Absolutely bad instructor with the videos not elaborating enough and the labs not functioning properly. Please fix it

So far this is the worse course- the coding is not well explained and the lessons are extremely ripetitive!

The videos were very repetitive, and didn't teach you much about best uses for diferent kinds of graphs.

Bad instruction to finish the assignment and not enough tools to get the objective done

Just Awful. Test is nothing like the practice modules. Very poorly done.

Total emarrassment for IBM

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 course with the IBM Lab is a very good way to learn and practice. The tools we've learned in this module can supply a good material to enrich all data work that need to be presented in a nice way.

Great course, one of the best course to get hands-on learning for Data Visualization with Python. Particularly the lap exercise, it will make you think on every line of code you write. Excellent!!!

It's a really great course with proper hands on time and the assignments are great too. i got enough opportunity to explore the things which were taught in the course. Really Satisfied. Thanks :)

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.

Really good course with easy to understand materials and wide varity of visualization techniques and tools.

The assessment was really complex, but the course overall is really usefull!!

Amazing course!!!! I liked your very detailed and well-organized notebooks <3

The course was overall, pretty good. Although it was extremely repetitive with regard to 'cleaning' the data, the information covered was explained and shown pretty well. The lab sessions were detailed. I would have liked to see more of the possible implementations as opposed to manipulation of the aesthetic. I also hoped they would cover seaborn in more detail.

Although most people are against the final assignment, I actually enjoyed it as the previous courses gave us a jupyter notebook with most of the work already done, only letting us write the main part of the code. Coding from scratch with just the dataset helped me understand the topic better and will definitely make it easier the next time I attempt data visualization.

The course is very good. Intuitive and easy to follow. The real challenge is in the peer review exercises, where your patience is tested. You really have to work hard to get all the solutions to the questions. There are so many things that the course just can't teach you in the time constraints.

A great course for you to further understand the mechanics of data visualisation as well as providing a space for you to familiarise and test your understanding on the subject matter.

this course should come before data analysis with python

Although I understand that learning doesn't take place at only one place, this course seemed very weak in terms of providing enough examples necessary to solve the problems in the final assignment.

All videos had a same part that was repeated, and no information was agregated by this repetition.

The contents of the labs are quite good, but a more detailed explanation could exist.

Some updates are needed: one of the labs uses MapBoxBright, which gives us a clean figure with no map because this is not available anymore.

The final assignment required us to look for solutions that were not present in the course, and in my opinion, they should be. The student should go to outside sources when it feels a need to understand something deeply or if the way presented by the instructors was not the best for the student to understand what's going on.

There's a lot of room for improvement: the videos should not be repetitive; the contents should be updated, anything that is required in the assignment should be presented throughout the course, if it's not in the labs it should at least be in the videos; the final assignment could provide a notebook with the requirements as the other courses in the specialization offer (in my case, I took it as part of Data Science Professional Certificate by IBM); if this is not the case, the student should be prompted to create a notebook with the questions and answers, which would estimulate even more the creativity around data visualization.

This course was nice but there were extra stressors that weren't included in the course.

Course could benefit from a refresh - for instance, support for Mapbox Bright tiles has been dropped from Folium for 1+ year, but the course still tries to demonstrate their use. There are several posts from confused students wondering why this doesn't work. Surely it'd be better to just remove/update this section of the course rather than have to deal with so many bug reports in the forum?

Also the videos for this course are extremely repetitive and barely of any relevance, e.g. 1-2 mins of several of the videos is just the same footage of the data being imported to Pandas and cleaned. Once you've seen this once, you've more or less got the point. Add to this that the Final Assignment required knowledge of matplotlib which was *not* covered in the course, and had to be researched elsewhere, and it seems obvious that the quality and relevance of the video content could be improved significantly.

Out of all the courses in the IBM Data Science Professional Certificate, this was the one I had the highest expectation for and unfortunately I was a bit disappointed. The course materials are lacking in information and the final assignment asks for customisations that weren't covered in the course materials, which leads to question: are these important things to know and the materials are lacking in information ? Or are these irrelevant and should be a part of the final assignment? Because if they're just there to make sure no one gets a 100% grade, then that's just sad.

This class could have been one of the best based on my interest, but it wasnt explained very well and I had to use outside sources to figure out what was going on in the labs and sections... Also some of the final project material wasnt covered in the class itself... It was more difficult than it needed to be... Once I used Google to find answers, the stuff I actually learned were useful...

Much of what was tested, was not taught in the course. Therefore, the course requires individuals to do their own research online to answer the final assignment questions.

The purpose of paying for the course is to quickly learn fundamentals from the course, NOT to spend hours looking online for the right answers and waste time with trial and error experimentation.