Interesting course with several ways to plot data in Python. I think it briefly goes over the main topics, but I would have liked to see some more examples and explanation. Also, the assignments at sometimes focussed to much on a particular plotting exercise, whereas I would've preferred more exercises with a bit simpler plots yet exposing me to more libraries. On the other hand, the assignments were very relevant and insightful.

Another great course, very informative and useful with good real-world assignments. Very clear lectures and the Jupyter notebooks are very useful too. My only gripe is that assignments are graded by other students. Not ideal. Some students don't fully grasp the concepts. You can resubmit so that you'll be graded again, but... that doesn't guarantee you'll get better graders. But that aside, it's a great course!

Excellent course, which will teach you a lot if you put the effort in. I spent a lot of time exploring matplotlib, and python, as well as reading the design resources.

Unfortunately, when I marked the final assignment, 2 of the 4 I saw were clearly plagarised. This seems to be an ongoing problem, and so the certificate itself should be treated with caution. That's why I've given it 4 stars, rather than 5.

This course teaches the extremely important concept of high-quality visualization in data science. Having performed similar staff in PhD, I did not have much difficulty in assignments, but it can be tough to learn and apply the concepts for beginners, if they do not spend some significant amount of time for the assignments. There could be more practical quizzes or practice exams to aid learning.

The course taught me well how matplotlib works, as well as quite a bit of theory of making plots and what to look for. This knowledge is very useful and applicable in many situations when doing data science. On the other hand, it required perhaps too much work. Ocassionaly I had a feeling some of the work was repetitive and not leading to new knowledge any more. But overall, very well worth it!

Great course, I wish I could have more time between evaluations to explore more details and artistic details of matplotlib figures. It have four weeks and the necessary contents are mostly in the course but there are many interesting external references that help in the course if they are read. Stackoverflow questions and answers are very useful to accomplish some course objectives.

I had a lot of trouble with some of the assignments and often I had to see other peoples solutions before being able to come up with my own because I just did not understand what was being asked of me. But I really liked the final assignment where they just ask you to use what you have learned for something real, so in the end I am satisfied and I have learn a lot of pyplot.

This course is a great introduction and application of charting and plotting in the Python matplotlib module. It also covers the fundamental science and theory behind making impactful charts, which has had a big impact on how I think about charts and present data. I especially enjoyed learning some of the more sophisticated capabilities of plotting with matplotlib and pandas

I learned so much from this course. The assignments were interesting and the lectures engaging. My one complaint is that as a complete beginner to this field (even after the Intro to Data Science w/ Python course), the assignments were incredibly difficult. Completing them was often a several week endeavor, but finally completing the work was always satisfying.

Good course on teaching you the fundementals of matplotlib. However, there were many things which could have been expanded on. The entire course felt more like a continuation of the last course as opposed to its own course. However, the assignments and the course content were informative, and will give you a solid introduction to matplotlib.

The material of the first week is extremely good and inspiring. However, the following lectures and assignments aren't directing us to the exact result. Could have given more lectures on how to generate those high-quality graphs. For now, everything is too basic to that goal. Looking forward to the more advanced data visualization stuff!

I found the course to be very informative and requiring the student to think differently about data and how to represent that data. I also like the ethical aspects of the course, that require the author of the charts to really thinking about the message being conveyed, how it is be presented and the message that might be inferred.

Overall great class, you will nead tons of research outside the class material, but you do learn a lot. P.s. so many learners just copy and paste the final project which is annoying. I was expecting to see some new way to to analyze problem by utilizing new skill that people acquired from this class. Otherwise all good.

Good intro to plotting, charting and visualization in Python. Focuses mainly on matplotlib. I feel good about the content that I learned, but also feel like I wanted to learn more in this class. Maybe more coverage of other python charting libraries. More examples of financial type charts -- High/Low/Open/Close etc.

Theory part was kept quite short and most of the time is spent programming, which I liked a lot. You will need to research lots of stuff on your own to finsih the assignments. Maybe some more information would have been helpful at times.

Overall a solid course and I certainly improved my coding doing the exercsises.

Good introduction to working with matplotlib.

All the parts about theoretical/aesthetic considerations when making figures were, in my opinion, very fuzzy, unenjoyable and somewhat of a waste of time. These are things which should be taught through experience or many specific examples, not long-form articles.

Great course. I learned how to use matplotlib. The professor is excellent and all of the material makes sense. The only regret I have is that the class was a bit too short and relied too much on googling features of matplotlib. But, that's how industry goes so I am OK with the course overall.

Most of the learning was self-directed as I worked through the assignments using Stackoverflow and other online resources and documentation. It would have helped to provide a lot more instruction on customizing templates such as the Seaborn library, adding notations, and fixing alignment issues.

Provided enough structure and worked examples to give a meaningful introduction to the topic, but left the assignments open-ended enough that they were interesting and challenging. I ended up spending a lot of time on the various assignments, but actually found the whole process quite rewarding.

This course is okay. I think it tries to cover to much ground in to few lectures, particularly when it comes to more advance features and functions of matplotlib like animations and interactivty. It devotes a whole week to theoretical discussion of graphic design which is okay but not great.

Good course overall. Like previous one, you can do as much as you want. How much you learn is completely dependant on you. It is not difficult to pass it, but if you want to get the best out of it during the assignments, you will have to spend some time in stackoverflow and matplotlib help.

Great exploration and navigation through the not so straightforward matplotlib for Python. The lecture would have been perfect if there were not so many references to a local library, which helped with the concepts but left the student with nothing to reuse in the future.

good lectures and theory. I miss automatic code evaluation and more numpy data transformations. I suck at these and I was hoping this course would have more of the same kind of tasks as the first + plotting.

Overall, good class as a starter point for plotting in pandas.

The course and material is great. The videos are not too long, but they provide the necessary guidance. I do think that the peer-reviewed grading could be improved. Feedback is minimal, and I do not think anyone really gets feedback on the quality of their code.

Several different API's are touched (matplotlib matlab-style and object oriented interfaces, seaborn, pandas, and it's easy to get lost. Some additional reference material would be helpful: cheat sheets, course slides with a bit more detail of the API's touched.