First and foremost, do not even walk into a software interview without knowing
what Big O Analysis is all about  you will embarrass yourself. It's simply
something that you must know if you expect to get a job in this industry.
What is Big O Analysis:
When solving a computer science problem there will usually be more than just one solution.
These solutions will often be in the form of different algorithms, and you will generally
want to compare the algorithms to see which one is more efficient.
This is where Big O analysis helps  it gives us some basis for measuring the efficiency of an algorithm.
A more detailed explanation of Big O analysis would be this: it measures the efficiency of an algorithm
based on the time it takes for the algorithm to run as a function of the input size.
Think of the input simply as what goes into a function  whether it be an array of numbers,
a linked list, etc.
Sounds quite boring, right?
It's really not that bad at all  and it is something best illustrated by an example with actual
code samples.
Example of Big O Analysis
Let's suppose that we are given a problem in which we want to create a function that, when
given an array of integers greater than 0,
will return the integer that is the smallest in that array.
In order to best illustrate the way BigO analysis works, we will come up with two
different solutions to this problem, each with a different BigO efficiency.
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Here's our first function that will simply return the integer that is the smallest in the array.
The algorithm will just iterate through all of the values in the array and keep track of the
smallest integer in the array in the variable called curMin.
Let's assume that the array being passed to our function contains 10 elements  this number is
something we arbitrarily chose. We could have said it contains 100, or 100000 elements 
either way it would have made no difference for our purposes here.
int CompareSmallestNumber (int array[ ])
{
int x, curMin;
// set smallest value to first item in array
curMin = array[0];
// iterate through array to find smallest value
for (x = 1; x < 10; x++)
{
if( array[x] < curMin) {
curMin = array[x];
}
}
// return smallest value in the array
return curMin;
}
As promised, we want to show you another solution to the problem.
In this solution, we will use a different algorithm. What we do is compare each value
in the array to all of the other numbers in the array,
and if that value is less than or equal to all of the other numbers in the array then we know
that it is the smallest number in the array.
int CompareToAllNumbers (int array[ ])
{
bool is Min;
int x, y;
// iterate through each
for (int x = 0; x < 10; x++)
{
isMin = true;
for (int y = 0; y < 10; y++)
{
/* compare the value in array[x] to the other values
if we find that array[x] is greater than any of the values
in array[y] then we know that the value in array[x] is not the
minimum
remember that the 2 arrays are exactly the same, we are just
taking out one value with index 'x' and comparing to the other
values in the array with index 'y'
*/
if( array[x] > array[y])
isMin = false;
}
if(isMin)
break;
}
return array[x];
}
Now, you've seen 2 functions that solve the same problem  but each one uses a different algorithm.
We want to be able to say which algorithm is more efficient, and BigO analysis allows us to do
exactly that.
Big O analysis in action
For our purposes, we assumed an input size of 10 for the array. But when doing Big O analysis,
we don't want to use specific numbers for the input size  so we say that the input is of size n.
Remember that BigO analysis is used to measure the efficiency of an algorithm based
on the time it takes for the algorithm to run as a function of the input size.
When doing BigO analysis, "input" can mean a lot of different things depending on the problem
being solved. In our examples above, the input is the array that is passed into the different functions. But, input could also be the number of elements of a linked list, the nodes in a tree, or whatever data structure you are dealing with.
Since input is of size n, and in our example the input is an array  we will say that the array is of
size n. We will use the 'n' to denote input size in our BigO analysis.
So, the real question is how BigO analysis measures efficiency. Basically, BigO will want to
express how many times the 'n' input items are 'touched'. The word 'touched' can mean different
things in different algorithms  in some algorithms it may mean the number of times a constant is
multiplied by an input item, the number of times an input is added to a data structure, etc.
But in our functions CompareSmallestNumber and CompareToAllNumbers, it just means the number of
times an array value is compared to another value.
In the function CompareSmallestNumber, the n (we used 10 items, but lets just use the variable 'n' for now)
input items are each 'touched' only once when each one is compared to the minimum value.
In Big O notation, this would be written as O(n)  which is also known as linear time.
Linear time means that the time taken to run the algorithm increases in direct proportion
to the number of input items. So, 80 items would take longer to run than 79 items or any
quantity less than 79.
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You might also see that in the CompareSmallestNumber function, we initialize the curMin variable
to the first value of the input array. And that does count as 1 'touch' of the input. So, you
might think that our Big O notation should be O(n + 1). But actually, Big O is concerned with
the running time as the number of inputs  which is 'n' in this case  approaches infinity.
And as 'n' approaches infinity the constant '1' becomes very insignificant  so we actually drop
the constant. Thus, we can say that the CompareSmallestNumber function has O(n) and not O(n + 1).
Also, if we have n ^{3} + n, then as n approaches infinity it's clear that the "+ n"
becomes very insignificant  so we will drop the "+ n", and instead of having
O(n ^{3} + n), we will have O(n^{3}).
Now, let's do the Big O analysis of the CompareToAllNumbers function. Let's just say that we
want to find the worst case running time for this function and use that as the basis for the
Big O notation. So, for this function, let's assume that the smallest integer is in the very
last element of the array. Since we are taking each element in the array and comparing it to
every other element in the array, that means we will be doing 100 comparisons, if we are assuming
our input size is 10 (10 * 10 = 100). Or, if we use a variable that will n ^{ 2 } 'touches'
of the input size. Thus, this function uses a O(n^{ 2 }) algorithm.
Big O analysis measures efficiency
Now, let's compare the 2 functions: CompareToAllNumbers is O(n^{2}) and
CompareSmallestNumber is O(n). So, let's say that we have 10,000 input elements, then
CompareSmallestNumber will 'touch' on the order of 10,000 elements, whereas CompareToAllNumbers
will 'touch' 10,000 squared or 100,000,000 elements. That's a huge difference, and you can
imagine how much faster CompareSmallestNumber must run when compared to CompareToAllNumbers 
especially when given a very large number of inputs. Efficiency is something that can make a
huge difference and it's important to be aware of how to create efficient solutions.
In an interview, you may be asked what the BigO of an algorithm that you've come up with is.
And even if not directly asked, you should provide that information in order to show that you
are well aware of the need to come up with an efficient solution whenever possible.
