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 stars73.79%
- 4 stars20.09%
- 3 stars4.14%
- 2 stars1.01%
- 1 star0.94%
TOP REVIEWS FROM APPLIED SOCIAL NETWORK ANALYSIS IN PYTHON
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 comprehensive course for introduction of social network analysis. Best part is every concept is covered in detail and how to implement using networkx library.
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.
This was an excellent overview of using and analyzing graphs with Python. I learned a lot, got to apply my learning from previous courses, and I earned my Specialization!
About the Applied Data Science with Python Specialization
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