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Applied Social Network Analysis in Python に戻る

ミシガン大学(University of Michigan) による Applied Social Network Analysis in Python の受講者のレビューおよびフィードバック

4.7
2,589件の評価

コースについて

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....

人気のレビュー

NK

2019年5月2日

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

2018年9月23日

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.

フィルター:

Applied Social Network Analysis in Python: 426 - 431 / 431 レビュー

by DW J

2018年4月6日

by Afreen F

2021年2月7日

by MENAGE

2021年2月22日

by Natasha D

2019年12月5日

by Moustafa S

2020年8月19日

by Sonam A

2019年12月18日