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.
- 5 stars73.92%
- 4 stars19.95%
- 3 stars4.14%
- 2 stars1.02%
- 1 star0.95%
來自APPLIED SOCIAL NETWORK ANALYSIS IN PYTHON的熱門評論
Really enjoyed the mathematical component of this course. It was fun to see how you could connect the graph theoretical components to the machine learning concepts from earlier courses.
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.
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.
Great content but assignment / auto grader sometimes difficult to deal with. In particular, errors not clearly described. Much time wasted due to wrong package version, etc. etc.