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 forma parte de Programa especializado: Ciencias de los Datos Aplicada con Python
Acerca de este Curso
Qué aprenderás
Represent and manipulate networked data using the NetworkX library
Analyze the connectivity of a network
Measure the importance or centrality of a node in a network
Predict the evolution of networks over time
Habilidades que obtendrás
- Graph Theory
- Network Analysis
- Python Programming
- Social Network Analysis
Ofrecido por
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Programa - Qué aprenderás en este curso
Why Study Networks and Basics on NetworkX
Network Connectivity
Influence Measures and Network Centralization
Network Evolution
Reseñas
- 5 stars73,92 %
- 4 stars19,99 %
- 3 stars4,11 %
- 2 stars1,02 %
- 1 star0,94 %
Principales reseñas sobre APPLIED SOCIAL NETWORK ANALYSIS IN PYTHON
Very helpful courses. I was able to review and got much better at some things I already knew like data visualization and was able to explore some new areas like network analysis.
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.
This course contains many important concepts of Graph Theory and Network Analysis. The explanation is clear and neat. Also, the assignments are fun and comprehensible.
Very good class.
The lecturer is amazing!! The quizzes help you understand the concepts. The assignments are a little basic though.
Overall you learn a great deal.
Acerca de Programa especializado: Ciencias de los Datos Aplicada con Python

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