Big O notation is a mathematical notation used in computer science to describe the performance or complexity of an algorithm, particularly regarding time and space as the input size grows. It provides an upper bound on the growth rate of an algorithm's running time or memory usage, allowing developers to compare the efficiency of different algorithms objectively. Big O notation expresses complexity in terms of the worst-case scenario, providing a worst-case time or space complexity for an algorithm. Common Big O complexities include O(1) for constant time, O(log n) for logarithmic time, O(n) for linear time, O(n log n) for linearithmic time, O(n^2) for quadratic time, and O(2^n) for exponential time. Understanding Big O notation is crucial for algorithm analysis and optimization, as it helps developers identify potential bottlenecks in their code and make informed decisions about which algorithms to use based on their performance characteristics. For example, when dealing with large datasets, choosing an algorithm with a lower Big O complexity can lead to significantly faster execution times and better resource utilization. Additionally, Big O notation is essential for preparing for technical interviews, as many interview questions revolve around analyzing the complexity of algorithms. Ultimately, mastery of Big O notation empowers developers to write efficient code and build scalable software applications.