Analyzing Complex Networks: Centrality Metrics with Python and NetworkX

In our highly connected world, finding the most influential nodes in a network is key to understanding how information, influence, or resources spread. This text-only course guides you from the fundamental concepts of graph theory to writing clean, modern Python code that extracts actionable insights from complex datasets. What you'll learn: - Understand the core mathematical concepts behind degree, betweenness, and closeness centrality - Calculate advanced metrics like eigenvector centrality and PageRank to identify key network nodes - Write clean, type-hinted Python code using NetworkX to load, manipulate, and analyze graph data - Interpret centrality distributions to pinpoint critical bottlenecks and structural hubs - Practice your skills with step-by-step written tutorials and code-focused network analysis exercises You will start by mastering foundational graph terminology and basic definitions before progressing to practical network analysis workflows. This course is designed for beginners in data science, sociology, or business analytics; no prior experience with graph theory is required. Start analyzing complex networks and uncovering hidden patterns today.