Difference Between Linear and Non-Linear Data Structure

Fri Apr 11 2025

Data analysis has great potential in career building, from the media world to the marketing field, each industry needs numbers to increase its profits and productivity. There are two different techniques to structure data: (a) Linear Data Structuring and (b) Non-Linear Data Structuring. This article will help you understand the difference between Linear and Non-Linear data structuring techniques. Statistics show that in India around 97,000 jobs remain unfilled annually and the Bureau of Labor Statistics (BLS) projected a 36% growth in data scientists jobs between 2023 to 2033. It clearly indicates that opting ‘Data Scientists’ or ‘Data Analyst’ career could be the great decision of your life. Now the question is, what does a data scientist do? The answer is simple, they collect, sort, and present data in a readable and understandable format.

Table of Content

Data analysis is not a hard-to-crack nut job, it is a piece of cake once you know which technique or method can give you the desired results in a limited time. Along with this, you will also learn the types of data structures with examples to choose the best one.

🧱🔗Linear Vs. Non-Linear Data Structures

Let’s have a quick look at the difference between Linear and Non-Linear Data Structures before jumping into a detailed description of both techniques.

Basis	Linear Data Structure	Non-Linear Data Structure
Data Element Arrangement	Linear data structures arrange data elements in sequential and connect with their previous and next adjacent.	Contrary to linear, non-linear data structures arrange data hierarchically and show the relationship of each node.
Implementation Complexity	There is no complexity in the implementation of Linear data structures. It is way more easy to implement.	Non-linear data structures are divided into different levels which makes it implementation complex.
Levels	These are single-level data.	As they are divided into different levels, hence they require a higher level of understanding.
Traversal	Linear data structures are connect with their previous and next adjacent, so it is easy to traverse in a single run.	Due to complex data structuring single-run traversal is to possible in non-linear data structuring.
Memory Utilization	Linear data structures use heavy computer memory and are inefficient in memory utilization.	Non-linear data structures are memory-friendly in comparison to linear data.
Time Complexity	It depends on the input size. Bigger input sizes could increase the time complexity.	There is no connection between the input sizes and time complexity in Non-linear data structures.
Applications	Ideal for application software development.	Ideal for image processing and Artificial Intelligence.
Examples	Linked List, Queue, Stack, Array.	Tree, Graph, Hash Map.

Now, let's understand the concept of Linear and Non-Linear Data Structures in detail with their types and suitable examples for better understanding.

📏What is a Linear Data Structure?

Difference Between Linear and Non-Linear Data Structure.png

In computer science Linear Data Structure arranges data in a sequential or linear format which establishes a relation of each element to its previous and next adjacent. Linear data structure example are Linked Lists, Queue, Stack, and Array.

The following are the key traits of Linear Data Structures: -

Data elements are arranged sequentially, one after the other.
Allows single-run traversal in a linear order.
It follows the First-In-First-Out principle
Some types are restricted to the addition of more elements.
Data can be efficiently accessed in the linear data structure using their index.
There are no hierarchical relationships.
Elements are stored in contiguous memory locations, placed one after the other in memory.
Elements can be accessed linearly and no need for complex navigation.

Advantages	Disadvantages
Efficient data access Dynamic sizing Ease of implementation Versatility Simple algorithms	Limited data access Memory overhead Complex algorithms Inefficient use of memory Unsuitable for certain operations such as searching for an element in a large dataset.

➡️Types of Linear Data Structure With Examples

Arrays: All the elements in this type of linear data structure are the same type and stored in contiguous memory locations. There are three types of arrays: One Dimensional, Two-Dimensional, and Multi-Dimensional.

A one-dimensional array is the simplest form and consists of a single row.
A two-dimensional array refers to a matrix or 2D array forming a grid-like structure.
Multi-dimensional arrays have more than two dimensions. It is used when the data needs to be organized in a multi-dimensional grid.

Linked Lists: It is a chain and a collection of nodes and each node contains an element that establishes a reference to the next node. Singly, Doubly, and Circular are the three types of linear data structures.

In Singly Linked List, each node stores the reference of its next node forming a list of nodes such as 1->2->3->4->NULL
The doubly Linked Lists is a bidirectional structure, in which each node establishes reference to their previous and next node at the same time.
In the above two linked lists, the first and the last nodes do not have any reference to each other. On the contrary, Circular Linked Lists connect the first and the last node to form a circle.

Stacks: It follows the Last-In-First-Out (LIFO) principle which means the last element is the first one to be removed from the stack. There are two types of stack: (a) Fixed Size Stack and (b) Dynamic Size Stack

As the name suggested fixed size stack does not allow to addition of more elements once the stack is full, it will show an error.
On the other hand dynamic size stack can be grow or shrink as per the requirement. It allows easy resizing and adding as many as elements possible.

Queues: On the contrary queues follow the principle of First-In-First-Out (FIFO) order, which means the first added element will be removed first from the queue. Input-restricted, Output restricted, circular, Dequeue and Priority queues are the types of it.

