Big O Analysis - Data Structure Algorithms Session

​Big O analysis is a way of measuring how efficiently an algorithm uses time or memory as the input size grows. Instead of focusing on exact runtimes, it describes how performance scales, helping developers compare algorithms and choose those that remain fast even with large data. Common Big O notations include O(1), O(log n), O(n), and O(n²), each representing different growth rates and impacts on performance.