Dynamic Programming Fundamentals: Solving the Coin Change Problem

Dynamic programming often feels like one of the most intimidating topics in computer science, yet it is key to passing technical interviews and writing efficient code. This text-based course demystifies dynamic programming by focusing deeply on a classic algorithmic challenge: the coin change problem. You will transition from naive recursive solutions to highly optimized, production-ready code. What you will learn: 1. Understand the core principles of dynamic programming, including overlapping subproblems and optimal substructure. 2. Analyze recursive strategies and recognize where they fail due to exponential time complexity. 3. Apply memoization to cache results and drastically speed up execution. 4. Implement tabulation to build iterative, memory-efficient solutions. 5. Practice writing clean, modern code with type hints to ensure correctness and readability. 6. Evaluate space and time complexity using Big O notation to compare different algorithmic approaches. You will start with foundational definitions of algorithmic efficiency before exploring step-by-step code walkthroughs. Each concept is reinforced with written exercises designed to build your problem-solving intuition. This course is designed for beginning developers and computer science students looking to build a strong foundation in algorithms. No prior experience with dynamic programming is required. Start reading today to unlock the power of dynamic programming and elevate your coding skills.