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Avis et commentaires pour d'étudiants pour Applied Social Network Analysis in Python par Université du Michigan

4.7
étoiles
2,529 évaluations
425 avis

À propos du cours

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....

Meilleurs avis

NK
2 mai 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
23 sept. 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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151 - 175 sur 416 Avis pour Applied Social Network Analysis in Python

par Mischa L

6 janv. 2018

Great course. Very good homework assignments, but somewhat on easy side

par Rui

11 oct. 2017

very good introductory course for social network analysis using Python.

par Diego F G L

30 mars 2021

Great course and and great contents. I really enjoyed the assignments.

par Dirisala S

22 juil. 2019

The have lot of stuff to learn. It will definitely enhance your skill.

par Dibyendu C

19 oct. 2018

Well structured and quality lecture content with excellent assignments

par Nikhil N

18 juil. 2021

W​onderful course with very detailed explanations!!! Simply wonderful

par Liran Y

20 mai 2018

Interesting and fun. Daniel's lecturing style is clear and enjoyable.

par Chiau H L

4 avr. 2019

Awesome course!!! Helped me a lot to get started with graph analysis

par Keqi L

14 avr. 2019

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

par Kai H

8 nov. 2018

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

par Tatek E

23 mars 2020

Excellent presentation, exercise and reading materials. Thank you

par wenzhu z

22 févr. 2018

very clear logic, and will always wrap up at the end of the class

par 杨志陶

17 mai 2020

A practical way to learn social network analysis. Great course!

par Renzo B

23 sept. 2019

I learned a lot of things that I can apply to my line of work.

par charles l

4 févr. 2019

A completely new area for me, and a really fascinating course.

par Yee F

1 juil. 2021

Course is much easier to understand that applied text mining.

par Haris P D

31 janv. 2020

One of the most awesome course that I have taken on Coursera!

par Wai Y P S

22 juin 2021

Thanks you so much University of Michigan for Great course

par Marco Z

22 avr. 2020

Very interesting , a new point of view for future analysis!

par Israel D D G

22 août 2020

Excellent course, good technical and teoretical knowledge.

par LEE D D

5 nov. 2017

Excellent! It was one of the great assignments I ever had!

par Manuel T

30 janv. 2018

good stuff. Assignments are a little bit too easy though.

par Jiahui B

28 nov. 2017

Very useful course. It helps me finish my course project.

par Ruihua G

8 juil. 2019

this course provided a overview of the network analysis.

par Jiefei W

11 avr. 2020

Practiced with what was covered in the 1th~3rd courses.