In the Input Restricted Queue input can be taken from any of the ends but deletion is possible with any of one end.
Output Restricted Queue allows to take input from both ends but deletion is possible from only one end.
As the name suggested Circular Queue forms a circular relation of the last position with the first position and operations performed in FIFO order.
A dequeue is also known as a double-ended queue in which insertion and deletion can be performed from both ends.
In the Priority Queue elements are accessed based on the priority assigned to them.

🌳What is a Non-Linear Data Structure?

Non-linear data structures data are represented in a hierarchical or interconnected manner. This type of data is suitable for handling complex data relationships. They show complex relationships and efficiently use computer memory. Examples of non-linear data structures are trees and graphs.

The following are the key traits of the non-linear data structures:-

It creates complex structures to show connections between elements like parent and child.
Data is stored in a more flexible format, unlike linear data structures.
Data can be changed dynamically without any restrictions.
Data can be accessed easily and on the highest priority.
It ensures the efficient use of memory allocation through the scattering of data and connections.
Different traversal or navigation is required, like breadth-first traversal in graphs.

Advantages	Disadvantages
Well-suited for representing hierarchical or interconnected data. Efficient search, insertion, and deletion operations, especially in large datasets. Allows flexible and dynamic storage. Designed for efficient searching and sorting	Generally more complex to implement and manage Require more memory to store pointers or references Unbalanced, leading to performance degradation in search and other operations.

🌲🕸️Types of Non-Linear Data Structure With Examples

Majorly Trees and Graphs are the two types of data structures of non-linear data, their details are mentioned below:-

Tree: This kind of data structure is based on the parent-child relationship such as file systems or organizational charts. Nodes are connected to each other in a hierarchical format. In this data structure, there is a root node which is an entry point for the other nodes. Other than the root node all the nodes are called child nodes. This kind of data is easy to understand.

The types of trees in the data structure include: simple, binary, binary search, AVL, B- and B+ tree.

In a Simple Tree, each node can have any number of children but no cycles. This has a single root and children node.
When each node has at most two children then it is called a Binary Tree.
In Binary Search Tree left child is smaller, and the right child is larger than the parent.
AVL Tree is a self-balancing binary search tree.
B-Tree maintains sorted data and allows for efficient insertion, deletion, and search operations.
B+ Tree stored data in the leaf nodes and internal nodes only.

Graph: It is a collection of vertices and edges, where edges connect the nodes. They can show more complex relationships in an easy-to-understand manner. The types of graphs in the data structure include:

Directed Graph in which each edge has a starting and an ending node.
Undirected Graph in which edges do not have directions and the connection between the nodes is bidirectional.
Weighted Graph is a data structure in which each edge has a weight or cost. They are used to represent distances or costs between nodes.‍
The unweighted Graph shows that all edges are equal, with no specific weight assigned.

🤔Conclusion: Linear Vs Non-Linear Data Structures: Which One to Choose

Analyzing data is crucial to deciding future strategies and plans of action. Linear and non-linear data structures are suitable for the data collection process, sorting numbers and showing the desired results. Still, picking the right one is a tough decision, hence the right choice for data structuring is based on the nature of your problem and the type of data presentation.

Efficiency and the nature of relationships are the major factors in choosing the right technique. Now, you have complete knowledge about the data analyst sort data for business organizations to enhance their productivity. To begin your career in data science this information can play a vital role, the more you indulge yourself in the data the more you will learn. Collegehai.com is helping you gain a deeper understanding of data science, and data analysis, and building a career in this field.

❓Frequently Asked Questions(FAQ)

1. What are the common types of non-linear data structures?

Trees and graphs are the common types of non-linear data structures. They help in presenting data in a hierarchical format. In non-linear data structures elements are shown in parent-child relationships.

2. When should I use a linear data structure?

Linear data structures are ideal when you need to represent data sequentially and need to show simple relationships between elements. Along with this linear data structure offers dynamic changes in the data without any restrictions.

3. What are the 4 types of linear data structure?

Linked Lists, Queues, Stack, and Array are the four main types of data structures in linear data structure techniques.

4. What is linear search with an example?

Linear search is a simple algorithm that sequentially checks each element in a list until a match is found or the end of the list is reached. It's like looking for a specific item in a pile of papers, checking one by one until you find it.

5. What are the 3 types of linear models?

The three broad classes of mean structures for linear models are regression models, classificatory models (also known as ANOVA models), and analysis-of-covariance models

6. What is linear search with example?

Linear search is a simple algorithm that sequentially checks each element in a list until a match is found or the end of the list is reached. Imagine searching for a specific item in a pile of papers; you'd pick them up one by one until you find what you're looking for.

7. What are the 8 data structures?

The eight fundamental data structures are: arrays, linked lists, stacks, queues, hash tables (or maps), trees, heaps (or priority queues), and graphs.

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