The Big-O notation is used to represent asymptotic upper bounds. It describes relevant time or space complexity of algorithms. Big-O analysis provides a coarse and simplified estimate of a problem difficulty.

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Simply put, Big-O notation is how programmers talk about algorithms. A function's Big-O notation is determined by how it responds to different inputs. How much slower is it if we give it a list of 1000 things to work on instead of a list of 1 thing?

This webpage covers the space and time Big-O complexities of common algorithms used in Computer Science.

When talking about Big O we simply mean the growth’s rate of an algorithm and how well, fast it scales.