
Data Structures
Data structure is a way of organizing and storing data in a computer program so that it can be accessed and manipulated efficiently. It is a fundamental concept in computer science that is used to store and manage large amounts of data.
Data structure plays a critical role in computer programming and is a fundamental concept in software development.
Why Do We Need Data Structures ?
Data structure is used to organize and store data in a computer program. It is essential because it enables fast and efficient data access, manipulation, and storage. Without data structure, data would be stored in an unorganized manner, making it difficult to access and manipulate.
We need data structure to manage and store data effectively. It is used in many areas, including databases, operating systems, compilers, and artificial intelligence. The use of data structure enables efficient data access and manipulation, and it also helps to improve the performance of the program.
Different Types of Data Structure















Advantages of Using Data Structures
- Efficient data access: Data structure enables fast and efficient data access, making it easier to retrieve and manipulate data.
- Data organization: Data structure helps to organize data in a logical and structured manner, making it easier to manage and manipulate.
- Memory management: Data structure helps to manage memory effectively, reducing the risk of memory leaks and other memory-related problems.
- Improved performance: The use of data structure helps to improve the performance of the program by reducing the time taken to access and manipulate data.
There are two main types of data structures: linear data structure and non-linear data structure.
Linear Data Structure
A linear data structure is one in which data items are ordered consecutively or linearly, with each member connected to its previous and next neighboring elements like Array, Linked List, Stack etc.
Array Data Structure
An array is a collection of elements of the same data type. It is a linear data structure that is used to store and manipulate data efficiently. An array can be one-dimensional or multidimensional.

Queue Data Structure
A queue is a linear data structure that follows the First-In-First-Out (FIFO) principle. It is used to store and manipulate data in an ordered manner. A queue has two operations, enqueue and dequeue, which add and remove elements from the queue, respectively.

Stack Data Structure
A stack is a linear data structure that follows the Last-In-First-Out (LIFO) principle. It is used to store and manipulate data in an ordered manner. A stack has two operations, push and pop, which add and remove elements from the stack, respectively.

Linked List Data Structure
A linked list is a linear data structure that consists of a sequence of nodes. Each node contains a data element and a reference to the next node in the sequence. It is used to store and manipulate data in an ordered manner.

Non-Linear Data Structure
Non-linear data structure is a type of data structure where data elements are not arranged in a linear sequence. Here are some examples of non-linear data structure.
Tree Data Structure
A tree is a non-linear data structure that consists of a collection of nodes. A tree is used to store hierarchical data such as file systems, organization charts, and family trees. The topmost node of a tree is called the root node, and each node can have zero or more child nodes.

Graph Data Structure
A graph is a non-linear data structure that consists of a collection of nodes and edges. The nodes represent the data elements, and the edges represent the relationships between the nodes. Graphs are used to model complex relationships such as social networks, transportation networks, and computer networks.

Trie Data Structure
A trie is a non-linear data structure that is used to store and retrieve strings efficiently. It is also known as a prefix tree because it stores strings by their prefixes. A trie consists of nodes that represent characters in the string and edges that connect the nodes.
