Introduction to Design and Analysis of Algorithms

Understanding how algorithms work and measuring their efficiency is the cornerstone of computer science and software engineering. Without a solid grasp of algorithm analysis, writing code that scales efficiently becomes nearly impossible. This course provides a structured introduction to designing and analyzing algorithms, helping you transition from writing code that just works to writing optimal, high-performance solutions. You will start by learning core concepts and terminology, beginning with asymptotic notations like Big O, Big Omega, and Big Theta to evaluate performance. From there, you will explore classic sorting and searching techniques, and dive into fundamental algorithm design paradigms. What you'll learn: - Understand core algorithmic concepts, foundational definitions, and the importance of efficiency. - Analyze time and space complexity using asymptotic notations. - Implement and compare classic sorting and searching techniques. - Solve recurrence relations using the Master Theorem and substitution methods. - Apply greedy algorithms and divide-and-conquer strategies to solve complex problems. - Practice analyzing algorithms to prepare for academic exams and technical interviews. This course is structured to build your knowledge from the ground up, starting with theoretical mathematical foundations before moving on to practical algorithmic strategies and step-by-step analysis. It is designed specifically for beginners, students preparing for computer science exams, and self-taught developers with no prior background in algorithm analysis. Start reading today to build a strong foundation in computer science theory and write more efficient code.