Labs are very interesting. Would suggest including more data wrangling techniques (to better align with the final assignment), and removing Mapbox Bright references. On the whole, this was a far more interesting module than I had expected.

The Course helped me to clear my doubts and made my concepts of Data Visualization more strong.

The Ungraded assignments helped me to apply the concepts in practice. The quizzes were easy and I think there should be more questions in quiz.

This was more difficult than expected, but I did enjoy the lab work as challenging as they were, I found it helped in bringing previous learning all together. I took much longer in completing the labs than the estimated time provided.

OK overall - but I wish there were more and smaller hands-on exercises. Also, waiting to load 3rd party libraries in exercises is quite slow (3-5 mins for choropleth, for example) - it would be nice if these could be pre-loaded somehow.

Course provided very detailed introduction and lab sessions were great however at of today April 11,2022. A few line of codes have been deprecated, though it might (or not) affect the experience, I hope the course will be updated soon.

Good intro to matplotlib, visualization of dataframes. Some of the more advance content, e.g. choropleth maps, relies on older versions of some of the python packages (folium) so the examples need to be updated for the newer versions.

the course is great, however, I think matplotlib's structure should have been explained in greater detail, this would help the student to accelerate the understanding of the library's documentation and implement his visualizing needs.

Good materials but need to be continuously updated since the libraries and such change often. There were times that only a specific version of Python and a library would work at all. It would be better if it weren't so fragile.

Great module to learn about the most common different types of plots we could generate with Python coding. I would suggest to deepen more in Matplotlib's Pyplot scenarios, including tick, label, legenda nd filling options.

I enjoyed the course to the fullest. My only complaint is regarding question number 2 in the final assignment. The bar chart that we were requested to create and display is not shown in the videos or labs how to create it.

Generally good content with great lab additions except for Folium. Folium 0.5.0 is outdated (it is 0.11.0) now and the choropleth method is deprecated. Also, the final assignment threshold label differs between versions.

Course content is good. But there're some replication for each video about "recap of the data". The final assignment is quite difficult because I need to find some codes from the internet to meet some task requirements.

After learning, I still feel a bit confused. I think it would be better if there is a comprehensive summary, such as: what graphics use which imports, and the comparison between the coding required by each graphic

Good course but some parts need additional explanations to better understand. Anyway, links to additional resources have been provided in complicated areas. So overall helped me a lot to understand the concepts.

Very tough course, needed to really dive into books about folium and etc...for the final peer to peer grade exam

but thats fine, sometimes in data science you need to really look and search to find solutions.

Cours intéressant et évaluation pertinente. Il est cependant plus optimal de s'appuyer sur des ressources externes pour avoir accès à des mémos sur Pandas et Matplotlib (il y a de très bons sites là-dessus).

Videos were short and could have explained the lab work better. Also, the Data was discussed in every single video which was annoying and I always skipped 30 seconds in every video.

The course itself is nice.

Course material did not prepare you well for the final assignment, the final assignment was too difficult and didn't have enough clear instruction. Overall, the course material was very interesting though.

Course was interesting. Few more sample exercises on the features of map, artist layer could have been useful. Since these are more visualizing concepts which requires more practice and thinking. THANK YOU

This course and the following course "Data Analysis with Python" should be switched. It's mentioned that "Data Analysis with Python" should be completed before this one but they are in the reverse order.

Good course, some of the lab assignments did not load properly so it was difficult to practice... (week 2 & 3). Assignment was good after using Jupyter Notebooks as the scripting interface. Thank you!

This course is asking for more details. It could be extended to one or two more weeks in order to provide broader understand and examples of how to make good use of visualization tools and resources.

The final assignment requires self-research (not included in the course material) to fully complete the required items. The course shall cover all that the assignment requires, at least touch a bit.

In each video we transform dataset and it take more 1 minute for each video. Will be good if in video will be some quick quiz as in "Data Analysis with Python" and "Python for Data Science and AI"

The Course Was Good. It would have been better if some lab sections were covered in labs. As we all know understanding a code then reading might help the students grasp better faster and deeper.