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

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

4.7
별점
2,591개의 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 431개 리뷰 중 426~431

교육 기관: DW J

2018년 4월 6일

교육 기관: Afreen F

2021년 2월 7일

교육 기관: MENAGE

2021년 2월 22일

교육 기관: Natasha D

2019년 12월 5일

교육 기관: Moustafa S

2020년 8월 19일

교육 기관: Sonam A

2019년 12월 18일