Shortest Path Algorithms: Graph Theory Fundamentals

Finding the most efficient route is a fundamental challenge in computer science, powering everything from GPS navigation to network routing. Understanding how to model these problems using graphs and solve them with classic algorithms is a crucial skill for any developer or computer science enthusiast.\n\nIn this course, you will transition from a beginner to confidently implementing and analyzing shortest path algorithms. You will learn how to represent graphs in code, select the right algorithm for different scenarios, and write clean code to solve routing problems.\n\nWhat you'll learn:\n- Understand foundational graph theory concepts, including vertices, weighted edges, and directed versus undirected graphs.\n- Implement Dijkstra's algorithm to find the single-source shortest path in graphs with non-negative weights.\n- Apply the Bellman-Ford algorithm to detect negative cycles and handle negative edge weights.\n- Analyze the time and space complexity of different pathfinding approaches to write optimized code.\n- Practice modeling real-world scenarios, such as network routing and map navigation, as graph problems.\n- Write clean, modern Python code using type hints to represent graphs and execute pathfinding algorithms.\n\nThis course starts with the core terminology of graph theory before guiding you step-by-step through the mechanics of Dijkstra's and Bellman-Ford algorithms. You will explore written walkthroughs, trace algorithm executions manually, and practice implementing the logic through structured text-based coding exercises.\n\nThis course is designed for beginner programmers, computer science students, and self-taught developers who want to build a solid foundation in graph algorithms. Basic familiarity with programming concepts like loops and functions is recommended, but no prior graph theory knowledge is required.\n\nStart mastering the essential algorithms that power modern navigation and routing systems today.