Data Structures provides programmers with an effective way of organizing and storing the data in the computer and using it effectively. It is the core of Computer Science. Concepts of Computer Science like Operating systems, Artificial Intelligence, Compiler Design, Graphics, and more.

Data Structures are a crucial part of the implementation of the algorithms. Examples of Data Structures are Linked Lists, arrays, queues, Stack, etc. Implementing the algorithms with the help of these Data Structures allows programmers to utilize the data efficiently and take care of the computer’s storage space. It helps you improve the performance of your software or any programs that you run and store or retrieve the user data faster.

Data Structures can be classified into Primitive Data Structures and Non-Primitive Data Structures. Non-Primitive Data Structures can be again categorized as Linear and Non-Linear. Linear Data Structures are classified into Static (example: Array) and Dynamic (example: Linked List, Queue, Stack). Non-Linear Data Structures are then organized into Trees and Graphs.

The need for these Data Structures is growing drastically. As applications are becoming more and more complex, there is a need to store and manage the data effectively. Several main reasons for the need for Data Structures include:

• Data Search

Finding the data from the pile of stored data in a complex application is tricky as you will have to traverse a lot that slows down the processor. With the help of Data Structures and implementing them efficiently, you can easily carry out the data search.

• Processor Speed

With the increase in the complexity of the software and system piling of data is growing more and more in this data-driven world. If these large amounts of data are not processed efficiently, it slows down the processor speed. But with the help of Data Structures, you can effectively handle the data and increase the processor speed.

• Multiple Requests

Then are chances of failure of web servers when there are thousands of user requests. This happens due to the inefficient way of organizing data. This issue can be resolved by effectively implementing the Data Structures to handle these requests, resulting in the better performance of the web server.

Advantages of Data Structures include:

• Efficiency
• Abstraction
• Reusability

Several operations can be done on Data Structures. They are:

• Traverse

Traversing through Data Structures is nothing but visiting each stored data and finding the one you require. It also performs specific operations like sorting and searching.

• Search

To find the data and its location from a pile of stored data from a Data Structure is searching. The two well-known algorithms for searching the data are Linear search and Binary search. 

• Sort

To arrange the data in a specific order in the Data Structures is known as sorting. Many algorithms allow you to sort the data effectively, for example, Quicksort, Bubble sort, and more.

• Insertion

Insertion is the method of adding data to your Data Structures.

• Merge

When you combine similar data types to produce an effective Data Structure is called merging. Merging can be effectively seen in some of the sorting algorithms like Merge sort etc.

• Deletion

Deleting the unwanted data from the Data Structures is known as Deletion.

Data structures and algorithms are vast concepts that need to be learned in-depth to become an efficient programmer. To learn Data Structures enrol in Great Learning Academy’s Free Data Structures courses and attain free Data Structures certificate.