Overall really good. However, would be better if a few videos added about object-oriented visualization. Also some links and methods used should be updated to the latest ones.

Best of the 5 IBM Data Science Courses I've taken so far. Some problems connecting with the labs, but you can bypass these by downloading the ipynb's from cognitiveclass.ai.

It was very nice and brief course but it could have been better. Some other topics must be included and some more exploration of different properties needed to be addressed

This course, although useful was difficult to follow at times. It did not get that into the Artist Layer of Matplotlib but the final project requires the student to use it.

It's an informative course, it even tested a student's perseverance and creativity in solving/bypassing various bugs. The final assignment was definitely an eye opener.

final assignment is tough. Everything else was decent and intuitive. Good jupyter notebooks and labs for practice were provided. Do practice all ungraded lab sessions.

Would've enjoyed the course more, if it got into the nitty gritty of annotations, but a comprehensive and decently delivered course nonetheless. Kudos to the IBM team.

The learning was good - I had a really hard time with getting the IDE environment and labs/assignments to work well. Doing screenshots over and over is inefficient.

Just like the few previous Python courses by IBM - errors and typos have yet to be fixed. But other than that, it is a really good introduction into using Matplotlib.

This is a very fascinating and demanding course when one follows all the Skills Network Lab exercises. The advanced visualization tools are worth the trouble!

Examples contained less python codes as compared to asked in final assignment. More python codes for visualization are to be conveyed.

Queries are not solved.

The course content was good, but final assignment needed more clarity in the what was demanded from the question. Lots of interpretation left for the student.

Some tests are not comprehensible in their entirety and can only be carried out with a great deal of effort. Otherwise very good content. I could learn a lot.

Material had great breadth of topics. Should also cover related topics like how to add "narratives" to the visualizations to enhance storytelling skills.

Course is really helpful for indulging someone into data visualization but sometimes in the lab some stuff is just present for you to figure out yourself.

The information provided was straightforward and easy to understand. However, the final lab requires extended knowledge that is not covered in the videos.

Excellent lab material. However, I feel the video lectures were a bit too brief and could have tried to explain the technical concepts a little bit more.

Nice course. But Seaborn examples could have been more helpful. Also, please use Python 3 for examples. Thanks for the video and more better class labs.

The Course is an interesting and practical Course. Alex Aklson, the lecturer is a good teacher too.

Many thanks to IBM and all the teachers on Coursera.

The Course materials are brief and Short and understandable. No need to learn junk topic only relevant areas are learn in this course. Thanks to IBM .

The course was good, syllabus was okay, I think that seaborn could have also been added in this, though waffle chart and map was very new for me...

Good condense course. One thing is a the recall of the data set and the lab at the beginning or at the end of each lecture which are very annoying.

A lot of information was packed into this course. There was not a lot of room to practise the material. The final week especially was challenging.

A good overview of different visualization methods in Python. The final assignment is a little tricky and requires a diverse set of Python skills.

Its a great course to learn all Data Visualization libraries in python and thier constructs. It helps me alot to learn Data Science using Python.