Algorithmic Thinking: Greedy & Dynamic Programming

Many programming challenges require more than just knowing a language; they demand smart problem-solving techniques. This course introduces you to powerful algorithmic paradigms that enable you to tackle complex problems with efficiency. By the end of this course, you will be able to identify problems suited for Greedy and Dynamic Programming approaches, design efficient solutions, and analyze their performance effectively. What you'll learn: * Understand the core principles of algorithmic thinking and problem decomposition. * Apply the Greedy approach to solve optimization problems by making locally optimal choices. * Master Dynamic Programming techniques for breaking down complex problems into overlapping subproblems. * Analyze the time and space complexity of algorithms using Big O notation. * Learn strategies for identifying when to use Greedy versus Dynamic Programming. * Practice designing and implementing solutions for a variety of algorithmic challenges. * Understand how memoization and tabulation optimize recursive solutions in Dynamic Programming. The course begins with foundational concepts of algorithm analysis and problem-solving strategies, then systematically explores the Greedy paradigm with practical examples, followed by an in-depth study of Dynamic Programming principles and common patterns. You will build your understanding through clear explanations and structured exercises. This course is designed for absolute beginners in algorithms and programming, with no prior experience in advanced data structures or complex problem-solving techniques required. Start your journey into efficient problem-solving today.