May 2, 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.
Feb 1, 2016
Easy, mostly instructive Course. The Assignments and quizzes are quite good, and illustrates the lessons very well.\n\nSee the videos for general presentation, but use the energy on the excersizes.
By Mark P•
Jul 12, 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.
By Carlos M C D•
Feb 8, 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.
By Bangda S•
Nov 10, 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.
By Efe Y•
Jan 20, 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.
By Dominic H•
May 27, 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.
By sunsik k•
Jul 18, 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
By Fabiana G•
Jun 23, 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The content of the course is good, but it seems abandoned - some links are outdated or don't work.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.
By Steve W•
Feb 3, 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.
By Andrew G•
Jul 28, 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,
By Wentao B•
Apr 2, 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.
By Justin z•
Apr 13, 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.
By Bekhzod A•
Mar 13, 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).
By Ehab H A•
Feb 4, 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.
By Sven B•
Apr 30, 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.
By Pedro R A O•
Sep 10, 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.
By Rigoberto Á E•
Nov 26, 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.
By Deleted A•
Aug 12, 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.
By Shuwen Y•
May 28, 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.
By Christoph G•
Jun 12, 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.
By Angela L•
Jan 19, 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.
By Juan D P M•
Oct 13, 2021
Some lessons are pretty good, but there is a gap between the lessons and the assigments. You're evaluated in some aspects that are not very explained in the lessons.
By Sahil S•
Jan 16, 2021
Assiignments are out of date, some commands are deprecated. Also, the quizzes and projects require more in depth lessons or practice problems to complete. Thank you
By Yuqi J•
Jan 14, 2019
Some of the lectures on loading data were very dry, but I guess that can't really be helped. Also the final course project's requirements were on the vague side.
By Buddsalakhum R•
Mar 1, 2021
I think this one is easier than the R programming but still, more exercises to practice would be good. Also, the assignment's explanation should be more simple.
By Samantha H•
May 30, 2018
It would be better if we had practice problems along the way. This course seemed to have a lot of commands that didn't stick until I put them into practice.