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Reproducible Research

Data Science,

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.

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Rating 4.3 based on 519 ratings
Length 5 weeks
Effort 4-9 hours/week
Starts Mar 8 (6 weeks ago)
Cost $50
From Johns Hopkins University via Coursera
Instructors Roger D. Peng, PhD, Jeff Leek, PhD, Brian Caffo, PhD
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Data Analysis Probability And Statistics

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What people are saying

final project

One of the best learning contents, great cour Final project was very intersting.

The final project was very interesting and taught me a lot about how I approach analysis projects, and how to improve this going forward.

The final project is challenging in terms of proper data cleaning, and it may take much more than 2 hours to complete it adequately.

The final project lacks clear directions.

My only qualm is that the final project was more difficult than I expected it to be given the content.

decent course, it is as long as you make it but start the final project early This course was very helpful for my career Good intro to the topic 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 struggled in getting the final project right but it helped me understand the course better.

The second and final project is very time consuming.

Interesting final project!

The final project is a lot of work (mostly data cleaning) but very fun and informative.

Good, but the final project involved too much programming and the size of the data file was unmanageable on my three year old laptop.

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data science specialization

Reproducible Research is the fifth course in the Data Science specialization, and the last course in what could reasonably be considered the basic R introduction portion of the series.

A few of the lectures were a bit repetitive if you are taking the full data science specialization.

Nice course Good course as part of the data science specialization.

A must for every analyst for its simple tips on reproducibility, which can go a very very long way at work or school In my opinion, this is one of the most valuable courses in the Data Science Specialization.

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.

By far the most time consuming, yet rewarding course in the data science specialization thus far.

I only took this course because it is part of data science specialization.

This is unfortunately the last module with Roger Peng in Coursera's Data Science Specialization track.

Course Project 2 was fun and learned a lot course material and projects help a lot in learning and tips on how to better document research and projects Very useful in bringing together skills learned in the earlier courses of the Data Science specialization: R programming, R Markdown, knit, RPubs.

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importance of reproducible research

I guess from the case studies and research on the web whats I learned from this course is the importance of reproducible research is.

The importance of reproducible research is stressed clear and concisely, Roger D. Peng does a great job of explaining the material.

Very Good Content Thank you instructors, for making me realize the importance of reproducible research.

Course nicely highlighted the importance of reproducible research and the use of markdown and knitr packages.

Without taking this course wouldn't have fully understood the importance of reproducible research in data science.

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too much

A bit too much focused on academic research, I find.

Didn't enjoy cleaning data too much :) Reproducibility is one of the key elements of modern scientific method.

Important and interesting stuff - but lots of it is repeated too much, which make it seem like 4 weeks is too much for the material.

nice Awesome course Interesting course, but course assginments lack guidance, have too much complexity and require a time spent too long compared to the benefits Nice course.

ok, but the focus is too much on knitr, My experience of taking this course was really challenging and great, today I got to know how it is critical and crucial to Reproduce exact result as per author original research.

Nice course to explore Too much repetition; one video has been stretched into 10.

However, I think the last assignment is simply too much.

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found this course

Good I found this course very informative and helpful.

I found this course is really important and good.

I have found this course very useful in order to learn the keys of reproducible research.

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.

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highly recommend

I found the lessons and the final project valuable in breaking down my own weaknesses in documenting and discovering new aspects of R. I highly recommend this class.

Great knowledge, highly recommended.

I highly recommend this course.

I highly recommend taking this.

Highly recommended for beginners to learn the basics of Data Science, Re-producibility and how to write a good report around the analysis done by you as a data analyst.

Highly recommend!!

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previous courses

Very interesting course, I was able to apply what I learned in the previous courses of the specialization, and that was a good exercise.

Not much to take in this course comparing to the previous courses.

Interesting material, but wasn't necessarily of the same depth of knowledge like previous courses in the series Best course, I have come cross.

Slightly less information than the previous courses in DS spec but important for someone who has not done scientific research in the past.

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.

And I'm giving it 1 star so you can imagine how the previous courses are.

I like the complexity of this one and how it builds upon previous courses.

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case studies

Only introduced and taught one main topic: knitr package in R. Much of course spent repetitively advocating for reproducible research with case studies and peer reviewed assignments.

While week 4 offers case studies which I feel are not much important.

Great course with good case studies and clearcut goals - learn knitr, markdown, and a little history of literate programming languages.

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.

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.

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it provides

It provides learner with the knowledge and skills needed to be a modern scientist/analyst, focusing on making analyses reproducible from the beginning to the end.

Too expensive for the material it provides; it is helpful and necessary but this course can be summarised in 1-2 lectures.

best practices

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.

This is an excellent course which teaches you fundamental best practices in research.

It provides tools that help a scientist to thoroughly document and publish their research in a fully reproducible way and it shows the current best practices for reproducibility.

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.

A pretty good coverage on the need for reproducibility and the best practices to make it happen.

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Rating 4.3 based on 519 ratings
Length 5 weeks
Effort 4-9 hours/week
Starts Mar 8 (6 weeks ago)
Cost $50
From Johns Hopkins University via Coursera
Instructors Roger D. Peng, PhD, Jeff Leek, PhD, Brian Caffo, PhD
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Mathematics
Tags Data Science Data Analysis Probability And Statistics

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