Big O notation describes how well an algorithm scales as the size of the input approaches a large number or infinity. The notation approximates the worst-case (upper-bound) performance of an algorithm.

Notion       / Name              / Human speak             / Common in
O(1)         / constant          / Order of 1              /
O(Log n)     / logerithmic       / Order of Log n          / Binary trees, BST, cutting things in half, recursively.   
O(Log n^c)   / polylogerithmic   / Order of Log n squared  /
O(n Log n)   / linearithmic      / Order of N log N        /
O(n)         / linear            / Order of n              /
O(n^2)       / quadratic         / Order of n squared      /
O(n^c)       / polynomial        / ?                       /
O(c^n)       / exponential       / ?                       /

Notes: 1) c is constant and n is the number of inputs. 2) O is order

Quadratic time every item in the list (aka n for the input size), we have to do n more operations. So n * n == n^2

Lineaer time, every item in the list, we have to do n operations.

Constant time means is no matter how big our input is, it always takes the same amount of time to compute things.

MIT’s Lecture Notes on Big O
Justin Abrah’s Big O Notation Explained
Justin Abrah’s How to Calculate Big O