What are Data Structures - Types of Data Structures (Complete Guide)

Introduction to Data Structure

Data is any set of values or collection of facts that after processing gives you the required information. In today’s era of information technology, data is the most popular term in the industry. Almost 70% of youngsters dream of careers in data science, data mining, data analysis, big data, etc.

In this Data Structures tutorial, we will see what is a data structure. The various storage methods to organize and manage this crucial asset. You can't become a software engineer, frontend developer, full stack developer, or eligible for any important job profile without the knowledge of data structures. You should know where and how such a vast amount of generated data gets stored. To become proficient in this foundational concept of computer knowledge, check our Dsa Training.

What is a Data Structure?

A Data Structure is the method of storing and organizing data in the computer memory. It is the branch of Computer Science that deals with arranging such large datasets in such a manner so that they can be accessed and modified as per the requirements. In other words, a data structure is a fundamental building block for all critical operations to be performed on the data.

The following examples from our day-to-day life will help you understand the concept of data structures.

• Our name is a data structure. It comprises First name, Middle Name, and Last name.
• The date is a data structure. It includes three types of data viz date (numeric value), month (character or numeric value), and year (numeric value).
• The 11-character IFSC code of banks is a data structure. The first four alphabetic characters in the code indicate the bank name, the fifth character is zero by default, and the last six characters (numeric or alphabetic) represent the branch.

How Data Structure vary from Data Type?

If you are confused about Data Structure and Data Type then keep in mind that both are totally different. Let's see some key differences between Data Structure and Data Type.

Aspect	Data Type	Data Structure
Definition	Describes the nature of a single data item.	Organizes multiple data items systematically.
Level	Fundamental or abstract type.	Higher-level organization of data.
Examples	int, float, char, bool.	Array, Linked List, Stack, Tree, Graph.
Purpose	Defines the operations on a specific value.	Manages and structures multiple data items.
Complexity	Simpler, focused on single units of data.	More complex, involving relationships between data.
Use Case	Basic variable declaration.	Efficient algorithms and problem-solving.

Let's familiarize ourselves with some terms going to be used throughout the DSA tutorial.

Basic Terminologies in Data Structures

• Data: It is a value or a collection of values that gives you contextual information. E.g. 30 degrees C temperature of the data, roll nos of students, etc.
• Database: It is a record of data. It is stored on a hard disk as compared to data structures that get stored in computer memory i.e. RAM.
• Elementary Items: They are data items that are unable to be divided into sub-items. e.g. roll no. of a student.
• Entity: It is a class of certain objects.
• Attributes: They are the specific properties of an entity.

Read More - Data Structure Interview Questions for Freshers

Types of Data Structures

Data Structures are mainly classified into two categories:

1. Primitive Data Structures: They store the data of only one type i.e. built-in data types. Data types like integer, float, character, and booleans come in this category.
2. Non-Primitive Data Structures: They can store the data of more than one type i.e. derived data types. Data types like arrays, linked lists, trees, etc. come in this category. Non-Primitive Data Structures are data structures derived from Primitive Data Structures.

   Based on the structure and arrangement of data, these data structures are further divided into two categories

   1. Linear Data Structures: The data in this data structure is arranged in a sequence, one after the other i.e. each element appears to be connected linearly.

      Based on the memory allocation, the Linear Data Structures are again classified into two types:

      1. Static Data Structures: They have a fixed size. The memory is allocated at the compile time, and its size cannot be changed by the user after being compiled; however, the data can be modified. e.g. array
      2. Dynamic Data Structures: They don't have a fixed size. The memory is allocated at the run time, and its size varies during the execution of the program. Moreover, the user can modify the size as well as the data elements stored at the run time. e.g. linked list, stack, queue

   2. Non-Linear Data Structures: The data in this data structure are not arranged in a sequence. They form a hierarchy i.e. the data elements have multiple ways to connect to other elements. e.g. tree and graph

1. Types of Linear Data Structures

1. Arrays

An array is a collection of elements, all of the same data type, stored in contiguous memory locations. It has a fixed size, which is specified during its creation.

Characteristics:

• Fixed-size (cannot grow or shrink dynamically).
• Indexed collection, meaning elements are accessed using an index.
• Memory-efficient for storing large datasets of the same type.

