- Data Structures & Algorithms
- DSA - Home
- DSA - Overview
- DSA - Environment Setup
- Data Structures
- DSA - Data Structure Basics
- DSA - Data Structures and Types
- DSA - Array Data Structure
- Linked Lists
- DSA - Linked List Basics
- DSA - Doubly Linked List
- DSA - Circular Linked List
- Stack & Queue
- DSA - Stack
- DSA - Expression Parsing
- DSA - Queue
- Graph Data Structure
- DSA - Graph Data Structure
- DSA - Depth First Traversal
- DSA - Breadth First Traversal
- DSA - Spanning Tree
- Tree Data Structure
- DSA - Tree Data Structure
- DSA - Tree Traversal
- DSA - Binary Search Tree
- DSA - AVL Tree
- DSA - Red Black Trees
- DSA - B Trees
- DSA - B+ Trees
- DSA - Splay Trees
- DSA - Tries
- DSA - Heap
- Recursion
- DSA - Recursion Basics
- DSA - Tower of Hanoi
- DSA - Fibonacci Series
- DSA Useful Resources
- DSA - Questions and Answers
- DSA - Quick Guide
- DSA - Useful Resources
- DSA - Discussion

# Data Structures and Types

**Data structures** are introduced in order to store, organize and manipulate data in programming languages. They are designed in a way that makes accessing and processing of the data a little easier and simpler. These data structures are not confined to one particular programming language; they are just pieces of code that structure data in the memory.

Data types are often confused as a type of data structures, but it is not precisely correct even though they are referred to as Abstract Data Types. Data types represent the nature of the data while data structures are just a collection of similar or different data types in one.

There are usually just two types of data structures −

Linear

Non-Linear

## Linear Data Structures

The data is stored in linear data structures sequentially. These are rudimentary structures since the elements are stored one after the other without applying any mathematical operations.

Linear data structures are usually easy to implement but since the memory allocation might become complicated, time and space complexities increase. Few examples of linear data structures include −

Arrays

Linked Lists

Stacks

Queues

Based on the data storage methods, these linear data structures are divided into two sub-types. They are − **static** and **dynamic** data structures.

### Static Linear Data Structures

In Static Linear Data Structures, the memory allocation is not scalable. Once the entire memory is used, no more space can be retrieved to store more data. Hence, the memory is required to be reserved based on the size of the program. This will also act as a drawback since reserving more memory than required can cause a wastage of memory blocks.

The best example for static linear data structures is an array.

### Dynamic Linear Data Structures

In Dynamic linear data structures, the memory allocation can be done dynamically when required. These data structures are efficient considering the space complexity of the program.

Few examples of dynamic linear data structures include: linked lists, stacks and queues.

## Non-Linear Data Structures

Non-Linear data structures store the data in the form of a hierarchy. Therefore, in contrast to the linear data structures, the data can be found in multiple levels and are difficult to traverse through.

However, they are designed to overcome the issues and limitations of linear data structures. For instance, the main disadvantage of linear data structures is the memory allocation. Since the data is allocated sequentially in linear data structures, each element in these data structures uses one whole memory block. However, if the data uses less memory than the assigned block can hold, the extra memory space in the block is wasted. Therefore, non-linear data structures are introduced. They decrease the space complexity and use the memory optimally.

Few types of non-linear data structures are −

Graphs

Trees

Tries

Maps