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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

4.5
4,953件の評価
815件のレビュー

コースについて

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....

人気のレビュー

OK

Jun 27, 2020

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 ..

RM

May 14, 2020

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.

フィルター:

Applied Plotting, Charting & Data Representation in Python: 51 - 75 / 801 レビュー

by 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."

by Markus K

Apr 20, 2020

I didnt like the peer review

by 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.

by Qiang L

Mar 27, 2020

The construction of this course is fine, but content is really bad. Instructor could not give detailed introduction in matplotlib. So basically you need to learn everything by yourself. On the other hand, there is huge gap between course and assignment. I would say that you should have at least intermediate level of matplotlib before you take this course, which strongly against the principal of this course. I suggest instructor giving a more general idea first and gradually providing more specific application and harder examples.

by Kumar I

May 25, 2017

Compared to the first course in this series, I found this one not so challenging. The final project was very loose (I understand that the instructors wanted to give the feel of a real research). The first assignment was very superficial. As much as Cairo's principles are important, I feel devoting an entire assignment to that is justified. The second and third were relatively straight-forward, but that was perhaps the saving grace.

Wish the course spent time in dwelling on complex visualizations.

by Liam L

May 11, 2020

Too little teaching and too much googling. The questions are poorly defined and you end up using Stacker and the discussion forums to really understand what they are asking. I put the time in and got the answers but would have liked them to explain whats going on a bit more rather and give a bit of guidance. Also very expensive, compared to other Pandas and Matplotlib courses.

by 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.

by Darien M

Nov 21, 2019

This course is anbalagous to taking a creative writing course, but all lessons are on vocabulary and grammar. Once again the lectures are unhelpful. The discussion forum in this course does not provide much help (unlike the first course in the sequence). I suppose they are applying the graduate school mentality to teaching: you want to learn it, figure it out. I myself am definitely not at that level right now.

The assignments are challenging, and you will learn from them, but you won't learn deeply. It seems all very superficial. Just look things up to get them done. Type in any question you have and a solution will certainly appear on SO. Why not give students the tools necessary to solve challenging problems on their own (like in Python for Everybody and Python 3 Programming)?

Professor Brooks is clearly passionate about programming and is very accomplished/intelligent. Unfortunately the teaching in this course is of low quality.

by 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.

by Rohan G

Dec 30, 2019

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.

by Abhimanyu S

Apr 11, 2020

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.

by Jorge S R

Jun 06, 2020

No real teaching. Just skimming fast through a table of contents. It's better to read a book on the subject than taking the course.

by Harshad H

Jun 19, 2019

Too slow grading and a very inefficient process.

by Sophia C

Oct 14, 2018

Not very well done

by Yue Z

Apr 08, 2017

really bad!

by 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: https://wiki.archlinux.org/index.php/Arch_Linux#Principles ...

by 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!).

by 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.

by Vinayak

Jul 05, 2019

This course helped me understand the basics of Data Visualization unlike any other internet resourses.

It starts with one module completely dedicated to the theory behind data visualization and how to present data in a genuinely insightful manner and then delves into matplotlib and eventually seaborn to implement the same.

I enjoyed Dr. Brook's teaching and the exercises. With a solid pedagogy, challenging exercises (the last one is especially fun and gives you a feel for the subject) and insightful lectures it's a great course for people looking to gain knowledge about basics of python data visualization.

by Han C

Aug 28, 2017

I really enjoyed this course. As a python novice I had to spend lots of times in googling commands for arguments, options, examples. Well I see many peoples are only relying on course materials but the considering this course as a motivator. I often felt frustrations and pressure, but not tried to be defeated by myself. Hope you guys find your own way to get it done. I still see lots of thing to learn, but I am not worried. This is only the beginning. Course is not a magic pill, it just gives a start point. As a start point, this is really nice cource to take.

by Hari G S

Sep 12, 2019

This is an excellent course on visualization in Python. The videos are brief and covers just the right amount of information. Reading resources and assignments are carefully chosen and perfectly complements what we've learned in the lectures. Assignments, most of the time, require us to read the matplotlib documentation but is easily understandable once gone through the lectures. Assignments are not very easy/simple, but completing it with real data and help from documentation, stack overflow and discussion forums is deeply satisfying.

by David C

Jul 12, 2017

This was an interesting course. The professor was excellent and the practical exercises, in particular, were beneficial in learning the material. My only complaint would be that a lot more time in the exercises was spent formatting and manipulating Pandas dataframes than applying the matplotlib libraries to produce charts and graphs of the data. I would have preferred to spend more time experimenting and using the graphics libraries and less on trying to manipulate data to get it into formats acceptable for grading.

by Sabu J

Oct 17, 2017

U-M and Coursera together brought a great and very interesting course. Great that the learners get exposed to various aspects of DS, be it the concepts , trends etc. A great platform for participants to learn together and experiment. Course introduces what is relevant in the industry and provide multiple opportunities to apply the learning. On top that it is laced with interesting challenges, not a cake-walk -:)

My sincere thanks to U-M, Coursera, teaching staff and all who made this happen

by Varun S T

Jul 24, 2020

Great Course. The best part about it is that you are forced to dig deep into various resources and work hard on your assignments, which helps you to apply everything you will learn from the lectures. Extremely practical and totally hands-on. Looking foward to the remaining courses in the specialization. I feel so much more confident about not only creating amazing visualizations but also about acquiring datasets and preparing them, which we learnt in the previous course.

by Eric G

Feb 20, 2019

You are going to learn by doing, less then getting a deep lecture of Matplotlib. Yes you will learn it quickly, but the lecture videos are only about 15-30 minutes a week, while the projects will take you a few hours to complete (With the last two taking significantly more time if you want them to). I was a little disappointed that I didn't get I 100% clear picture on how to use Matplotlib and Seaborn, but I do feel like I gained comfort, so it was worth taking!