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.
Ce cours fait partie de la Spécialisation Science des données appliquée avec Python
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Compétences que vous acquerrez
- 5 stars73,92 %
- 4 stars19,99 %
- 3 stars4,11 %
- 2 stars1,02 %
- 1 star0,94 %
Meilleurs avis pour 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.
The material and assignments were great and well aligned. The autograder for the Jupyter Notebooks was finicky at best and resulted in lots of time wasted getting formatting correct.
Basic yet informative course. The videos are well paced and the presenter is instructive. The exercises are well made, putting more enphasis on what was learned in the videos.
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.
À propos du Spécialisation Science des données appliquée avec Python
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