Social Network Analysis with Python and NetworkX

Understanding the hidden structures within social media, transportation systems, and biological networks is essential for modern data analysis. This course provides a clear path to mastering network science, teaching you how to model and interpret the complex connections that define our world. You will transition from a basic understanding of graph theory to performing sophisticated analysis on real-world datasets. The curriculum emphasizes practical application, ensuring you can identify influential nodes, assess system vulnerabilities, and predict how networks change over time. - Understand foundational graph theory and the motivations for network modeling - Apply the NetworkX library to construct and analyze complex network structures - Measure node importance using degree, closeness, and betweenness centrality - Evaluate network robustness and connectivity to identify critical points of failure - Explore dynamic network evolution and generative models for graph growth - Practice modern Python workflows using type hints and virtual environments The course begins with essential terminology and basic graph types before progressing into deeper structural analysis and evolutionary patterns. You will read through detailed explanations and study code implementations that reflect current industry standards. This course is designed for beginners in data science and programming who want to explore the intersection of social science and computation. No previous experience with network analysis is necessary. Start analyzing the connections that shape our world today.