Network Analysis and Visualization with NetworkX

In our data-driven world, understanding the relationships between entities is crucial. This text-based course teaches you how to model complex systems—from social networks to transport infrastructure—using network graphs in Python. You will progress from foundational graph theory definitions to practical, programmatic network analysis. By reading through clear explanations and structured code examples, you will learn how to build, manipulate, and visualize network structures. You will gain a solid grasp of how to load real-world data into graphs and save your network models for future use. What you'll learn: - Understand foundational graph theory concepts, including nodes, edges, weights, and directed graphs - Construct and manipulate complex network structures programmatically using Python and NetworkX - Import and export network data using standard formats like edge lists and adjacency lists - Plot and customize network layouts to highlight key structural properties and relationships - Apply basic network metrics such as degree centrality and path lengths to analyze graph properties - Write clean, modern Python code incorporating type hints to ensure your network analysis is robust and maintainable This course begins with core terminology and basic graph properties before moving into hands-on data loading, analysis, and visualization techniques. It is designed for beginners with a basic understanding of Python who want to explore network science and data visualization. No prior experience with graph theory or NetworkX is required. Start exploring the power of connected data today.