Understanding Time Complexity in Data Structures & Algorithms ⏱️ | DSA One-Minute Guide

Time complexity is a measure of the efficiency of an algorithm or data structure in terms of the amount of time it takes to run as the size of the input data increases. In computer science, it is common to analyze the time complexity of algorithms and data structures in order to determine their performance and efficiency.

There are several ways to express time complexity, but the most common is using "big O" notation, which gives an upper bound on the number of operations an algorithm or data structure will perform in the worst case. For example, an algorithm with a time complexity of O(n) will perform a number of operations that is directly proportional to the size of the input data. An algorithm with a time complexity of O(1) will always perform a constant number of operations, regardless of the size of the input data.

Understanding time complexity is important for choosing the right algorithm or data structure for a given problem, as well as for optimizing the performance of an existing solution. It is a key concept in computer science and is often covered in introductory courses on data structures and algorithms.