Big O Notation is a mathematical expression that describes how much time an algorithm takes to run according to the size of it's inputs; mostly concerned about the worst case scenario.   Types:   1- Constant Time O(1):  On this order, regardless of the number of items, the iterations(time) are constant.   Example:  const getFirstItem = items =>     items[0];   getFirstITem([1, 2, 3, 4]);  // 1 (one iteration)  getFirstItem(['b', 'd', 'g']);   // 'b' (one iteration)   2- Linear Time O(n):  On this order, the worst case grows with the number of items.   Example:  Javascript's built in function indexOf, it loops over an array to find the  correct index of the passed element. The worst case is looping over the whole array.   [1, 2, 4, 9, 23, 12].indexOf(12);   3- Quadratic Time O(n ^ 2):  For this order, the worst case time is the square of the number of inputs. It grows exponentially according to the number of inputs.   Example:  Using nested loo...
 
 
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