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Applied Social Network Analysis in Python(으)로 돌아가기

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

4.7
별점
2,570개의 평가

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

NK

2019년 5월 2일

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

2018년 9월 23일

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 427개 리뷰 중 151~175

교육 기관: Vighneshbalaji

2020년 4월 28일

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

교육 기관: Chanaka S

2020년 8월 1일

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

교육 기관: Amila R

2019년 9월 30일

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

교육 기관: Roberto L L

2019년 3월 26일

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

교육 기관: 高宇

2018년 12월 2일

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

교육 기관: Thaweedet

2018년 8월 15일

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

교육 기관: Mischa L

2018년 1월 6일

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

교육 기관: Rui

2017년 10월 11일

very good introductory course for social network analysis using Python.

교육 기관: Diego F G L

2021년 3월 30일

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

교육 기관: Dirisala S

2019년 7월 22일

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

교육 기관: Dibyendu C

2018년 10월 19일

Well structured and quality lecture content with excellent assignments

교육 기관: Nikhil N

2021년 7월 18일

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

교육 기관: Liran Y

2018년 5월 20일

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

교육 기관: Namrata T

2022년 3월 24일

Terrific Course. Learned a lot in graph theory and network analysis.

교육 기관: Chiau H L

2019년 4월 4일

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

교육 기관: BCN R

2022년 7월 30일

great course and specialization! quality of the contents is superb

교육 기관: Keqi L

2019년 4월 14일

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

교육 기관: Kai H

2018년 11월 8일

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

교육 기관: Tatek E

2020년 3월 23일

Excellent presentation, exercise and reading materials. Thank you

교육 기관: wenzhu z

2018년 2월 22일

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

교육 기관: 杨志陶

2020년 5월 17일

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

교육 기관: Renzo B

2019년 9월 23일

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

교육 기관: Charles L

2019년 2월 4일

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

교육 기관: Yee F

2021년 7월 1일

Course is much easier to understand that applied text mining.

교육 기관: Haris P D

2020년 1월 31일

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