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.
Este curso faz parte do Programa de cursos integrados Ciência de dados aplicada com Python
Informações sobre o curso
Habilidades que você terá
- 5 stars73,91%
- 4 stars19,96%
- 3 stars4,13%
- 2 stars1,02%
- 1 star0,94%
Principais avaliações do APPLIED SOCIAL NETWORK ANALYSIS IN PYTHON
Course was ok, the assignments are not too difficult. I wish the course would provided more insights and discussions of the presented metrics of centrality though.
I loved this course. It was well taught and had excellent problem sets and quizzes to internalize the learning. The material is very relevant to the market today. I highly recommend it.
Excellent tour through the basic terminology and key metrics of Graphs, with a lot of help from the networkX library that simplifies many, otherwise tough, tasks, calculations and processes.
Great class for an introduction to networks.I didn't give it 5 stars because it didn't give me enough information to apply the concepts learned to real life projects.
Sobre Programa de cursos integrados Ciência de dados aplicada com Python
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