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AugWhat are Data Structures - Types of Data Structures (Complete Guide)
What is Data Structure: An Overview
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 illustrations 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.
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:
- Primitive Data Structures: They store the data of only one type i.e. built-in data types. Data types like
integer
,float
,character
, andbooleans
come in this category. - 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
- 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:
- 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
- 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
- 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
andgraph
- 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.
Types of Linear Data Structures
We will now see in brief types of the linear data structures.- Arrays
An array is a powerful data structure that allows users to store and manipulate a collection of elements of the same data type in a single variable. Simply, it is a collection of elements of the same data type. The values are stored at contagious memory locations that can be accessed with their index number.We will see arrays
in detail in the section Arrays in Data Structures.
- Linked Lists
In a linked list data structure, data elements are connected through a series of nodes using links or pointers. Each node contains two fields: the data field contains the actual data, and the pointer field contains the address of the consequent nodes in the list. The pointer of the last node of the linked list is a null pointer, as it points to nothing. The elements are stored dynamically in the linked list.- Stack
In the stack data structure, elements are stored according to the LIFO principle, i.e., the Last In First Out principle. As per LIFO, the last element stored in a stack, e.g., a pile of books, will be removed first. Stacks can be implemented with the help of contiguous memory, an Array, and non-contiguous memory, a Linked List.- Queue
In the queue data structure, elements are stored according to the FIFO, i.e., First In, First Out principle. As per FIFO, the first element stored in a queue will be removed first. e.g. students standing in a queue where the first student in the queue will enter first in the school. The insertion of an element in a Queue is done at one end, and the removal is done at another or opposite end. Queues can be implemented with the help of Arrays, linked lists, or stacks.Types of Non-Linear Data Structures
We will now see in brief types of the non-linear data structures.- Graphs
Graph data structure consists finite number of nodes known as vertices and the edges connecting them. e.g. the nodes or vertices of a Graph can represent a single user in a telephone network, while the edges represent the link between them via telephone.- Trees
A tree data structure forms a hierarchy containing a collection of nodes, each of which stores a value and a list of references to other nodes (the "children"). It can be thought of as a linked list with two or more pointers. A tree is similar to a graph, as it is also a collection of vertices and edges, but there can only be one edge between two vertices.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, |
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.