Applications: Used to store data in a database, matrices, and images (in terms of pixels).

Operations:

• Access: Direct access to any element using its index.
• Search: Searching an element by value.
• Insert/Delete: Insertions and deletions are inefficient as elements may need to be shifted.

Advantages: Fast access via index (constant time). Simple implementation.

Disadvantages: Fixed size makes it less flexible. Insertion and deletion operations can be inefficient.

Read More: Arrays in Data Structures

2. Linked Lists

A linked list is a collection of nodes, where each node stores a data element and a reference (pointer) to the next node in the sequence. It is a dynamic data structure with no fixed size.

Characteristics:

• Dynamic size (can grow or shrink as needed).
• Each node points to the next, with no contiguous memory allocation.

Applications: Used in memory management systems, and implementation of stacks, queues, and graphs.

Operations:

• Traversal: Visiting each node from the head to the tail.
• Insertion/Deletion: Inserting or deleting nodes can be done efficiently by updating pointers.

Advantages: Dynamic size allows for efficient memory usage. Insertions and deletions are efficient at both ends (front or back).

Disadvantages: Sequential access (no direct access to elements by index). Extra memory is required for storing pointers.

Read More: Linked List in Data Structures

3. Stacks

A stack is a linear data structure that follows the Last-In-First-Out (LIFO) principle, where the last element added is the first one to be removed.

Characteristics:

• Only two operations are allowed: push (add an element to the top) and pop (remove the top element).

Applications: Function call management in recursion, expression evaluation (infix, postfix, and prefix), and undo functionality in applications.

Operations:

• Push: Add an element to the top of the stack.
• Pop: Remove the top element from the stack.
• Peek: View the top element without removing it.

Advantages: Simple and fast for certain tasks like undo operations and parsing expressions.

Disadvantages: Limited access to other elements (only the top element is accessible). Fixed-size in an array-based implementation.

Read More: Introduction to Stack in Data Structures

4. Queues

A queue is a linear data structure that follows the First-In-First-Out (FIFO) principle, where the first element added is the first one to be removed.

Characteristics:

• Operates with two main pointers: front (removes elements) and rear (adds elements).
• Efficient for processing tasks in order.

Applications: CPU scheduling, printer task scheduling, handling requests in web servers.

Operations:

• Enqueue: Add an element to the rear of the queue.
• Dequeue: Remove an element from the front of the queue.
• Peek: View the front element.

Advantages: Useful for handling tasks in a predictable order.

Disadvantages: Fixed size in static implementations. Inefficient for random access.

Read More: Queue in Data Structures - Types & Algorithm (With Example)

5. Deque (Double-Ended Queue)

Definition: A deque is a linear data structure that allows elements to be added or removed from both ends (front and rear).

Characteristics:

• Supports operations at both ends.
• More flexible than a standard queue.

Applications: Sliding window problems, caching algorithms.

Operations:

• Insert Front/Rear: Add elements at either end.
• Delete Front/Rear: Remove elements from either end.

Advantages: More flexibility than queues or stacks.

Disadvantages: Increased complexity in implementation.

Types of Non-Linear Data Structures

1. Trees

A tree is a hierarchical structure that consists of nodes, with each node containing data and references to its child nodes. The topmost node is called the root, and each node can have zero or more child nodes.

Characteristics:

• Root nodes, parent nodes,and  child nodes.
• No cycles (acyclic).
• Recursive structure, as each child node can also have its own sub-tree.

Applications: File systems (directories and subdirectories), decision-making algorithms (decision trees), binary search trees for efficient searching and sorting.

Operations:

• Insertion/Deletion: Insert or delete nodes while maintaining the hierarchical structure.
• Traversal: Visiting each node (pre-order, in-order, post-order).

Advantages: Efficient searching and sorting (in balanced trees like AVL trees and red-black trees).

Disadvantages: Complex implementation, especially for balanced trees.

Read More: Trees in Data Structures

2. Graphs

A graph is a collection of nodes (vertices) and edges, where edges represent the relationships between nodes. Graphs can be directed or undirected.

Characteristics:

• Vertices (nodes) and edges (connections between nodes).
• It can be cyclic or acyclic, directed or undirected.

Applications: Social networks (users and their connections), shortest path problems (e.g., Dijkstra's algorithm), web page linking (web graphs).

