Chevron Left
返回到 Applied Plotting, Charting & Data Representation in Python

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
5,707 个评分
971 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.

筛选依据:

851 - Applied Plotting, Charting & Data Representation in Python 的 875 个评论(共 955 个)

创建者 Ting W

Oct 1, 2019

very useful contents

创建者 AYUSH S

Oct 11, 2018

Excellent course...

创建者 Mallikarjuna R Y

Apr 25, 2020

good presentation

创建者 Saikiran y

Jun 12, 2020

it's good course

创建者 Oleh Z

Jan 31, 2018

Good introductor

创建者 David A d A S

Jun 25, 2017

Great course !

创建者 Suraj P

Jul 13, 2020

Great Course!

创建者 SATYAM G

May 30, 2020

Great course!

创建者 Qian H

Jun 28, 2017

Nice Tutorial

创建者 Rodrigo Z

May 2, 2018

Nice course!

创建者 Baggam S

Jul 7, 2020

Nice course

创建者 Shubhank R

May 27, 2020

nice course

创建者 Rohan K

Dec 1, 2019

Good Course

创建者 John P

Apr 2, 2018

Thank you!

创建者 Anant K

Aug 19, 2020

GOOD one

创建者 Tất T V

Oct 3, 2017

useful

创建者 SHAHUL E

Mar 2, 2018

heavy

创建者 NIKHIL C

Jun 6, 2021

nice

创建者 ERAGANABOINA S

Oct 31, 2020

good

创建者 MOHITH N

Jun 16, 2020

good

创建者 Eklavya J

Mar 21, 2020

na

创建者 Vladimir I

Aug 23, 2017

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.

创建者 VenusW

Mar 31, 2017

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.

创建者 Benny P

Oct 2, 2017

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.

创建者 Philipp A R

Mar 21, 2020

I liked the first course in this specialization more. As in the first one, the assignments require you to search StackOverflow, documentations and the discussion forums; videos are nice, but you won't learn a lot from them. Peer review is a double-edged sword. Some reviewers will give quite elaborate feedback, others do not put a lot of effort into their reviews. Peer-reviewing others can be quite annoying as often you have to wait several hours for submissions of other learners. Not to mention the quite large amount of learners who hand in plagiarized code (please look out for these cases if you participate in this course).