AP
Feb 12, 2016
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.
RR
Aug 19, 2020
A very important course that greatly improved my ability to communicate the findings of any sort of data analysis that I perform. This is a critical skill to acquire to "deliver the means."
By Nino P
•May 24, 2019
To be a data scientist you must use RMarkDown. Here you learn how to use it. A must do course for data scientists and highly valuable.
By Keidzh S
•Apr 24, 2018
Thank you so much. Representatives lessons in my opinion very effective. I learn so much about html and markdown files in this course.
By Leo F
•Feb 28, 2017
One of my favourites. The course is easy to follow and the idea of having a self-contained and reproducible document is very powerful.
By Luz M S G
•Oct 6, 2020
It was a good experience. The final project has been the most challenging that I have had in the specialization, but I learned a lot.
By Arjun S
•Aug 27, 2017
Great stuff. Glad to have the course make us create an Rpubs profile and publish research. Recommended strongly for data scientists
By Daniel C J
•Nov 14, 2016
Great course. A must for every analyst for its simple tips on reproducibility, which can go a very very long way at work or school
By Omar N
•Nov 8, 2018
Really good module/course, gives you a glimpse into real world implementation of data science and the challenges involved with it.
By ONG P S
•Jan 19, 2020
Very practical and knowledge learned can be applied into my works as auditors. This can benefit any fields involving using data.
By Don J
•Jan 22, 2018
These are important skills for a data scientist and I'm glad there is a full 4-week course dedicated to reproducible research.
By Richmond S
•Sep 29, 2016
I struggled in getting the final project right but it helped me understand the course better. Thumbs up reproducible research
By PRAKASH K
•Jul 13, 2020
I strongly recommend this course ,it focuses on reproducible research which is equally an important aspect of data analysis.
By Glenn W
•Mar 4, 2019
Favorite course so far. Really enjoyed working on the projects. They were very helpful in helping to reinforce the material.
By Mathew E
•Mar 30, 2021
This course has been an eye-opener for me and going forward, it would be an indispensable tool in my research activities.
By Amanyiraho R
•Jan 13, 2020
Very interesting and tackles a very important issue that Data scientists always miss-out, reproducibility of your project
By Azat G
•Jan 24, 2019
Amazing course, it introduced the concepts of reproducibility which is used to provide scientific fairness, transparency.
By Anusha V
•Jan 3, 2019
Excellent Course - particular useful for anyone doing research and performing any kind of analysis on the observed data.
By Adrielle d C S
•Apr 3, 2016
Muito completo. Inglês claro. Muitos exemplos. Chega a ser repetitivo em algumas aulas mas, antes sobrar do que faltar!
By Krishna B
•May 30, 2017
towards the end of week 1 lectures we can see all the parts of this specialization coming together in a very nice way!
By Monica Z
•Dec 11, 2020
Very challenging. However, every step in this specialization improves my knowledge and the way of solving problems.
By Prem S
•Aug 2, 2017
Nice course,especially it gives you a general idea and foundation on r markdown files if you already know R studio.
By Federico A V R
•Jul 27, 2017
This topic is relevant to the field, yet few institutions offer courses on it. Great knowledge, highly recommended.
By Lee Y L R
•Feb 1, 2018
Clear sharing of the importance of having proper documentation of data analysis process to enable reproducibility.
By Ann B
•Mar 14, 2017
I think this topic is sometimes overlooked, but very necessary. This course did a good job of covering the topic.
By Emily S
•May 17, 2016
I think this is an essential course that more people should take. Reproducibility is a huge issue in many fields.
By Deleted A
•Oct 7, 2019
I really appreciated the topics covered in this course. Is a wonderful follow-up to the Exploratory Data course.