By far the most time consuming, yet rewarding course in the data science specialization thus far. Literate Programing in general and R Markdown in particular are simple enough as concepts, but do take some time to grow accustomed to. However, I found the course to be a compelling argument for reproducibility that has application beyond just Data Science proper.

Although the technology is completely different, the concepts behind reproducibility really resonated with me and the work I do managing a division in Application Development. I'm constantly having to balance seemingly limitless demands, limited resources, and the difficulty of retaining staff in highly-competitive industry. Reproducibility becomes not just the basis for cross-training, product stabilization, and growth, but is a necessary ingredient of a team's survival.

This course not only cemented my own thoughts on the topic, but gave me some new ideas and tools for process improvement on the job.

El proyecto final del curso tiene poco que ver con lo enseñado a lo largo de éste, era muy necesario haber tomado los cursos anteriores (en especial R programming y Exploración de Datos). Además, el proyecto debería estar mejor planificado, se buscaba que la mayor parte del tiempo estuviera en limpiar la data? O un objetivo más fuerte era el uso de gráficos más elaborados u otro al interior de RMarkdown? O un análisis un poco más elaborado que sólo sumar?

This course teaches how to present a R code analysis that others can run the code to reproduce the same result. The length of the lecture is minimum and the project helps me to make the reproducible analysis on my own. One thing I would like to see improvement is that the instructor's speech. I hope that he can speak more smoothly without stopping to repeat words. It was quite a struggle to listen to his talking. Thank you.

The first assignment should occur during the second week instead of the first given how the material is presented. The second and final project is very time consuming. Ideally this course should run for 6 weeks instead of 4 because of this. The second project is challenging and it definitely drives home the point about reproducible result given the state of the raw data.

I don't think the content of this course was as polished as the others in the specialisation, the lectures seemed to be a mixture of repeats and videoed lecture room talks, based on content I'd probably have given 3 stars, however the 2 course projects, which were quite challenging given lecture content pulled it up, I found these very worthwhile.

The concept is very important, so it's good that this course is available. The video's are sometimes not that great of quality. It's okay to show recordings of lectures, but the sound is of less quality.

Another thing is that the teacher says a lot of "euhm". I know he cannot help that, but once you notice this it becomes a bit annoying.

Pretty good course that introduces a lot of useful tools and the concept of reproduciblity. 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.

In this course, there was a slide presentation with audio recorded in a classroom. This part of the course should be replaced as soon as possible to offer better experience. As it is presented right now, with a loudy environment, it really doesn't match to the quality of the other courses.

Besides the "real" scientists like medicine or bioscience, I think the whole community would be better off it everybody would follow the principles laid out here: Do your analysis but make sure that others can follow the rationale and that your steps are documented and thus reproducible.

This is a very useful course, it shows how to search your research in a complete manner. This is very important and even though its an easier course, the idea is important. The audio in the lessons that are taken from professor Peng real classrom is deficient. Its hard to keep track

Good theory course. As someone who holds a masters and worked through half of a PhD it wasn't super useful for me personally since I was aware of the power and need for reproducibility. It's not worth while but having more of a business use case for the need might be helpful.

very nice, I learned new stuff that I didn't know. Very easy to follow and to understand as well. The exercises and projects are really good to practice previous knowledge acquired. Also I'm pretty sure that this course will help me out in my tasks at my current work.

The course finally got me to use markdown files which I had dabbled with before. It was nice to cement some knowledge. What I didn't appreciate was the largely redundant video lectures. Some were what seemed to be the same lecture given a year or two apart.

A few of the lectures were a bit repetitive if you are taking the full data science specialization. Overall there are some valuable skills and thought patterns that will prove useful if interested in reproducibility and clarity of analysis.

It is about how to make your work available for others and yourself in the future. It is quite refreshing because I have never heard about anything in this course from anywhere else. It is useful for me and hope it will be useful to you.

The lessons on Knitr, Markdown and the case studies dissecting research were useful, but I felt far too little time was spent on examples of implementing reproducible research, and too much time spent talking about its benefits.

Good, but the final project involved too much programming and the size of the data file was unmanageable on my three year old laptop. Could the objectives be met with a smaller data file and less programming?

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).

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!

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

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