What Is Data Structure and What Is Classification of Data Structure?


Angela Bailey

Data structures are a fundamental concept in computer science that allow us to organize and store data in an efficient manner. They provide a way to represent and manipulate data so that it can be easily accessed and processed by algorithms. In this article, we will explore what data structures are and discuss the classification of different types of data structures.

What is Data Structure?

A data structure is a way to organize and store data in a computer’s memory or disk. It defines the layout, operations, and relationships between the data elements. The choice of an appropriate data structure depends on the type of problem you are trying to solve and the operations you need to perform on the data.

Data structures can be thought of as containers that hold different types of data. They provide methods for inserting, deleting, searching, sorting, and manipulating the stored information. By using efficient data structures, we can optimize the performance of algorithms and improve overall program efficiency.

Classification of Data Structures

Data structures can be classified into two main categories: primitive and non-primitive.

1. Primitive Data Structures:

Primitive data structures are basic or fundamental data structures that are directly supported by most programming languages. They include:

  • Integer: A whole number without any fractional part.
  • Floating-point: A number with a fractional part.
  • Character: A single letter, digit, or special character.
  • Boolean: Represents either true or false.

The above primitive data types are simple and atomic in nature. They cannot be further divided into smaller parts.

2. Non-Primitive Data Structures:

Non-primitive data structures are complex data structures that are derived from primitive data types. They are also known as abstract data types (ADTs). Some commonly used non-primitive data structures include:

  • Arrays: A collection of elements of the same type, stored in contiguous memory locations.
  • Linked Lists: A sequence of nodes where each node contains both data and a reference to the next node.
  • Stacks: A linear data structure that follows the Last-In-First-Out (LIFO) principle.
  • Queues: A linear data structure that follows the First-In-First-Out (FIFO) principle.
  • Trees: A hierarchical structure with a set of connected nodes, starting from a root node.
  • Graphs: A non-linear structure consisting of a finite set of vertices connected by edges.

The above non-primitive data structures can be further divided into subcategories based on their characteristics and usage. These subcategories include binary trees, hash tables, priority queues, and more. Each type of non-primitive data structure has its own advantages and disadvantages, making them suitable for specific scenarios and problem-solving approaches.

In conclusion, understanding different types of data structures is crucial for efficient programming and problem-solving in computer science. By choosing the right data structure based on the requirements of your program, you can optimize performance and ensure effective manipulation of the stored information.

To summarize, we discussed the definition of data structures and their importance in organizing and manipulating data. We also explored the classification of data structures into primitive and non-primitive types, with examples of each. By using HTML styling elements like bold, underline,


  • , and subheaders, we created an engaging and visually appealing article.

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