Introduction to Network Analysis and Graph Theory

In our interconnected world, understanding the relationships between data points is just as important as the data itself. Whether you are analyzing social networks, transportation systems, or information flows, network analysis provides the tools to uncover hidden patterns and structures. This text-only course guides you from the absolute basics of graph theory to performing practical network analysis. You will learn how to represent systems as networks of nodes and edges, calculate key metrics, and discover community structures within complex datasets. What you'll learn: - Understand foundational network concepts, including nodes, edges, directed graphs, and weighted connections. - Calculate key centrality measures like degree, closeness, and PageRank to identify influential nodes. - Analyze network topology and structure, including density, path lengths, and clustering coefficients. - Detect community structures and subgroups within larger, complex networks. - Apply modern Python library concepts to model and manipulate network data programmatically. - Explore real-world applications of network science in social media, biology, and infrastructure. The course begins with essential definitions and core mathematical concepts of graph theory before moving into practical analysis techniques and algorithmic workflows. You will work through structured written explanations, conceptual examples, and step-by-step code walkthroughs. Designed for beginners, this course requires no prior experience in network science, making it perfect for aspiring data analysts, researchers, and programmers. Start reading today to unlock the power of connected data.