Skip to main content

What is Data Structure and Types of data structure


Data Structure is an organized way to store the data through various Linear and Non-Linear ways . Data structures Algorithms are the initial stage to start for programmers of any kind like desktop softwares , mobile applications or web applications . Data Structures contains various algorithms to complete different kind tasks in the programming practicals .

What is Data Structure and Types of data structure


Some of the very popular and useful concepts for the structuring of data in the programmes are –

1.    Arrays – Arrays are the collection of the same type of data types . If an element stored in array is Integer type , definitely all other of Integer Data types .
All the elements stored in an array are numbered from index 0 to n-1 . This array block covers a block in the memory keeping together its elements at one place . It is a Linear data type structure .

2.    Linked Lists – Linked Lists store the data elements in the same way as an array does but linked list can store the elements at different-different positions in the memory and , link them to access when the program needs , that’s why this method of storing the data in memory is called Linked List .
Link list does not cover a memory block to store elements rather than stores the elements at the different memory locations So We can skip the Low Memory Availability problems by using Linked Lists concept . It is also a Linear data type structure .

3.    Stacks – If we choose the LIFO way to store elements in memory , It is Stack . Here LIFO stands for Last In First Out . In this strategy a Pointer variable used to show the top element of the stack . If an element added then pointer increments by one else decrements by one . It is also a Linear data type structure .

4.    Queues – This concept uses the FIFO strategy to store the data elements in the memory . Here FIFO stands for First In First Out , means the first entered element can leave first from the queue . You can see the Queue system in Ticket booking where the first entered person in the queue collects the ticket first and leave . It is also a  Linear data type structure .

5.    Graphs – To store the data elements in the Polygon Form or other branches connected is the Graph strategy . This can be Directional and non-Directional strategy . It is a Non-Linear data type structure .

6.    Trees – To store the data nodes in a Tree like hierarchy is Tree System . The initial or root node is called the parent child and then categorized into tree like hierarchy and called Child Nodes . It is a Non-Linear data type structure .

7.    Hashing – Hashing is the most popular and secure way now a days to store data in the memory from the software programmes or mobile applications . It use a mapping system where every node store a data element a particular key maps that node to access the data .

The above described data structure concepts cover all the major fundamentals used to structure and manage all the available data like records , files data etc .

To implement these concepts on paper work , programmers use Algorithms . Algorithms are the straight and step by step guide to implement a concept while practicle tasks .

Programmers use the different-different programming languages to implement the data structuring Algorithms in the programmes . They use C , C++ , Java , Python etc programming languages codes as they feel easy and convenient . 

Some of the very useful other Algorithms are –

1.    Searching Algorithms – These algorithms are used to search specific strings or words in the file using various searching algorithms like Binary Search , Quick Search , etc . All these algorithms differ by their Time and Space Complexity .

2.    Sorting Algorithms – These algorithms are used to sort a group of data to arrange either in a ascending or descending way using the various sorting algorithms like Binary Sorting , Insertion Sorting , Merge Sort , Bubble Sort , Heap Sort etc . All these sorting algorithms differ by their Time and Space complexity .

Comments