Big O notation is used in Computer Science to describe the performance or complexity of an algorithm. Big O specifically describes the worst-case scenario and can be used to describe the execution time required or the space used (e.g. in memory or on disk) by an algorithm. -Big Os O(1) Constant- no loops O(log N) Logarithmic- usually searching algorithms have log n if they are sorted (Binary Search) O(n) Linear- for loops, while loops through n items O(n log(n)) Log Liniear- usually sorting operations O(n^2) Quadratic- every element in a collection needs to be compared to ever other element. Two nested loops O(2^n) Exponential- recursive algorithms that solves a problem of size N O(n!) Factorial- you are adding a loop for every element Iterating through half a collection is still O(n) Two separate collections: O(a * b) -What can cause time in a function?- Operations (+, -, *, /) Comparisons (<, >, ==) Looping (for, while) Outside Function call (function()) -Rule Book Ru

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