Big O Notation
Big O Notation is a mathematical concept used in computer science to describe the performance or complexity of an algorithm, specifically how its runtime or space requirements grow as the input size increases. It provides an upper bound on the worst-case scenario, allowing developers to analyze and compare algorithm efficiency in terms of scalability. This notation abstracts away constant factors and lower-order terms to focus on the dominant growth rate, such as O(1), O(n), or O(nĀ²).
Developers should learn Big O Notation to design and select efficient algorithms, especially for applications handling large datasets or requiring high performance, such as in data processing, search engines, or real-time systems. It helps in optimizing code by identifying bottlenecks, making informed trade-offs between time and space complexity, and is essential for technical interviews and competitive programming where algorithm analysis is a key skill.