Algorithm analysis is a critical aspect of computer science and software engineering that focuses on evaluating the efficiency of algorithms in terms of time and space complexity. Understanding how an algorithm performs helps developers choose the most suitable algorithm for a specific problem, ensuring that applications run efficiently and effectively. Time complexity refers to the amount of time an algorithm takes to complete as a function of the input size. It is typically expressed using Big O notation, which provides an upper bound on the growth rate of the algorithm’s running time. For instance, an algorithm with O(n log n) time complexity will perform better than one with O(n^2) time complexity for larger datasets. Space complexity, on the other hand, measures the amount of memory an algorithm uses as a function of the input size. Analyzing both time and space complexity allows developers to make informed decisions regarding trade-offs between speed and memory usage. Moreover, algorithm analysis helps in comparing different algorithms for the same problem. For example, sorting algorithms like quicksort, mergesort, and bubble sort can be analyzed to determine which one is the most efficient for a particular dataset. Additionally, understanding algorithm efficiency is essential for scaling applications, especially in performance-critical systems where resource usage is a concern. Ultimately, algorithm analysis provides insights into how to optimize code, improve performance, and ensure that software applications can handle increasing loads and complexities in real-world scenarios.