An Introduction to Data Structures, Big O and little bit about complexity and performance level of an algorithm.

1. DATA STRUCTURES AND BIG O
- ABBHIRAMI. M
http://abbhirami.blogspot.in/

2. SO WHAT EXACTLY DOES IT MEAN?
 Data structures, splitting this we get data and
structures.
 Structuring the data, is what is called data structure.

3. BUT WHY SHOULD WE STRUCTURE IT?
 Structuring it or tiding its order lets us to get an
easy access to it.
 Manipulations on them is quite easy when you
structure them. So that is why we structure it.

4. TYPES OF STRUCTURING DATA
 Quite common way of grouping the liked ones-
Arrays
 Attaching links with having pointers - Linked list
 Branching – Tree structure

5. WHAT IS BIG O NOTATION IN DS?
 This is a very common question that is widely
asked when we deal with data structures.
 Big O is nothing but checking the complexity of an
algorithm or performance level of an algorithm,
mainly dealing with the worst case scenario. Here
comes the next question
 Why should I know its performance level?

6. PERFORMANCE LEVEL DOES IT MATTER?
 Of course. Yes. When you work with computer, its
swiftness really matters with the rate of growth of
the inputs. This is what we call here as
performance level.
 Well, not just its swiftness, its efficient memory
management so as to access things quickly from
the storage devices also matters.

7. GETTING BACK TO BIG O
 Right now we know a little about it. Let us fathom its
meaning a little bit more.
 Let us remember at this moment our purpose of
structuring the data - it is to access it easily.
 So we use different search techniques which would let
to find our data quickly even when our inputs are large.
 We can have many ways to search our data but the one
that is working well with “big inputs” is what we really
want and only that searching technique would be
considered efficient.
 Remember: Large inputs really matters with complexity.

8. SEARCHING TECHNIQUE
 Case 1 : Liner search – O(n), where O denotes the
Big O(worst case scenario), letting us know that when
“n” inputs are given, this search technique takes “n”
amount of time to access ,since it checks with each
data item. That is when more input is added the time
consumed to search increases( More inputs, more is
time taken to fetch the element needed),
 Case 2 : O(1) int array[i++] = int newItem. Here in this
case, no matter what ever the size is this statement is
going to perform only one action.(no variable used
inside the braces like “n” in the above case, constant
used)

9. CONCLUSION
 I hope this helps you to get a better idea on what is
data structure and Big O notation.