines for project 2 need to be improved.

By Marcela Q

Nov 26, 2019

A little repetitive and basic but useful!!!!

By Lakshay S

Jul 13, 2019

Pathetic It was . Not at all Interesting !!

By Raviprakash R S

Feb 13, 2017

Important topic to cover....Nice course

By Shawn L

Apr 4, 2018

Thought the final project was fun.

By Paul R

Mar 13, 2019

Along with the principles of "reproducible research", the primary tool introduced in this course is knitr to produce reproducible research papers and Rpubs for publishing papers. I think this specialization covers RMarkdown 3 different times. Assignments were good, at this stage you start to produce proper papers on an analysis topic which is very much needed before hitting the statistics/regression lectures; however this material can be compressed and needs to be combined with the 9th course which covers Rmarkdown/RStudio again.

By Jeffrey R

Jul 11, 2018

While I have enjoyed and learned a lot from other courses in the specialization I found this course to contain a lot a repeated information and was overly theoretical. I would have been much more helpful to dig deeper into the capabilities that R markdown offers (i.e building templates, syncing with reference managers, etc.) A a scientist I really appreciate your dedication to getting the word out about reproducibility but at the same time I would have liked to have gained more "how-to" knowledge in this course.

By Ricardo M

Dec 13, 2017

I believe that this module could be split up to other modules on the Data Science Specialization, instead of having a complete module only for this topic.

Additionally, the process of having the classmates review to get an assignment complete should be changed. In some cases, the assignment is submitted before the deadline, but the lack of reviews by others leads to the assignment extend the deadline. The same happens when we finish an assignment and there are no submissions to be reviewed.

By Jordan B

Mar 5, 2018

Same information repeated almost all the time ... it looks like the video were made independantly of the course and simply uploaded into Coursera as is .. It is ok in general but in this case, it was really painful to watch. Like video 1 (5min) then video 2 (6 min with 3 as a reminder of the video 1). Reminder are fine across courses or even in different weeks of the same course but not in 2 videos in a row. Otherwise content interesting but could have been explained in way less time.

By Anton K

Nov 27, 2019

The material is shallow. Projects are way too time demanding. Everybody knows that data cleaning is a routine and long process. That is precisely why nobody likes it. And if there's only one way to clean the data - by hand and only after reading a lot of related database documentation - this kills all the fun of studying and makes the overall picture of consepts relations unclear.

By Jaymes P

Oct 27, 2020

This course was not well organized. It seemed like lessons were just thrown together and covered much the same content. Some were recorded lectures, others filmed in an office, saying virtually the same things with nearly the same slides but on different weeks. Same old story with the instructor--impossible to listen to because of all the so, um, uhhh, so, kinda, so ummm, etc.

By Gonzalo D

Mar 5, 2016

The course is poorly organised: There is a project on week one that requires knowledges of week two. Some concepts are dictated more than once because it uses videos made for this course + other recorded from a class room.

I think this course should be a 3 weeks project and the price should be the half of it cost.

Though I enjoyed the second project.

By Gianluca M

Oct 18, 2016

It is not a bad course, but it is very little informative. There is some nice general discussions about data science by the teacher, there is the explanation of the package knitr, and little else.

As part of the data science specialization it is nice. As a stand-alone course, I would definitely not recommend it.

By Julien N

Jul 23, 2018

Very disappointed by this course (was used to better by JHU)!

Nothing more than a R Markedown tutorial

Not up-to-date (a full video about an deprecated R package).

A section about evidence based analysis that is hard to understand (and of questionnable interest if not to "fill" this rather empty course)

By Roberto M

Nov 19, 2016

This course seems 'light' in content - too much time is spent reviewing case studies instead of discussing different ways to create documents that enable reproducible research. Perhaps this should be a topic/chapter in another course, and not a standalone course.

By Marvin T O

Mar 29, 2017

Reproducible research with doubt is important but videos and what it is discuss are not appealing and beyond that, what are worthen are the projects. I did not learn so much from the videos but by myself. Though, the forum is very useful.

By Matt E

May 1, 2018

This section could have been completed in a two week schedule instead of four. It is not a terribly complex subject. Statistical inference, however, is. It has a lot of content and could easily go for 5 or 6.

By Jackson L

Nov 8, 2017

This leaves a lot to be desired. I felt the lectures were fragmentary at best and really lacked in depth analysis. A lot of time was spent on the philosophy of analysis rather than practical tools in R.

By Willie C

Feb 2, 2020

Lecture videos were very repetitive. Course projects were repetitive, too. Important information, but didn't need to be stretched out over a full "four-week" course.

By Abhimanyu B

Jan 17, 2017

Provides a very summary overview of a very important aspect of data analysis. Expected more!

By Johnny C

Apr 3, 2018

The course was interesting, but it is bad many of the videos are recorded lectures.

By Pratik P

Feb 2, 2017

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