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+# Big-O Notation
+
+In mathematics, big-O notation is a symbolism used to describe and compare the *limiting behavior* of a function.
+A function's limiting behavior is how the function acts as it trends towards a particular value and in big-O notation it is usually as it trends towards infinity.
+In short, big-O notation is used to describe the growth or decline of a function, usually with respect to another function.
+
+[NOTE: x^2 is equivalent to x * x or 'x-squared']
+
+For example we say that x = O(x^2) for all x > 1 or in other words, x^2 is an upper bound on x and therefore it grows faster.
+The symbol of a claim like x = O(x^2) for all x > *n* can be substituted with  x <= x^2 for all x > *n* where *n* is the minimum number that satisfies the claim, in this case 1.
+Effectively, we say that a function f(x) that is O(g(x)) grows slower than g(x) does.
+
+Comparitively, in computer science and software development we can use big-O notation in order to describe the time complexity or efficiency of algorithms
+Specifically when using big-O notation we are describing the efficiency of the algorithm with respect to an input: *n*, usually as *n* approaches infinity.
+When examining algorithms, we generally want a lower time complexity, and ideally a time complexity of O(1) which is constant time.
+Through the comparison and analysis of algorithms we are able to create more efficient applications.
+
+## Examples
+
+As an example, we can examine the time complexity of the [bubble sort](https://github.com/FreeCodeCamp/wiki/blob/master/Algorithms-Bubble-Sort.md#algorithm-bubble-sort) algorithm and express it using big-O notation.
+
+#### Bubble Sort:
+
+```c++
+// Function to implement bubble sort
+void bubble_sort(int array[], int n)
+{
+    // Here n is the number of elements in array
+    int temp;
+    for(int i = 0; i < n-1; i++)
+    {
+        // Last i elements are already in place
+        for(int j = 0; j < n-i-1; j++)
+        {
+            if (array[j] > array[j+1])
+            {
+                // swap elements at index j and j+1
+                temp = array[j];
+                array[j] = array[j+1];
+                array[j+1] = temp;
+            }
+        }
+    }
+}
+```
+
+Looking at this code, we can see that in the best case scenario where the array is already sorted, the program will only make *n* comparisons as no swaps will occur.
+Therefore we can say that the best case time complexity of bubble sort is O(*n*).
+
+Examining the worst case scenario where the array is in reverse order, the first iteration will make *n* comparisons while the next will have to make *n* - 1 comparisons and so on until only 1 comparison must be made.
+The big-O notation for this case is therefore *n* * [(*n* - 1) / 2] which = 0.5*n*^2 - 0.5*n* = O(*n*^2) as the *n*^2 term dominates the function which allows us to ignore the other term in the function.
+
+We can confirm this analysis using [this handy big-O cheat sheet](http://bigocheatsheet.com/) that features the big-O time complexity of many commonly used data structures and algorithms
+
+It is very apparent that while for small use cases this time complexity might be alright, at a large scale bubble sort is simply not a good solution for sorting.
+This is the power of big-O notation: it allows developers to easily see the potential bottlenecks of their application, and take steps to make these more scalable.
+
+For more information on why big-O notation and algorithm analysis is important visit this [video challenge](https://www.freecodecamp.com/videos/big-o-notation-what-it-is-and-why-you-should-care)!