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 is part of the Applied Data Science with Python Specialization
About this Course
What you will learn
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
Skills you will gain
- Graph Theory
- Network Analysis
- Python Programming
- Social Network Analysis
Start working towards your Master's degree
Syllabus - What you will learn from this course
Why Study Networks and Basics on NetworkX
Influence Measures and Network Centralization
- 5 stars73.80%
- 4 stars20.09%
- 3 stars4.14%
- 2 stars1.01%
- 1 star0.94%
TOP REVIEWS FROM APPLIED SOCIAL NETWORK ANALYSIS IN PYTHON
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.
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.
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.
Lectures are very well-designed. Especially, the assignment of week 4 is too good, that give me an overview of how we can apply machine learning in network analysis.
About the Applied Data Science with Python Specialization
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