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學生對 密歇根大学 提供的 Applied Social Network Analysis in Python 的評價和反饋

4.6
1,473 個評分
238 個審閱

課程概述

This course will introduce the learner to network analysis through tutorials using the NetworkX library. The course begins with an understanding of what network analysis is and motivations for why we might model phenomena as networks. The second week introduces the concept of connectivity and network robustness. The third week will explore ways of measuring the importance or centrality of a node in a network. The final week will explore the evolution of networks over time and cover models of network generation and the link prediction problem. This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python....

熱門審閱

NK

May 03, 2019

This course is a excellent introduction to social network analysis. Learnt a lot about how social network works. Anyone learning Machine Learning and AI should definitely take this course. It's good.

JL

Sep 24, 2018

It was an easy introductory course that is well structured and well explained. Took me roughly a weekend and I thoroughly enjoyed it. Hope the professor follows up with more advanced material.

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101 - Applied Social Network Analysis in Python 的 125 個評論(共 230 個)

創建者 Mohammad H

Oct 26, 2018

the course will teach basic of SNA so clear

創建者 Мирзабекян А В

Aug 09, 2018

One of the most interesting and challenging courses in specialization, in my opinion.

創建者 Thaweedet

Aug 16, 2018

Great, You will to learn how to develop feature for social network data

創建者 Tam

Aug 18, 2018

Wow

創建者 Kai H

Nov 08, 2018

Good course, may be better if offer more practice and application.

創建者 Armand L

Oct 28, 2018

Hard but instructive course !

創建者 Niranjan H

Nov 14, 2018

As a course by itself or as part of the specialization, either way (it helps to have completed the first two in the set), it is a great course.

It provides a very good high level picture of what is needed in ones toolbox.

Essentials: networkx, matplotlib and to a lesser extent pandas.

創建者 Ayon B

Nov 20, 2018

Nice course. Well presented.

創建者 To P H

Nov 11, 2018

Great course

創建者 David M

Oct 09, 2018

Excellent course and professor!

創建者 Jun W

Aug 19, 2018

A very interesting course, beyond my expectation.

創建者 高宇

Dec 02, 2018

Very Nice Coursera! It lead me to reknow the relations among the worrld.

創建者 Magdiel B d N A

May 12, 2019

ok

創建者 Ana M L M

Apr 15, 2019

Excellent course.

創建者 Keqi L

Apr 14, 2019

Interesting slides and knowledge. e.g. Page rank is super cool!!!!

創建者 Nitin k

May 03, 2019

This course is a excellent introduction to social network analysis. Learnt a lot about how social network works. Anyone learning Machine Learning and AI should definitely take this course. It's good.

創建者 Stephen

May 03, 2019

Good Course

創建者 Keary P

Apr 21, 2019

Nice way to end the 5 course specialization. Brought together several machine learning and python skills that I learned in the previous courses. Instructor does a great job introducing new concepts with high level theory and intuitive examples. Course slides were superb and can serve as future reference material.

創建者 Parul S

Apr 20, 2019

good

創建者 Santiago D D

Apr 22, 2019

This class was an excellent introduction to network analysis, where concepts, metrics and purpose of application where provided in a clear and digestible manners. The instructor made the class very livable with topics that might have been too dry under different circumstances.

創建者 John H E O

Jun 16, 2019

Amazing course!!!

創建者 Shadi A

Jun 23, 2019

Great course

創建者 Vincenzo T

May 16, 2019

Very good course! I was afraid going into this after going the rather bad "Text Mining". However, it was super fun, well done and informative!

創建者 Henri

May 19, 2019

Great intro to networks; last assignment is challenging but is a good opportunity to put everything together (python+ML+Network).

創建者 Carl W

May 30, 2019

Month 5 was very nice. I enjoy networks and appreciate your presentation of the material. I would also like to thank all of those who worked to bring the specialization to life. This includes the lecturers, grad students, and mentors who devoted time to the class.

THANKS!!