It was a good course that follows steps clearly and effectively. However, I cannot rate it higher that 2 stars for a very important reasons: Big Parts of the Final peer-reviewed assignment are not even covered in the course!!! I had to scour the internet and find my own solutions (and many others clearly had the same problem as seen in the discussions section). This is a big problem and needs to be addressed as we should be tested on the material actually learnt!

Also, almost every video repeats how the data is 'cleaned' which is good once or twice, but unnecessary the 15th time.

The final assessment was not covered in class, and it was very difficult to figure out how to do.

I did not like that some assignments do no rely on the material that was given in the course. For example, data visualisation with Artist layer was not covered in details in the course and you have to spend tons of time on Internet digging out how to implement that. This is a waste of time, I need a course that gives me complete and structured info, not a course that sends me out to explore the Internet.

Final exam was frustrating. It took longer to complete than the rest of the course combined. Questions were included that were not part of the course including the need to reset keys. Peer review was almost impossible since I could not read the tiny screen shots very well.

Data could in most cases not be loaded, making the labs useless. Also, the videos have unnecessarily much redundancy. Really didn't learn much from this course.

It is a strange course and the worst in this specialisation so far. In one week 50% of the video is how the data is prepared. That would be ok if it won´t be the same video snippet 4 times. Also the relation of video vs. reading is 1 to 6. In one week is only 6 minuites of Video and about 1 h of ungraded assignment.

The final assignment isn´t solvable with the given code or examples. It is ok that one had to google for code snippets, but this is far too much.

The course is very inconsistent, it repeats the same one minute in all of the videos, when reviewing the dataframe. Many times some things are asked without providing previous explanation, and the final assignment is also an example, I had to search all over internet to resolve it, because I couldn't find any reference in the content provided.

worst instructor I have ever seen,

very few instruction but the assignment is extremely hard!!

I would give zero if the system has. An empty course that you almost cannot learn anything especially when you're going to practice.

No mid-lecture quizzes

End of section quizzes test rote memorization

Narration is poorly done

The course material is not sufficient for completing the final graded assignment. It required many hours of internet research to collect the details necessary for the final graded assignment.

The course materials are poorly structured. Labs are not well-designed and not friendly to students with little experience. This is not a very effective course.

This course was really well designed. I've taken the preceding courses and I really connected with the format of this course. I liked how the labs really explored different options and played around with the code in a variety of ways to show a more complete picture of what the code instances can do. I especially liked how sometimes the labs would purposely use incorrect code which users might enter, and then explain why that code didn't apply or work for given scenarios.

I liked how the videos (specifically regarding Canada dataset) would repeat the cleansing of the data and introduction of the data for each type of plot because it really reinforced the concept, however it could have been better if the corresponding code were displayed alongside its effects instead of just showing its effects because then it would drive home the code and the concept instead of just the concept.

I also liked the fact that the final project asked students to make connections beyond the individual class scope, as a way of teaching that mimics real-world projects and learning.

I appreciate that the videos were done in a human's voice and not a robot. It helps me focus to hear the natural pace and emphasis on certain points. Also, the labs were very clear (thank you). There was clear guidance/notes through steps which is very helpful because this is a new thing for me. The final was also fair and comprehensive. I have a long way to go but this class was very well done.

The course labs had broken links which caused issues with several of the students. The quizzes also had several question choices where two of the answer choices were the exact same, leaving the student to guess. Not to be so critical, although the datacamp classes are much more effective when it comes to learning.

A lot of functions and lines of code weren't explained they were just left to be figured out by the learner. While some lines of code could be understood without much explanation, others were too complex for people new to programming (which most people taking this course are).

This was a valuable course to learn visualization with Python. I especially appreciate the section on mapping and the waffle charts. The only suggestion I would offer is to have more opportunities for practice coding, especially for grouping data for analysis, labeling charts, calculating percentages. These could either be optional assignments or links to other resources. But regardless, this is the best course out of the series for me so far.

This course is really good. The instructor did a great job introducing common graphs, charts and map techniques. What they look like. How, where and when to use them.

The lab is time-intensive which give chance to thoroughly practice the technique. One more plus point is the lab uses real data and guide you through the step of retrieving, cleaning, analyzing, visualizing and mapping.

Definitely recommend.

Great course, extremely thorough! Interesting assignments. It is more than obvious that the author has put time and effort into it. Thanks for that :)

A lot of the work in the final project was not in the course (in either the content or the lab work). There were plenty of topics covered that could have formed a challenging final project without asking us to do things that we weren't equipped to do by the course. Fortunately, I was able to find what I needed but after putting all of that work into the course and labs, I shouldn't have had to spend as many hours on the final project as I did. If those are things I needed to know, then the content was inadequate. If I didn't need to know those things, the project was poorly created.

ZERO support from our teachers, assignments that have little to do with what they teach us (Videos don't even have any information explaining core concepts) Most of the learning was done by Google. Quite annoying to be honest.

Need more detailed explaination of artist layer. Very confusing. Questions on topics which are not covered in syllabus.

This course provides lectures that enable learners to understand the theory, application and practices that data scientists use to create meaningful visual presentations of complex data relationships. The labs provide adequate opportunities to do hands-on end-to-end work with data and visualization tools. The learner is challenged to go beyond the scope of information presented in the course to also search other resources to gain the knowledge necessary to complete the final project. Searching for additional resources builds a foundation for independent future work.

The final project was somewhat more challenging due to some file downloading issues. But I was able to get some help in the forums for that, which helped me accomplish my goals.

I learned and understood how to make graphics based on a previously clean and standardized data source. I liked this section.