Ever since the evolution of programming languages happened, data structure came into existence. It’s a systematic approach to structuring different chunks of code under different types of data structures.
We have arrays, lists, trees, and many other types, which programmers like to interpret as a classification of data structure.
Here’s what you need to know about data structure to get started.
What Are Data Structures and The Popular Types of Data Structures?
Data structures are an essential component of computer programming and are used to organize and store data in a way that allows for efficient access and manipulation.
There are several types of data structures, each with its own unique properties and applications.
Arrays:
An array is a collection of elements of the same data type that are stored in contiguous memory locations.
Arrays provide efficient random access to data, making them useful for applications such as sorting, searching, and graph traversal.
Example: An array of integers [1, 2, 3, 4, 5] can be used to store a list of numbers and perform operations such as sorting, searching, or finding the maximum or minimum value.
Linked Lists:
A linked list is a collection of nodes that contain data and a pointer to the next node in the list.
Linked lists provide efficient insertion and deletion of elements, making them useful for applications such as dynamic memory allocation, symbol tables, and sparse matrices.
Example: A linked list can be used to store a list of student records, where each record contains information such as name, ID, and grade.
Stacks:
A stack is a collection of elements that can be accessed only from one end of the collection.
Stacks follow the Last In First Out (LIFO) principle, making them useful for applications such as parsing expressions, backtracking, and depth-first search.
Example: A stack can be used to implement a web browser’s back button, where each page visited is pushed onto the stack and popped off when the back button is pressed.
Queues:
A queue is a collection of elements that can be accessed from both ends of the collection.
Queues follow the First In First Out (FIFO) principle, making them useful for applications such as job scheduling, buffering, and breadth-first search.
Example: A queue can be used to implement a printer spooler, where print jobs are added to the queue and processed in the order they were received.
Trees:
A tree is a collection of nodes that are connected in a hierarchical structure.
Trees provide efficient searching, sorting, and indexing of data, making them useful for applications such as file systems, databases, and network routing algorithms.
Example: A binary search tree can be used to store a list of numbers in a way that allows for efficient searching and sorting.
Graphs:
A graph is a collection of nodes (vertices) and edges that connect them. Graphs provide an efficient representation of relationships between objects, making them useful for applications such as social networks, transportation networks, and computer networks.
Example: A graph can be used to represent a network of computers connected to the Internet, where each computer is a vertex and each connection is an edge.
Each type of data structure has its own advantages and disadvantages, and the choice of which data structure to use depends on the specific requirements of the application.
Classification of Data Structure Vs Primitive Data Structure
Classification of data structure refers to the categorization of data structures based on various characteristics such as their organization, behavior, and operations performed on them.
There are several ways to classify data structures, including:
Linear vs Non-linear Data Structures:
Linear data structures are those in which the elements are arranged in a sequential manner, such as arrays, linked lists, stacks, and queues.
Non-linear data structures, on the other hand, are those in which the elements are not arranged in a sequential manner, such as trees and graphs.
Static vs Dynamic Data Structures:
Static data structures are those in which the size of the structure is fixed and cannot be changed during program execution.
Examples include arrays and static lists. Dynamic data structures, on the other hand, are those in which the size of the structure can be changed during program execution. Examples include linked lists, stacks, and queues.
Homogeneous vs Heterogeneous Data Structures:
Homogeneous data structures are those in which all the elements are of the same data type, such as arrays and static lists.
Heterogeneous data structures, on the other hand, are those in which the elements can be of different data types, such as structures, unions, and pointers.
Moving on, there’s another classification of data structure that experts like to call: primitive data structure.
These structures are the basic building blocks, which are usually provided by the programming language itself. These data structures are simple, atomic, and directly represent a single value.
Examples of primitive data structures include integers, floating-point numbers, characters, and Boolean values.
To that effect, another example of a primitive data structure could be an array that can be created using a sequence of contiguous memory locations, each of which contains a primitive data type, such as integers or characters.
Final Thoughts on Types of Data Structures
Without the inception of data structure, interpreting and categorizing programming language would have been a difficult task. People would have to spend hours just to locate a chunk of code, or understand the gist of it.
RAK Free Trade Zone P O Box 16111 Ras Al Khaimah, UAE
Suite 1003-4, Park Avenue, 24A, Blk 6, PECHS, Khi, PK
