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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

4.5
5,507 個評分
927 條評論

課程概述

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....

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OK
2020年6月26日

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
2020年5月13日

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.

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801 - Applied Plotting, Charting & Data Representation in Python 的 825 個評論(共 913 個)

創建者 chris l

2020年1月4日

Practical useful course

創建者 Ting W

2019年10月1日

very useful contents

創建者 AYUSH S

2018年10月11日

Excellent course...

創建者 Mallikarjuna R Y

2020年4月25日

good presentation

創建者 Saikiran y

2020年6月12日

it's good course

創建者 Oleh Z

2018年1月31日

Good introductor

創建者 David A d A S

2017年6月25日

Great course !

創建者 Suraj P

2020年7月13日

Great Course!

創建者 SATYAM G

2020年5月30日

Great course!

創建者 Qian H

2017年6月28日

Nice Tutorial

創建者 Rodrigo Z

2018年5月2日

Nice course!

創建者 Baggam S

2020年7月7日

Nice course

創建者 Shubhank R

2020年5月27日

nice course

創建者 Rohan K

2019年12月1日

Good Course

創建者 John P

2018年4月2日

Thank you!

創建者 Anant K

2020年8月19日

GOOD one

創建者 Tất T V

2017年10月3日

useful

創建者 SHAHUL E

2018年3月2日

heavy

創建者 ERAGANABOINA S

2020年10月31日

good

創建者 MOHITH N

2020年6月16日

good

創建者 Eklavya J

2020年3月21日

na

創建者 Vladimir I

2017年8月23日

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: https://github.com/vdyashin/EarthquakesInAsia. 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.

創建者 VenusW

2017年3月31日

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.

創建者 Benny P

2017年10月2日

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.

創建者 Guo X W

2020年6月4日

This course provides an overview to the matplotlib and seaborn library and guides learners to create useful visualisations with Python. My main issue with the course is that the various topics are not covered in sufficient detail. Successful completion of the assignments required far too much independent learning on commands that were not covered in the course (particularly for Assignment 3).

The course also covered Principles of Information Visualisation in great detail. I thought that was refreshing and useful. However, I felt that the portion on Matplotlib Architecture could be explained in more layman and palatable terms. In addition, it would have been more meaningful if the course drew more on actual real-world datasets instead of histograms generated from a random normal distribution.