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学生对 密歇根大学 提供的 Applied Plotting, Charting & Data Representation in Python 的评价和反馈

4.5
5,752 个评分
978 条评论

课程概述

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....

热门审阅

OK
Jun 26, 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 13, 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.

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801 - Applied Plotting, Charting & Data Representation in Python 的 825 个评论(共 963 个)

创建者 Richard H

Jul 28, 2017

Great course. But it is very broad (and challenging) for a four week course.

创建者 Richard B

Sep 5, 2018

Good background - some of the presentations (such as on seaborn) are rushed

创建者 Jiangzhou F

Jun 8, 2020

Only thing I don't like is the peer review part. The rest is pretty good.

创建者 M M

Mar 17, 2017

I found the lectures interesting and thorough yet short and to the point.

创建者 Amine D

Oct 13, 2019

Really good , you need to read documentation and look at your peers work

创建者 Jose E R

Sep 2, 2019

Excellent course. I learned plotting and data representation in Python

创建者 Jeffrey D B

Oct 16, 2018

Class was OK, would have liked some discussion of Bokeh and/or Altair.

创建者 Didac B

Oct 21, 2020

Really useful course to master the matplotlib visualisation pacakge

创建者 Haldankar S N

May 6, 2020

week 3 is slightly faster as compared to other weeks of the course

创建者 Kishan D

Apr 20, 2020

The versions of pandas and numpy used in this course are outdated.

创建者 Jialie ( Y

Dec 29, 2017

It would be greater, if teacher can cover more API in the lecture.

创建者 Srinivas R

Oct 9, 2017

a quick but sparse introduction to plotting and charting in python

创建者 Abir H R

May 26, 2020

Should be updated with the updated pandas and matplotlib library

创建者 Fatemeh M

Jul 24, 2018

That was great !

Thanks all the instructors and their colleagues!

创建者 Fabian R

Jun 30, 2017

Very good overview. Could have been a little bit more material.

创建者 Manoj K K M

Jun 3, 2018

Good course to get a feel on plotting, what is chart junk etc.

创建者 Iván C S R

Feb 7, 2019

Really helpful to improve skillset of visual communication.

创建者 Alexander C

Jul 23, 2017

Very good course. Requires a lot of work but well worth it.

创建者 Lan Y

May 27, 2020

Don't like to review the assignment part. Others are good

创建者 Shuyi Y

Apr 2, 2017

Great training in matplotlib and the plotting experience.

创建者 VP P

Jun 7, 2021

This was very strenuous. There are better graphing tools

创建者 布鲁斯然

Jan 15, 2018

I learned something that helps with my work. Thank you.

创建者 Qiyu L

Feb 5, 2018

Somewhat easier compared to other courses in the pack.

创建者 ALISON J D

Jan 15, 2018

A fun course, well-taught and with some lovely charts.

创建者 Nuno d S

Jun 29, 2017

Very nice course, however It could cover more topics.