Foundations of Graphs and Graph Algorithms in Python

Graphs are the backbone of modern software, powering everything from social networks and recommendation engines to GPS routing. Understanding how to represent and traverse these structures is a critical milestone for any aspiring developer or computer science student. This written course guides you through the core concepts of graph theory and implementation. You will transition from understanding basic terminology to writing clean, optimized Python code that models real-world networks and solves complex relational problems. What you'll learn: - Understand fundamental graph terminology, including vertices, edges, directed/undirected graphs, and weights. - Represent graphs in Python using adjacency lists and adjacency matrices. - Implement graph nodes and structures using modern Python features like dataclasses and type hints. - Master Breadth-First Search (BFS) and Depth-First Search (DFS) traversal algorithms. - Apply graph algorithms to solve practical pathfinding and connectivity problems. - Analyze the time and space complexity of different graph representations and traversals. You will begin with basic definitions and conceptual explanations, then move step-by-step into hands-on Python implementations. Each concept is reinforced with clean code snippets and structured text explanations to help you build a strong conceptual mental model. This course is designed for beginner to intermediate Python programmers who want to learn data structures and algorithms from scratch. No prior knowledge of graph theory is required. Start reading today to unlock the power of graph algorithms in your Python projects.