Operations:

• Add/Remove Vertices and Edges: Modify the structure of the graph.
• Traversal: Visit all nodes via BFS (Breadth-First Search) or DFS (Depth-First Search).

Advantages: Can model complex relationships effectively.

Disadvantages: Memory-intensive and complex to implement, especially for large graphs.

Read More: What is a Graph in Data Structures

3. Hash Tables

A hash table stores data in an associative manner using a hash function to map keys to values. It provides efficient lookup, insertion, and deletion.

Characteristics:

• Key-value pairs.
• It uses a hash function to compute the index for storing data.
• Handles collisions through techniques like chaining or open addressing.

Applications: Caching (storing frequently accessed data), database indexing, implementing associative arrays.

Operations:

• Insert: Store a key-value pair.
• Search: Look up the value corresponding to a key.
• Delete: Remove a key-value pair.

Advantages: Fast access to data (constant time complexity in average cases).

Disadvantages: Hash collisions can reduce efficiency. Requires resizing when the load factor becomes high.

Read More: Hash Table in Data Structures

Linear Vs Non-linear Data Structures

Characteristics	Linear Data Structure	Non-linear Data structure
Arrangement of data	The data is consecutively stored one after the other.	The data is stored in a non-sequential (hierarchical) manner.
Storage of data	Each data stored is directly linked to its previous and next elements.	The data is stored in a random manner or contiguous memory locations.
Layers of data	The data is arranged in a single layer.	The data is arranged in multiple layers.
Time complexity	The time required to access all the data in the data structure increases as the volume of the data increases.	The time required to access all the data in the data structure remains the same even if the volume of the data increases.
Number of traverses	The user can traverse the data structure in a single pass.	The user cannot traverse the data structure in a single pass.
Advantages	Irrespective of the volume of the data, these data structures are easy to use where the operations are simple.	Irrespective of the volume of the data, these data structures are easy to use where the operations are complex.
Memory usage	Inefficient memory usage	Efficient memory usage
Uses	Linear data structures are widely used in software development.	Non-linear data structures are widely used in Artificial intelligence, image processing, etc.
Examples	Array, Stack, Queue, Linked Lists, etc. Each of these data structures can be further subdivided into its types.	Trees, Graphs, Tables, Sets, etc. Each of these data structures can be further subdivided into its types.

Why learn Data Structures?

• A data structure represents an Abstract Data Type (ADT) that confirms its relevance to the algorithm or application.

• Data Structures allow us to organize and store data, that are significant from the management, organization, and storage perspective

• A data structure helps in efficient data search and retrieval.

• Data structures are the most necessary elements of many robust algorithms.

Data Structure Operations

Some of the basic operations performed on a data structure are as follows:

• Traversing- Accessing or visiting each data element in a particular order in a data structure is called traversing.

• Searching- It is finding the location(s) of data that satisfy one or more conditions. We have various search techniques like linear search and binary search to search for elements in a data structure.

• Inserting—Inserting new data into the data structure is called insertion. Data can be inserted at the initial (first), final (last), or anywhere between the first and final locations. This operation increases the size of the data structure.

• Deleting—Removing existing data elements from the data structure is called deletion. Data is deleted from the initial (first), final (last), or anywhere between the first and final locations. This operation decreases the size of the data structure.

• Sorting- Arranging the data in a specified order (ascending or descending) is sorting. A user uses different techniques to sort data, viz bubble sort, shell sort, etc.

• Merging- Combining the data from two data structures (or files) into a single is called merging. This operation improves the competency in searching and ascertains correct data access to the users.

Advantages of Data Structures

• Improved data organization and storage efficiency.
• Faster data retrieval and manipulation.
• Facilitates the design of algorithms for solving complex problems.
• Eases the task of updating and maintaining the data.
• Provides a better understanding of the relationships between data elements.

Disadvantages of Data structures

• Increased computational and memory overhead.
• Difficulty in designing and implementing complex data structures.
• Limited scalability and flexibility.
• Complexity in debugging and testing.
• Difficulty in modifying existing data structures.

Summary

We saw a detailed introduction to data structures in this DSA tutorial. We understood its importance and types. In the upcoming tutorials, we will look at each type of data structure and various operation techniques on it in detail. If you want to master the concepts of data structures, refer to our Data Structures CertificationCourse.