Peer-grading is horrible. Nobody reads assignments or checks your work - they just give top grade for every category and leave "." as a comment, all to breeze through the mandatory peer grading. This certificate has very low value.

I think there there is too much time given to the esoteric of what makes plots pretty rather than the nuts and bolts of how to do it and the limitations of using Pandas and Matplotlib for real world data

I feel like I didn't learn much. Various topics are covered but quickly and superficially. The notebooks provided each week are useful. Almost all assignments are peer reviewed.

Another one of those UoM courses where you learn nothing unless you scour the internet for actual education. Makes one wonder what value UoM brings to the table...

I am not too crazy over the peer review assignments plus the course was hard to follow

Not well organized.

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If you are already an expert at python and data science you might enjoy this course. If, on the other hand, you would like to learn plotting in python, this is a very poorly taught course.The lectures are too fast and cover too much material before you get a chance to practice. When you do get assignments, they are ambiguously worded and expect you to research way beyond the course material before being able to do them. Very disappointing.

Disappointing. Matplotlib is built from layers of interacting functionality, and this course doesn't create a structure to understand it. Unclear and confusing. Note however that the following courses in the specialization show matplotlib code but don't necessitate writing it, so you can do them (at most auditing this course before) and only return to this course if you want a specialization certificate.

Another course where the lectures are basic with madeup, easy examples and the assignments are real world, messy with poor direction and unclear end results. You have to use the forums to understand the desired outcome and then spend your time on Stack Overflow to solve. Why then even pay for a course?

This course is absolutely terrible, and in no way self-sufficient. The professor basically tells you what can be done using matplotlib, give you a cursory example and leaves you all on your own to understand what actually happened by referring to sources such as google or stack overflow.

This course needs a serious overhaul. Assignments are very unclear and only reviewed by other students, which questions the legitimacy of this course certificate. Lectures are shallow and clearly made by very unexperienced lecturers.

Nice assignments but spent most of my time on Google rather than utilizing my notes made from the video lectures. That kinda destroys the purpose of taking an online course in the first place.

Very hard to grasp the content as the content is not well explained in the required detail, as it should have been.

A lot of technical errors to submit assignments....

Spent more time to resolve the technical issues than learning

Assignments are not clear at all.

Things covered in videos are not enough to complete course assignments.

fake reviews from coursera bots, assigment is scam too :P

Too slow grading and a very inefficient process.

Peer review is not good.

Not very well done

really bad!

NOT GOOD

Overall, the course is great and definitely deserves 5-star rating.

However, it starts quite slow and in my opinion first few lectures discuss irrelevant topics, like minimalism of presentation. The problem is that a person can't grasp them without experience...

For example, several videos discuss idea of Edward Tufte. I understand that CS and mathematical statistics are the background of the instructor, but really, Tufte had only repeated well-known basics. Indeed, it was Leonardo da Vinci who first said that "simplicity is the ultimate sophistication". He was followed by Antoine de Saint Exupéry with "It seems that perfection is attained not when there is nothing more to add, but when there is nothing more to remove" and the KISS principle of Kelly Johnson of Lockheed Martin Skunk Works.

Perhaps, for the authors of the course software engineering is closer: https://wiki.archlinux.org/index.php/Arch_Linux#Principles ...

I found the course very rewarding, and I was surprised how easy it is to make nice looking graphs in python. Extra points to teachers for putting substantial emphasis on good design and aesthetics.

You can pass the course without making any animations or interactive graphics; however, I found those assignments most rewarding so I recommend you give them a try.

Workload-wise, this course took me about double the amount indicated on the course website, but it would have taken considerably less time if I had set the bar lower for myself.

As with Course 1 of this specialisation, the lectures only give an introduction to the topics and you'll have to look up matplotlib documentation and answers from stackoverflow to complete the assignments. I found this course less challenging than the first one (but still challenging enough for sure!).

Perfect, insightful, deep, challenging! I love the way prof. Christofer Brooks teach Data Science. Interactive IPython notebooks enables creativity to implement lecture notes right in the browser during watching lections.

I enrolled to "Applied Plotting, Charting & Data Representation in Python" course right after finishing the first "Python for Data Science" module. This is one of the best experiencies I got during my online education.

There are a very active forum discussions on this course, people and course staff are helpful.

Next, I want to enroll next courses of the Specialization.

Also I would like to say "Thank you" to course team and Coursera for the financial aid opportunity.