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

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This was a very good course and I feel like I really learned a lot. It starts out with a module on how data visualizations can mislead (intentionally or unintentionally!) and principles of good data visualization. I found it very informative. The instructor used good examples and also walks through the tweaking of a plot to follow the principles of good data visualization using matplotlib.

Subsequent modules give you lots of experience with the matplotlib library and the process of transforming data and making visualizations. There is often a moderate amount of flexibility in the assignments, giving the learner the opportunity to take as much as they want from the experience. You get back what you put in. Multiple assignments have you go out in the real world to look for charts and data to use in your assignments, which give you a very good opportunity to apply what you've learned, moreso than in constrained assignments where data is given to you.

My only complaint is the peer grading system, but I wish I could only dock half a star for it. I think it was actually implemented very well for a peer-grading system in a MOOC, and I even think that peer grading was the appropriate choice of grading system for this course, but of course it is going to have its downfalls. You are graded on a rubric provided by the instructor which mostly awards points just for completion of various parts of the assignment. This is how it has to be, as judging the quality of a visualization is very subjective (and dependent on the grader's comprehension of the course content), and students' grades shouldn't suffer because one of their graders doesn't like the colour red or because they don't understand the principles we are supposed to be applying.

As an aside, there are many complaints that students are directed to read the documentation of a given library, google, search stackoverflow, etc. A course cannot teach you everything you need to know about a library. You are going to have to look things up yourself, especially when debugging. This is the reality coding.

I liked this course. It was very practical. The reading material was very interesting and valuable. Project had some flaws in terms of formulation of questions but they really pushed me to search and look for help. That in my opinion was actually good and I think that after each of these courses there is still a lot we need to learn ourselves. The course materials was laid out pretty well. Sometimes I had a feeling that things were a bit rushed (like introducing seaborn at the very end) . There is a lot of software terminology involved in the beginning when explaining matplotlib notebook and it was hard to follow that part. My biggest complaint is the Coursera's online Jupyter notebook. I got very frustrated with it. Even if you save your work frequently it often happens that the connection does not work or isn't strong and after you close your assignment you edits are lost!!! My advice, download all necessary links and work in your own Jupiter notebook and then upload the assignment.

I think the greatest strength of this course is that at the end of this you will be very confident in writing code for creating data visualizations. However the expected timelines for completion of assignments are completely above expectations. Unless you already have experience with matplotlib and its API, it is difficult for students to cope up.

But on the other hand searching for stuff online on stackoverflow and matplotlib also was a really enlightening experience and teaches you are not the only one having these kind of problems. I would have loved to see more video explanations on ScalarMappable objects which was a huge part of the assignment but was not covered in the video lectures. Also would have loved to see more concepts explored about sea born package.

Great course!A couple things keep it from being 5 stars. 1 - the content comes a little fast without enough reinforcement. The balance here isn't perfectly struck as it is in the 3rd course of the specialization - Machine Learning. Although the content of week 1 is good, I think quite a bit of it should be optional and substituted with more coding exercises and longer assignments in the subsequent weeks. Week 2 has a bit too much esoterica for an intro course, and I'd rather have week 3 and 4 concepts reinforced more instead. At the end of this course, and after a few days, I'm confident I can look back and make any kind of plot I want. A minor quip - much of the code for the course will throw deprecation warnings in the latest versions of matplotlib.

Course materials (videos, jupyter notebooks were very useful. All the code that was shared through the lectures pointed students in the right direction. Taught useful concepts. The time estimates stated in the syllabus are grossly understated. I didn't use the forums at all, as I like to learn independently, so I probably found some creative solutions, but I came to the course with substantial programming experience in python & still found that I needed much more time to complete the assignments than what was stated.

Grading was a bit easy too, as all the assignments were peer graded. Good course overall.

It was good to know that visualization is possible in Python, but I would probably not use them because there are so many other tools that make it a lot easier to create interactive visualizations in a much shorter time. While I do not disagree with Albert Cairo, I do not think he is the only person we should read about nor the best visualization expert. Assignments were good, but peer reviewing was not always great; I wish to not be graded by peers who do not follow instructions and give poor grade because they do not understand the course content and/or just out of spite.

Great course overall. The assignments we're pretty challenging, making you search for most things as only part of what's covered in the course is actually asked in them. This isn't necessarily bad as it prepares you for learning how to solve problems. My only criticism is that, since they are all peer graded (by other peers who probably don't know any more than you), you can't really tell if your progress is accurate. Having the professional answer (at least once the course is finished, to not allow any cheating) would have been very instructional.

The course gave a really good overview of design principles for displaying information, something worth learning even if you aren't going into Data Science.

Really good course. Its a good mix of active and passive learning. Well formulated lectures, and interesting and challenging assignments - at least you can make them as challenging as you like..

Initially sceptical about the peer review system for this kind of learning, but actually received good and clear feedback, and was able to see the learning and approaches of other people.

The course itself is great. For those complaining it's not detailed enough I think data scientists need to learn how to search for code and adapt it for their own purposes. If it's too hard to achieve in this course, probably start from a easier course or this is not the right career for you.

However the grading system is broken. Cheaters just submit empty/irrelevant answers and trying to get 3 other people to give them good score on those empty answers. All the 3 reviews I've done for assignment 4 are such cases.

Great course to get one very comfortable with the matplotlib library without going too deep under the hood. I wish there was a bit more focus on the various advantages of using other libraries such as seaborn and Bokeh, but given the course's length that would have been hard to squeeze in.

I am hoping there will be a second part to this course, focusing on real-world data visualization problems and converting graphics, with newly acquired data science skills from other courses in the series, into a full portfolio.

It gives you a nice overview knowledge of the concepts. The instructional videos are too brief in my opinion. Some concepts do not have enough instruction to learn unless you have a computer science background and some assignments are frustrating because they do not give enough of the tools to even understand posts on stack overflow. That being said, by the end of the course I was able to make a very pretty graph and learned some useful academic concepts on plotting style in addition to the programming knowledge.

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!