Coursera : Applied Social Network Analysis in Python

This repository is summary of what I studied and python3 codes of network(graph) anlysis that is shown on coursera lectures.

Usually the codes focus on graph visualization using NetworkX v2.4 and graphViz maybe…

This ipython notebooks have markdown contents with Latex notation to represent math formula,

so I would like to say you better see notebook files with jupyter notebook or jupyter lab.

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.

What you will learn

• Analyze the connectivity of a network
• Measure the importance or centrality of a node in a network
• Predict the evolution of networks over time
• Represent and manipulate networked data using the NetworkX library

What you will have

Graph Theory,
Network Analysis,
Python Programming,
Social Network Analysis

Environment

• Python ≥ 3.6.9
  
• Jupyter ≥ 1.0.0
  
• Jupyterlab ≥ 1.1.4
  
• networkx ≥ 2.4