Reproducible Research

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6/10 stars
based on  6 reviews
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Cost FREE
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Course Details

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FREE

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Coursera online courses
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with yo...
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with your coursework. Coursera also partners with the US State Department to create “learning hubs” around the world. Students can get internet access, take courses, and participate in weekly in-person study groups to make learning even more collaborative. Begin your journey into the mysteries of the human brain by taking courses in neuroscience. Learn how to navigate the data infrastructures that multinational corporations use when you discover the world of data analysis. Follow one of Coursera’s “Skill Tracks”. Or try any one of its more than 560 available courses to help you achieve your academic and professional goals.

Provider Subject Specialization
Humanities
Sciences & Technology
4733 reviews

Course Description

Learn the concepts and tools behind reporting modern data analyses in a reproducible manner. This is the fifth course in the Johns Hopkins Data Science Specialization.
Reviews 6/10 stars
6 Reviews for Reproducible Research

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Rankings are based on a provider's overall CourseTalk score, which takes into account both average rating and number of ratings. Stars round to the nearest half.

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Jeff Winchell profile image
Jeff Winchell profile image
5/10 starsCompleted
  • 91 reviews
  • 66 completed
4 years, 10 months ago
This course has an odd assessment strategy. The second half of the course material is not tested, and the second project seemed rather simplistic. Still, I highly applaud the goal of literate programming and reproducible research, so I am happy I was introduced to this class. At first I had skipped it to take more advanced classes in the data scientist MOOC series, but then realized it could be useful to take. I'm glad I took the course.
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Marcelo Soares profile image
Marcelo Soares profile image
8/10 starsCompleted
  • 16 reviews
  • 13 completed
5 years, 2 months ago
This is unfortunately the last module with Roger Peng in Coursera's Data Science Specialization track. I like the complexity of this one and how it builds upon previous courses. I also like the fact that the course has two practical assignments, even if they are in subsequent weeks and time has been short. But good course to get the gist on how to publish live data.
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Greg Hamel profile image
Greg Hamel profile image
8/10 starsCompleted
  • 116 reviews
  • 107 completed
5 years, 7 months ago
Reproducible research is the 5th course in the John Hopkins data science track on Coursera. As the title states, this course is all about making research and data analysis reproducible using the R programming language. The first 2.5 weeks of lecture material in this course is great. It provides a well- organized overview of how to create reproducible research in R using R markdown and the knitr package, taking plenty of time to talk about best practices. Thankfully, Roger Peng has added in a little box with his face in at as he talks over his slides for many of his videos, which makes the content a lot more engaging than it is in some of the other John Hopkins courses that only have voiceovers. The final 1.5 weeks of lecture video material is not as useful or engaging and seems a bit lazy in that week 4 takes the form of recordings of lectures given sometime in the past. The videos in second half of week 3 only have voiceovers and th... Reproducible research is the 5th course in the John Hopkins data science track on Coursera. As the title states, this course is all about making research and data analysis reproducible using the R programming language. The first 2.5 weeks of lecture material in this course is great. It provides a well- organized overview of how to create reproducible research in R using R markdown and the knitr package, taking plenty of time to talk about best practices. Thankfully, Roger Peng has added in a little box with his face in at as he talks over his slides for many of his videos, which makes the content a lot more engaging than it is in some of the other John Hopkins courses that only have voiceovers. The final 1.5 weeks of lecture video material is not as useful or engaging and seems a bit lazy in that week 4 takes the form of recordings of lectures given sometime in the past. The videos in second half of week 3 only have voiceovers and they have an echo to them that makes them hard to listen to. All in all, the first 2.5 weeks of this course are definitely worth checking out if you have any interest in learning about reproducible research but you might want to skip through some of the content at the end of the course.
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Tamás Nagy profile image
Tamás Nagy profile image
9/10 starsCompleted
  • 7 reviews
  • 7 completed
5 years, 5 months ago
I only took this course because it is part of data science specialization. But the topic proved to be really interesting, and I got hooked quite fast. I liked the assignments too. The only small annoyance was the recorded classroom lectures, which were sometimes slightly redundant and not of good (sound) quality. It is a relatively easy course.
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Hamideh Iraj profile image
Hamideh Iraj profile image
2/10 starsCompleted
  • 70 reviews
  • 60 completed
5 years, 6 months ago
This course had nothing new to me. I did not watch any video and just did the quizzes and assignments. I passed data analysis from John Hopkins University before and I knew almost all the course material. I love coursera courses mainly because of challenging assignments and learning through that . but this course had not this characteristic.
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Richard Taylor profile image
Richard Taylor profile image
2/10 starsCompleted
  • 29 reviews
  • 28 completed
5 years, 6 months ago
This is so far the best course from the specialization. And I'm giving it 1 star so you can imagine how the previous courses are. This is about writing reports in RStudio using Markdown and RCode. You just need to learn basic Markdown and how to insert code chunks to create your report. That takes about 26 seconds. Once you do that you can answer the quizzes and do the assignments. Both peer assignments are just about writing a report doing some basic stuff on a given dataset. The topics are incredibly boring, a hallmark of the specialization. The quizzes are trivial, the assigments can take some time processing the data, creating the plots you are asked to display etc. I only wish the topics could be at least interesting or entertaining but no. The videos might be skipped, there's a lot of material just to fill 4 weeks and justify yet another course.
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