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Applied Plotting, Charting & Data Representation in Python に戻る

ミシガン大学(University of Michigan) による Applied Plotting, Charting & Data Representation in Python の受講者のレビューおよびフィードバック



This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The course will start with a design and information literacy perspective, touching on what makes a good and bad visualization, and what statistical measures translate into in terms of visualizations. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will be a tutorial of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. The course will end with a discussion of other forms of structuring and visualizing data. This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python....




its actually a good course as it starts from fundamentals of visualization to the data visualization,the assignments this course provide are exciting and full of knowledge that you learn in course ..



I am going for the specialization and I know this is just the second course in it and I haven't even seen the further courses yet, but this is already my most favourite course in the specialization.


Applied Plotting, Charting & Data Representation in Python: 901 - 925 / 1,002 レビュー

by John P


Thank you!

by Anant k


GOOD one

by Tất T V















by Eklavya J



by Lukas K


Overall an ok course, but unfortunately not in caliber as the first course in this specialization: Introduction to Data Science in Python. The course is sometimes very difficult and sometimes very easy. I understand that visualization is a hard topic to teach especially online, with a peer grading system but some improvements are needed to get full score. I learned much anyway and if you already done the first course and strive to get the specialization this is a no brainer.

Cons: # The major bad thing, few guidelines/best practice how to implement different visualization styles, hints&tricks etc. I ended up to stack overflow and google for numerous hours to find out which coding style fit’s to solve the majority of tasks when more advanced visualizations needed to be achieved.

# The matplotlib composition is complex in my opinion and it’s hard to understand what is: a instance, a static class or just a function callback etc. when invoking different matplotlib functions against artists, backend etc. This was the feeling during the first 3 weeks, not until the last week 4 and the final assignment I start to finally understand how to be efficient in matplotlib.

Pros: # Considered the lack of best practice and more advanced examples forces you to learn by yourself (aka Coursera discussion forums, google, stack overflow etc.) This is great but I don’t think this was the intention when creating this course. # Great to give the possibility to use your own data in the final assignment in week 4. It was a real motivator for me to push myself to learn the most and finally start to write descent code from matplotlib point of view.

by Vladimir I


Overall, it is a reasonably good course. Content touches not only how to 'program' a simple / interactive / animated visual but also some theoretic aspects of plotting in general. An interesting thing about this course is that you will decide how challenging the submissions will be though this will not affect your grades. My final assignment for this course: In this course, I learned how to create an interactive plot and applied this knowledge in order to create a portfolio-ready visualization.

Though, since this course is about plotting and charting there is a lack of visual materials and great examples of use cases. For potential Russian-speaking listeners, I would recommend sticking with the MIPT-Yandex specialization instead of this one. If that specialization would seem too hard then finish this specialization first. Though, they both specified as an intermediate level. I would claim that this one is for beginners.

by VenusW


First of all, the instructor is very responsible, keep updating information on the forum and course material. The course is a decent level of basic plotting technique review, should be in more detail. Compared with the first course of this specialization, this second course is much less challenging, require less effort to accomplish. The first course is the one attract me of this specialization, the second one, somehow, is a bit disappointing, especially compared with plotting skill of R in another data science specialization, which is even an elementary level course. This course cannot be labeled as intermediate level.

Another problem with this course is the peer review, the grading policy should be changed to punish irresponsible reviewers, no useful feedback got. What kind of responsible one provide feedback in two words, where require to answer three questions (week 4 assignment) to review.

by Benny P


The video guide is pretty good, it shows you a lot of thing that you need to learn. It covers a lot of breadth and depth, but only briefly. For further info, and for the most part of your time when doing assignment, you need to seek the relevant manuals yourself. But that is fine, because matplotlib is very very rich library and there's no way all can be taught in a single course like this, and also it makes you familiar with how to find information yourself.

The main drawback is with the assignments though. I'm okay with the peer review system. The problem is that the assignment specification is not too clear. For example, in assignment 4, you need to think yourself about what you want to visualize. So a lot of time was spent on thinking about WHAT problem to display rather than HOW to address the problem (using plotting/visualization), which is the subject of this course.

by Philipp A R


I liked the first course in this specialization more. As in the first one, the assignments require you to search StackOverflow, documentations and the discussion forums; videos are nice, but you won't learn a lot from them. Peer review is a double-edged sword. Some reviewers will give quite elaborate feedback, others do not put a lot of effort into their reviews. Peer-reviewing others can be quite annoying as often you have to wait several hours for submissions of other learners. Not to mention the quite large amount of learners who hand in plagiarized code (please look out for these cases if you participate in this course).

by Tiberiu T


This is a whirlwind course that glibly covers some very important concepts without devoting enough time to each one. Week One, although important, should be replaced with more coverage of matplotlib, or a review of the different types of charts and when to use them.

