Reproducible Research

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.

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.

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.

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.

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.

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!!

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.

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.

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.