Chevron Left
Applied Social Network Analysis in Python(으)로 돌아가기

미시건 대학교의 Applied Social Network Analysis in Python 학습자 리뷰 및 피드백

1,998개의 평가
320개의 리뷰

강좌 소개

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....

최상위 리뷰


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.


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.

필터링 기준:

Applied Social Network Analysis in Python의 310개 리뷰 중 101~125

교육 기관: Мирзабекян А В

Aug 09, 2018

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

교육 기관: Reed R

Mar 02, 2018

Well taught and in a field which is not covered by many other data science curricula

교육 기관: Rajesh R

Feb 08, 2018

Excellent course to understand various networking principles and analyszng the same.

교육 기관: Carlos S

Oct 08, 2017

Great introduction to network theory and applications using Python Networkx library.

교육 기관: Ricardo J M S

Jun 01, 2020

It is the best course of the 5 courses of the specialitation. I strongly recommend

교육 기관: Vighneshbalaji

Apr 28, 2020

Very Useful. I learned a lot. Thanks to Coursera and University of Michigan

교육 기관: Chanaka S

Aug 01, 2020

Lecture is God To Me The Person Who has Good Knowledge then easy to study

교육 기관: Amila R

Sep 30, 2019

Good starting point for those who want ro learn social network analysis.

교육 기관: Roberto L L

Mar 26, 2019

It was a wonderful course, linked network's models and machine learning.

교육 기관: 高宇

Dec 02, 2018

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

교육 기관: Thaweedet

Aug 16, 2018

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

교육 기관: Mykhailo L

Jan 06, 2018

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

교육 기관: Rui

Oct 11, 2017

very good introductory course for social network analysis using Python.

교육 기관: Dirisala S

Jul 23, 2019

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

교육 기관: Dibyendu C

Oct 19, 2018

Well structured and quality lecture content with excellent assignments

교육 기관: Liran Y

May 20, 2018

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

교육 기관: Lee C H

Apr 04, 2019

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

교육 기관: Keqi L

Apr 14, 2019

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

교육 기관: Kai H

Nov 08, 2018

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

교육 기관: Tatek K

Mar 23, 2020

Excellent presentation, exercise and reading materials. Thank you

교육 기관: wenzhu z

Feb 22, 2018

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

교육 기관: 杨志陶

May 17, 2020

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

교육 기관: Renzo B

Sep 24, 2019

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

교육 기관: charles l

Feb 04, 2019

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

교육 기관: Haris P D

Jan 31, 2020

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