Also, although I appreciate the in-video quizzes, it is difficult to go back and review the concepts you learn from them because they are not in the Jupyter notebook. For instance, there was a method Dr. Brooks used in a solution to an in-video quiz and I could not remember where I had seen it. I stumbled on it again after reviewing the video for something else.

by brian a


The first course in this series was really good and this one was so-so at best. I got some skills out of it since I obsessively plotted everything and over did the assignments, but the peer grading rubrics are crap. It's all or nothing so if you submit *anything*, you get a grade (and it usually approaches 100%) but I didn't really get much helpful/thoughtful feedback on anything I did since you literally get ZERO feedback from the instructors (nothing!) nor did I get much in the way of helpful info from the people who peer reviewed my work. I find that pretty disappointing really.

by Oliverio J S J


The contents of this course are interesting from the point of view of software engineering, but I am not sure if data scientist need such deep knowledge of graphic libraries. The main problem with the course is that the assignments require much more time than the one indicated in the course planning. In addition, assignment descriptions are often confusing, open to interpretation, and lack enough level of detail, which forces the students to begin by investigating what they have been asked to do.

by Jaime R


V​isualization in Python is still a bit of a chaotic mess, with so many different interfaces to matplotlib, one is left a bit confused, and its a bit hard to become a power user. Was hoping this course would provide a solid foundatation. Not sure I'm any more of a power user after this course. I'm still left to googling and looking at package code base to decipher how to best leverage. All this eats time, time not being spent doing analysis which should be ones focus.

by Bhavin P


This course introduces the learner to the various design principles that need to be followed while creating effective visualisations that include Alberto Cairo and Edward Tufte's work. It explains the information-visualisation wheel and proceeds to explain how to create visualisations in python using the Matplotlib Library. Various kinds of plots such as Line Charts, Bar Charts, Histograms, Scatter Charts are covered. Seaborn is introduced as additional library.

by Sarah B


This course gives an overview of plotting capabilities but I think it could have been presented more methodically. I think the challenge is that there are many ways to generate plots and so this is more a survey of those capabilities. I now know enough to go to stack overflow and matplotlib documentation and figure out what I need to get done, so my goal is accomplished, but my understanding of the plumbing of the different commands feels a big hazy.

by Alex W


The instructions for the second assignment are terrible. My peers graded my assignment based on what they thought the instructions implied I should have done instead of what it explicitly stated so I may have to repeat the assignment and could risk not passing the course which puts my whole specialization at risk. It's ridiculous since I spent sooooo much time on the assignment already due to lack of guidance from the video lectures.

by Jonathan V C


All material, explanations and content are great, no complains there, but I insist with the peer-graded assignments, we don't know if we are being graded well and some people just don't care, take points off for no reason associated with the rubric. Also, I like when the data source is given, I don't have time to search for a source of information that fits my investigation or the imposed topic of the last assignment.

by Peihong H


First, I would prefer there is a way to download the sample code professor Brooks used in the course. The screen showing his code moved too fast, and I have to pause and typed to try them out. Second, I will suggest the course show more code examples, more explanation for matplotlib architecture rather than most of the time just verbal description from the profession

by Renier B


The course is okey - lots of fuzzy theory such as Cairo's principles. Interesting stuff, but also quite self explanatory and seemed like a waste of time.

I would say its worth it to do this course if you have not had any exposure to Matplotlib or seaborn, but if you've done any significant using those then this course will feel a bit underwhelming.

by Venkatesh P


The python course followed a reinforcement approach with multiple examples and practice problems after every video. I liked the assignments in this course very much. They are very challenging. However the course isn't very informative. It will be helpful if the course is modified in a similar style to Python courses.

by Varun D P


Lot of working is required from our side. Not at all an introductory course! More like advanced level course. Requires lot of time, we have to find the documentation ourselves and ask question on stackoverflow. If you are looking for introductory course like Dr. Chucks courses I suggest not to take this course.