## Subtitles section Play video

• Hey everyone. In this video, I'm going to give you an introduction to Big O notation and time complexity.

• These concepts basically give you one way of describing how the time it takes to run your function

• grows as the size of the input grows.

• To see what I mean by that exactly, let's take a look at a few examples here.

• First of all, let's say you are given an array like this and let's say that this array could be of any lengths.

• It could be one hundred elements long, a thousand elements long or even one hundred thousand elements.

• And let's say you want to write a function that takes this array and returns the sum of all the numbers in this array.

• So, in your function you wanna add up all the numbers of this array and returns the sum.

• And that function might look like this function right here and I'm gonna use pseudocode here to write this function.

• So, first of all let's define our function that gonna be called find sum which is going to take given array

• as input and then inside this function first of all we gonna initialize a variable called

• total to 0 and then for each i in this given array or for each number total to 0

Hey everyone. In this video, I'm going to give you an introduction to Big O notation and time complexity.

Subtitles and vocabulary

A2 BEG array function sum grows input total

# Introduction to Big O Notation and Time Complexity (Data Structures & Algorithms #7)

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