Network Modeling with Python: Foundations of Network Science

Modern systems—from transportation grids and utility pipelines to social networks—rely on network models to function efficiently. Understanding how to represent, analyze, and optimize these networks using Python is a crucial skill for developers, data analysts, and engineers. This text-based course guides you from absolute beginner to confidently writing Python scripts to model complex networks, translating real-world systems into mathematical graphs to extract meaningful insights. What you'll learn: - Understand the core terminology of network science, including nodes, edges, weights, and directed graphs. - Build and manipulate network models using the industry-standard NetworkX library in Python. - Apply modern Python practices, including type hints and clean code structures, to write maintainable analysis scripts. - Calculate key network metrics such as degree centrality, shortest paths, and clustering coefficients. - Analyze real-world network data through structured, step-by-step written exercises. The course begins with foundational definitions of graph theory before moving into practical Python implementations. You will progress from basic graph creation to pathfinding algorithms and network robustness, all explained through clear written explanations and code snippets. This course is designed for beginners in programming, engineering, or data analysis, with no prior experience in network science required. Start building your understanding of network systems with Python today.