Graph Theory and Network Analysis in Python with Self-Assessments

Analyzing connected data is essential for modern software engineering and data science, from social networks to routing algorithms. This text-based course helps you master graph theory concepts and complex network analysis in Python while testing your understanding at every step. You will transition from understanding basic graph definitions to implementing complex network algorithms, analyzing real-world topological structures, and writing clean, typed Python code to solve network problems. What you'll learn: Understand fundamental graph theory concepts, including nodes, edges, directed graphs, and weighted networks; Build and manipulate complex networks using the NetworkX library in Python; Apply modern Python standards, including type hints, to write clean and maintainable graph algorithms; Analyze network properties such as centrality measures, clustering coefficients, and path lengths; Implement classic graph algorithms including depth-first search, breadth-first search, and shortest path calculations; Test your knowledge with structured written quizzes and conceptual self-assessments designed to reinforce your learning. The course begins with essential terminology and the mathematical foundations of graphs before moving into hands-on Python implementations, algorithm design, and network analysis techniques. Designed for beginners to graph theory and intermediate Python programmers, this course requires no prior experience with network analysis. Start reading today to master complex networks and validate your skills through structured written exercises.