Introduction and Types of Data Structures – Complete Guide



 

1. Definition of Data Structure

A Data Structure is a logical and systematic way of organizing data elements so that essential operations like search, add, and delete can be performed efficiently with minimal time and memory.

Simple Example:
Consider a library. Books (data) are arranged in shelves (data structures) according to their subjects, so that when you look for a book, it can be found immediately.

2. Types of Data Structures

Data Structures are mainly divided into two categories: Primitive and Non-Primitive.

A. Primitive Data Structures

These are basic data types that operate directly at the machine level. Programming languages provide them as predefined types.

  • Integer (int): Stores whole numbers. Example: 10, -5, 1000

  • Float/Double: Stores decimal numbers. Example: 3.14, 0.005

  • Character (char): Stores a single character or symbol. Example: 'A', 'z', '9'

  • Boolean (bool): Stores two values: True or False. Example: True, False

B. Non-Primitive Data Structures

These are complex structures built using primitive types. Their main purpose is to store large collections of data and define relationships between them.

i. Linear Data Structures

Data elements are arranged sequentially, one after another. Each element is connected to its previous and next element.

  • Array: Fixed-size collection of elements of the same type stored in contiguous memory locations. Example: Store marks of all students in a class

  • Linked List: Collection of elements (nodes) where each node contains data and a link to the next node. Example: Connect photos in a photo gallery using next/previous buttons

  • Stack: Works on LIFO (Last-In, First-Out) principle; add/remove happens from one end (top). Example: Undo/redo functionality in software

  • Queue: Works on FIFO (First-In, First-Out) principle; data added at rear and removed from front. Example: Printer job queue

ii. Non-Linear Data Structures

Data elements are not in sequential order. They are arranged hierarchically or in a network to represent complex relationships.

  • Tree: Hierarchical structure with a root node and child branches. Useful for representing hierarchies. Example: File and folder structure in a computer

  • Graph: Collection of nodes (vertices) and edges (connections). Can be directed or undirected, weighted or unweighted. Example: Cities and routes in Google Maps

  • Hash Table: Stores data in key-value pairs for fast access. A hash function is used to map keys to positions. Example: Implementing dictionaries, caching, and lookup tables

Details:

  • Tree: Root node at the top, child nodes below; each node can have multiple children.

  • Graph: Nodes connected by edges; can model networks, social connections, routes.

  • Hash Table: Uses a hash function to compute an index; allows quick insertion, deletion, and search operations. Highly useful when fast lookup is required.

3. Importance of Data Structures

  1. Optimized Data Storage: Storing data in the correct structure saves memory.

  2. Fast Data Processing: Choosing the right structure improves program speed and efficiency.

  3. Simplifies Problem Solving: Helps in efficiently solving tasks like sorting, searching, and navigation.

  4. Reusability & Maintainability: Once implemented, a data structure can be reused in multiple programs, making code easier to maintain.

Conclusion:
Data Structures form the foundation of programming. Understanding primitive and non-primitive structures, including linear and non-linear types like Trees, Graphs, and Hash Tables, is essential for creating efficient, organized, and high-performance programs.


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