Applied Social Network Analysis in Python
About this course
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 should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.
90/100
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- What the provider tells you
- 39/45
- Who stands behind it
- 35/35
- How complete the listing is
- 16/20
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What you'll learn
- understand the fundamentals of network analysis
- measure the importance of nodes in a network
- analyze network robustness and connectivity
- explore network evolution and link prediction
Course objectives
- introduce network analysis and its applications
- teach the use of the NetworkX library for network modeling
- examine connectivity and robustness in networks
- investigate temporal changes in networks
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