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學生對 密歇根大学 提供的 Applied Plotting, Charting & Data Representation in Python 的評價和反饋

3,764 個評分
603 條評論


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....



Nov 03, 2017

Loved the course! This course teaches you details about matplotlib and enables you to produce beautiful and accurate graphs.. Assignments are challanging, and helps to build a solid foundation.


Jun 28, 2017

Good course to learned matplotlib and other Graphs libraries, but the course goes further than Python and also encourages the studies to create more meaningful and beautiful Graphic views.


26 - Applied Plotting, Charting & Data Representation in Python 的 50 個評論(共 589 個)

創建者 Patrik T

Nov 10, 2019

CONTENT: The instructor shows some examples of different plots in python (e.g. line, bar, scatter) and some concepts (e.g. histograms or heat maps) but doesn't properly explain anything. Mostly you'll get an example graph with snippets of code only working for that particular example and for the assignment you're "strongly encouraged to use other sources". That's not what you're supposed to get when you're paying for an online course. You should get proper explanations.

ASSIGNMENTS: You're basically told to get data from any source you like and then plot some graphs. If you've had some experience with python and got your explanations for plotting from somewhere else, you'll mostly spend more time looking for data to present than for the actual assignment.

I don't understand why there's no selection of graphs and data sets to choose from so you can concentrate on programming and properly presenting data rather than wasting your time looking at reddit like recommended by the instructor.

ASSIGNMENT GRADING: You’ll have to grade your peers’ assignments with a rubric that’s just not working: you can give points for someone uploading an image/writing a paragraph of text, but you have to either give 0 or 100%, so there’s not way to properly grade partially wrong answers. Example: yes, there is an uploaded image and the student has explained how it follows “Cairo’s principle of beauty”, but it doesn’t follow the principle of beauty. So, how to grade: zero or hundred percent?

Likewise, your assignments are graded by your peers, so you’ll usually have at least one or two days to add to each assignment. You should take this into account when opting for the monthly subscription. Additionally, neither you nor your peers are qualified to grade the assignments, because you’re just learning how to curate and present data (if you’re not already a scientist and just want to learn how to do this in Python).

DISCUSSION FORUMS: You won’t find answers or discussions in the discussion forum. There are only posts asking to please grade a student’s assignment because it is urgent because the subscription is ending soon (see above).

SUMMARY: If you need the certificate for Applied Data Science in Python, you probably must take this course. Otherwise I strongly encourage you to skip it and find other (better) resources to learn plotting in Python.

創建者 Nicolau G

May 25, 2019

I registered to the whole specialization mainly to take the first three courses. I got stuck in the second (this one about data visualisation) because a file I need for assignment in week 2 is missing. There are many complaints in the forum from those affected (the file is supposed to contain weather data around your location, and it seems to affect some of us in Europe). I jumped to course 3.

I'm very happy with courses 1 and 3, but this one is extremely bad, with poor material, confusing data and wrong instructions. On top of that, there is no one in the forums to answer questions or fix the errors. I finished courses 1 and 2 (those are great, really) and I will drop this specialisation right now leaving this course unfinished.

創建者 Eklavya s

Aug 05, 2018

This course makes you give up on data science and MOOCs.

Seriously, the content is poorly presented he keeps on speaking , telling 2-3 lines about a function and so on.

I highly recommend stay away from this pathetic specialization.

創建者 Naveen P

May 15, 2018

Well to be honest youtube videos are quite informative than this. Opting out from this course.

創建者 Yifei Z

Oct 07, 2017

I feel like this course is bad. Since it basically tell us to search google for everything.

創建者 Jakob P

Jun 19, 2017

Too few lectures with detailed explanations of the functionality of matplotlib.

創建者 Javier P S

Mar 08, 2018

A course where you practice your googling capabilities. It could be improved.

創建者 Siddhartha B

Nov 03, 2017

Loved the course! This course teaches you details about matplotlib and enables you to produce beautiful and accurate graphs.. Assignments are challanging, and helps to build a solid foundation.

創建者 Matheus G S d L

Jun 28, 2017

Good course to learned matplotlib and other Graphs libraries, but the course goes further than Python and also encourages the studies to create more meaningful and beautiful Graphic views.

創建者 Josselin G

Sep 13, 2019

Pretty good course the material is good.

Offers good coverage and proposes some interesting problems.

Pairs grading works pretty well.

創建者 Somaiya J G

Nov 06, 2018

Really amazing course, Christopher Brooks salute man, you explained every details in good way that one can easily understand.

創建者 Ahmad H S

Jul 28, 2019

Amazing source

創建者 Ron M

Feb 09, 2018

Ideally, would be 2 1/2 stars if that was possible. Again, like Course 1 in the series, the time required is VERY underestimated, especially since the course is little more than a series of exercises that require extensive external research to learn how to complete. The instructor seems more interested in this subject matter than the first course, but the discussions are such high-level overview, much pouring through Stack Overflow is needed just to learn the topics. The best pieces, as in Course 1, are the extra reading that one might never otherwise be pointed towards, but other than those, a $10 web course that simply gave exercises and pointed to Google searches and Stack Overflow to learn the detailed material would accomplish 80+% of what this course does. And many find the assignments confusing which adds time or results in the wrong work product (some of that I believe is especially true for non-native language speakers) - lots of comments to that effect in the forums. Students grade each others' work product, and it is clear from doing so there are many interpretations of the exercises.

And, some students doing the evaluation of others are clearly not qualified to do so - if one does not really understand basic Python or statistics, they should not be indicating the calculations are wrong... And with three reviews required - it seems the grader uses the lowest grade of the three. If 2 reviewers give full marks, and another gives a 0, the 0 is what is recorded. This is especially annoying in tandem with the lack of value in the instruction - just getting some assignments from off the web and forcing yourself to do them would be far more satisfying.

I don't see continuing with the Michigan courses beyond this point - there are better options.

創建者 Andy F

Sep 20, 2019

The lectures really need to flesh things out more, they too often feel too fleeting and leave more than they probably should to searching other resources. Questions for the final piece clearly haven't changed in at least two years and lack clarity as to what should be done

創建者 Kareem H

Dec 08, 2019

Plotting concepts need more deep explanation or more practice, generally the provided information wasn't meet the course's level "in my opinion."

創建者 Mariusz K

Nov 10, 2019

Too little of expounding and too much of searching the net by oneself. Too few examples. It is a self-learning but what's the Course for then?

Plus the assignments. I didn't like the peer evaluation idea, just as evaluating the others, because I don't have time for this and that's not what I came for.

First - what's the motivation of random viewers to fairly and thoroughly evaluate my work? Plus it's hard to finish the course quicker for this reason, because one has to wait a couple of days to get a grade. That's the reason I resigned from waiting for the assignments evaluations for next weeks assignments and in consequence for the certificate.

創建者 Shuang S

Aug 15, 2019

It taught some visualization that is not use very often and sometimes I feel I couldn't catch up the knowledge, so if you are a beginner, skip this class first.

創建者 Yaron K

Sep 21, 2017

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.

創建者 Feng H

Aug 16, 2019

Lectures are not detailed enough and speaks too fast, assignments too difficult. If I have to do my own study 90% of the time why do I need to pay tuition?

創建者 Mack S

Jul 01, 2019

There are some rubbish assignments in this course which involve searching the web for badly made info graphics.

創建者 Sophia C

Oct 14, 2018

Not very well done

創建者 Yue Z

Apr 08, 2017

really bad!

創建者 Leonid I

Sep 17, 2018

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: ...

創建者 Aino J

Feb 02, 2020

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!).

創建者 Ilya R

Jul 25, 2017

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.