Reproducible Research

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.

If you are at university (PhD student, academic, researcher, etc.) then you kind of know most of the "theory". However, practising R was a huge plus (personally, I liked the Week 4 task).

Of course, I liked this course. There was even an extra non-graded assignment. Plus two graded assignments. Quality instruction videos and lots of practice. Everything a learner needs.

should be included much more in course 1. it would have been great to know up front how easy it is to mix text and code, not from a reproducibility standpoint, but just to take notes.

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.

Learning Knitr was cool. However, many of the slides were not directly relevant to the course. I think, more rigor can be added, or this course can be merged with one of the others.

You will learn how to use a very valuable tool in this class; its name is R Markdown. Besides Prof. Peng explains very well the importance of reproducible research. Nice course!

This course is very helpful in terms of not only doing the analysis but also getting to know the finer nuances of making a structured markdown document for future reproducible.

Very helpful and informative information on how to create reproducible research. The project gives you an opportunity to create reproducible research in the format of a report.

While I'm pretty sure this course is VERY important for researchers, it is not very useful for my area (IT) and I would like to know this before taking the course. Thank you.

This is a necessary evil. You can try to do the other classes in the specialization without it, but learning to use R markdown well is hard with out this or a similar class

I personally got a lot out of this, both from a philosophical perspective and a nuts-and-bolts perspective. And I got to practice a lot of stuff learned on earlier courses.

I always knew documentation was important but never liked it. This course showed me just how important it was and showed me just how easy it is to do with tools like knitr.

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

Enjoyed learning about rMarkdown, caching, and RPubs. Was also able to spend time plotting and aggregating data in different ways. Didn't enjoy cleaning data too much :)

it shows how to better communicate one analysis and i have learnt a lot from it. the lectures should be updated as some details and figures were irrelevant a this time

Reproducibility is one of the key elements of modern scientific method. The course was very informative and introduce ideas I did not know before, but are crucial.

First week has an assignment that requires knowledge from the second week. It would be better for the course if both assignments has two weeks for accomplishment.

This course helped me realize why reproducible research is absolutely necessary, and gave me the tools to implement reproducibility in my work. Project was great.

Good info on RStudio & RR.\n\nI can easily figure out who has attended this course by their methodical nature and work when I see Kaggle competitions. Great job!