HS
2. Mai 2020
This course provides an introduction of some important concepts and tools on a very important aspect of data science: cleaning and organizing data before any analysis. A must for any data scientist.
BE
25. Okt. 2016
This course is really a challenging and compulsory for any one who wants to be a data scientist or working in any sort of data. It teaches you how to make very palatable data-set fro ma messy data.
von Karolina W
•12. Juni 2016
Good course, learned a lot especially through the quizzes and the course project, but slides/presentations could be more engaging.
von Hernan S
•15. Apr. 2016
Fun and interesting. Won't remember all the ways to get and clean data, but it's good to review them so I know what's possible.
von C. T M
•27. Jan. 2016
it's a little rapid fire, but the exercises are excellent for applying the firehose of functions and concepts from the lecture
von Irmgard T
•20. Juni 2017
Good course, though I found that the lecture content could have matched the knowledge required for the tasks/projects better.
von Md F A
•23. Mai 2016
The demanding complexity for 'programming-project( final )' should offer little more instructional support on lecture slides.
von Alfredo A
•12. Juni 2017
Great introductory course in how to use R to get data from different sources and leave record of this step of data analysis.
von José I F J
•17. Nov. 2016
It is a very good course, but it does not follow the same level of proposed work as in the previous course (R Programming).
von Francisco J D d S F G
•3. Nov. 2016
A thorough course on how to structure and clean dirty data before making analyses on the data - very practical course in R.
von Rejane R d C P
•2. Nov. 2020
I think some assignments may be more clear such as the final project. They give way to several different interpretations.
von Greg R
•14. Apr. 2016
Some parts of the course felt like... here are some cool things you might want to do... Google them. Otherwise valuable.
von Lei S
•24. Dez. 2017
A little bit hard to follow, because there were so many things didn't work for me. I had to figure things out by myself.
von Madhav S K U
•29. Mai 2017
i found this useful and gained knowledge on important pacakages,if you are a beginer in R do not skip "swirl " practice.
von Jeroen v B
•16. Sep. 2018
The course is ok, but a little bit too general. It should require more actual coding, maybe worksheets might be useful.
von Giovanna A G
•25. Sep. 2016
It is amazing to learn how many different kinds of data exists and how to work with them using R. Wonderful course!
von Tran H H T
•21. Feb. 2016
Slides and videos are a bit insufficient in order to finish course projects.
Apart from that, this course is awesome!
von ESTEBAN C P
•23. Aug. 2021
Definetly it´s a better than the 2nd one (R Programming). Hopefully the next one keeps with this didactical level.
von Dylan P
•21. Apr. 2018
I think there should be more graded assignments. The quizzes help but doing more projects would be really helpful.
von jishuenkam
•14. Aug. 2016
I think it is a decent introduction to data cleaning. Could be more detailed in terms of the content delivered.
von Matt C
•22. Dez. 2016
This course was okay, but projects required much more in depth information than the course materials provided.
von Edén S
•4. Juli 2020
Very interesting course where we learn the methods to get data from the web, clean data and getting it clean.
von EzzEddin A
•22. Feb. 2018
This course is brilliant, but I expected more exercises to master more commands in R mentioned in the course.
von Stephen K
•8. Apr. 2017
Great info on accessing data from multiple sources. Also some excellent teaching on relevant R code modules.
von Thiago Y
•30. Aug. 2020
This part of the specialization is very important since 70% of a data scientist work is get and clean data.
von Nikita Z
•21. Mai 2020
I tried my best to the best, certain things were little complicated to understand but it was worth learning
von Agatha L
•16. Aug. 2017
The content choice is fantastic - introduced a lot of great tools - albeit somewhat rushed in presentation.