Asymptotic Analysis is a method used in computer science to evaluate the efficiency of algorithms. It focuses on how the performance of an algorithm changes as the size of the input data increases. By examining the growth rates of time and space requirements, it helps in comparing different algorithms and understanding their scalability. This analysis typically uses three notations: Big O, Big Θ, and Big Ω. Big O describes the upper bound of an algorithm's running time, while Big Θ provides a tight bound, and Big Ω indicates the lower bound. Together, these notations give a comprehensive view of an algorithm's efficiency.