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.
25 oct. 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.
par Debjit C•
6 juil. 2020
I had a very interesting experience in the course. Thanks to all the help from the discussion forum and data science communities such as StackOverflow . They have the best resources to learn.
The assignments were a bit difficult to understand but once understood, it was quiet easy to solve.
par Bruno V•
22 juin 2020
The course is good but should update its links and go deep into the regex syntax. Moreover, the tasks of the assignment was not difficult. However, it was not easy to understand the tasks as they were not well explained/written. Overall the course is good and I recommend it.
par Kelechi M A•
13 févr. 2022
There is a huge gap between what is touched on in the lectures and the project. The upside is that it shines light on what the student should do further research and study on. The downside is it almost becomes unwise and a waste of time to continue with Coursera.
par Ryan B•
20 avr. 2020
Learned some very useful skills, but I found that some of the weeks moved too quickly without sufficiently explaining the background information required (as someone without a data science background) with abstract concepts that were not grounded in application.
par FARROUK_ABDERRAHIM B•
12 oct. 2020
the assignment project was hard and really not enough instruction was given and it was a machine learning data set which made it very hard :) i mean we hadnt seen anythin similar to that during courses :) fix that and change project assignment for final week
par Mark P•
12 juil. 2021
The course give very broad overviews in the lectures, then drops very difficult questions in the quiz and assiagnments. It is good to push a little and make you dig for solutions on the internet, but the jump in difficulty is too far to make it worthwhile.
par Carlos M C D•
8 févr. 2016
The course is good, but it doesn't really offer all the tools required to pass the exams. I had to take extra courses in other place in order to pass. In addition, the exams some times become a bit too subjective of what the classmates want to grade you.
par Bangda S•
10 nov. 2016
This course provides a lot of methods and strategies about reading data, manipulating data. But I think some important issues in the real world are not discussed enough here, like how to treat missing values, how to deal with messy format data.
par Efe Y•
20 janv. 2021
Had a lot of trouble accessing and downloading datasets from the internet despite I were using the same source codes. Beside teaching how to download data from internet, it would be great if datasets were also included in the course content.
par Dominic H•
27 mai 2018
You will learn valuable tools, techniques and concepts but be prepared to feel overwhelmed (if you have no computer science background whatsoever) by quizzes and the assignment which require you to do research stuff outside of this course.
par sunsik k•
18 juil. 2017
Quite disappointed at 'Getting data' part because of lack of explanation(I only had to learn extra sources to understand) but satisfied with 'Cleaning data' part. It would have been more useful if course described how to use GitHub, at the
par Fabiana G•
23 juin 2016
The content of the course is good, but it seems abandoned - some links are outdated or don't work. I think it would be a much better experience for students if these first courses in the specialization got more love from the instructors.
par Steve W•
3 févr. 2016
The lecture material was high level, and didn't seem to be a good preparation for the quizzes.
The description for the final project was not very detailed, and the grading rubric likewise was not very specific for peer review.
par Andrew G•
28 juil. 2018
I thought the course project grading was supposed to focus on what we learned in class, not almost entirely on creating readme and codebook files. Also, the explanation of what was expected for the project was NOT CLEAR,
par Wentao B•
2 avr. 2016
The content of the course is too general, with too brief introduction of some commands in the lecture notes(slides), I don't think it would be very helpful for the students to deal with some real complicated problems.
par Justin z•
13 avr. 2017
brought up some good concept inside, like "tidy data", but not in detail, how to grab data from different source shouldn't be difficult. should have more focus on talking about data.table, "tidy data" principles etc.
par Bekhzod A•
13 mars 2016
Course provides interesting insight to getting and cleaning data. However, the course misses practical examples (not only showing the code in the slides, but also presenting how it works in R or RStudio).
par Ehab H A•
4 févr. 2019
This course was too hard for me compared to the first two in the program. Not sure whether it is because of my limited background in the subject area, or because of the abrupt shift in level from course 2 to 3.
par Sven B•
30 avr. 2016
This course is of lower quality than the preceding courses. The final assignment instructions are not clear. The forums helped but I have the impression that they are not really followed by the mentors.
par Pedro R A O•
10 sept. 2017
the course is good in terms of the knowledge but it is very unstructured. A lot of topics are treted just superficialy and the activities do not address the content of que lectures completelly.
par Rigoberto Á E•
26 nov. 2017
The professor Leak is not as gifted (in terms of teaching skills) as R. Peng. In some of the lectures he just reads what it's in the presentations but he does not go very deep into them.
par Deleted A•
12 août 2016
Contents in first half weeks are very superficial, have low depth so that do not help me do some meaningful studies. But later ones are good for understanding the structure of data.
par Shuwen Y•
28 mai 2016
less hands-on exercises and this course covers too much topics without details. More like general intro to each tool and data sources. Swirl is still a great package for practice.
par Christoph G•
12 juin 2016
I liked it, but I had the impression it wasn't as prepared as the other courses. Especially with the course assignment I had a bit trouble to understand, what was wanted.
par Angela L•
19 janv. 2016
This is not a beginner's course, so a decent grasp of the R language is necessary. It is best to take this course after some stints with Data Camp, Swirl, or Code School.