Python Social Network Analysis: Practical Modeling with NetworkX

In our highly interconnected world, understanding how individuals, organizations, and systems interact is a powerful skill. This course introduces you to the fundamentals of social network analysis, showing you how to model real-world relationships as mathematical graphs. You will learn how to transition from raw relational data to meaningful insights, using Python to identify key influencers, map communities, and analyze the flow of information. What you will learn: Understand foundational network concepts, including nodes, edges, adjacency, and basic graph theory; Build, manipulate, and query complex network structures using the NetworkX library; Measure node importance using key centrality metrics like degree, closeness, and eigenvector centrality; Analyze network clustering to detect cohesive communities and subgraphs; Apply modern Python data workflows, including type hints and Pandas integration, to clean and prepare network datasets; Interpret network metrics to solve real-world problems in social science, marketing, and organizational design. The course begins with essential terminology and foundational definitions before moving into practical, step-by-step written code walkthroughs. You will read clear explanations of network algorithms and practice constructing and analyzing graphs using structured code examples. This text-only course is designed for beginners, data analysts, and researchers looking to expand their analytical toolkit. A basic familiarity with Python is recommended, but no prior experience with graph theory or network analysis is required. Start reading today to unlock the hidden patterns in relational data.