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To learn more about our privacy policy Click hereWe have described the practical uses for different data structures, ranging from the widely used Array and Linked List to the geometric R-Tree and probabilistic LogLog.
Midway through the 20th century, computer science began to take off. As it grew, so did the requirement to organise and store data in a form that made it simpler to access and manage. Then, due to scientists' efforts to modify mathematical notions of data organisation for computer use, the discipline of data structures was born.
Data structures were initially developed as relatively straightforward structures like arrays and linked lists, but as computers became more powerful, new algorithms, and techniques emerged, and the field of data structures expanded to include a wide range of more sophisticated structures like trees, graphs, and other advanced structures. Click here to know more about the instructor-led DSA course, designed comprehensively for working professionals and beginners.
Non-linear and linear data structures are the two categories under which data structures fall.
Data is organised linearly in these types of data structures. Additional divisions include:
Static data structures are those that are linear and have a fixed size. eg: Array
Data structures with a variable size are said to be dynamic data structures. During operation, it may receive random updates. e.g., vectors;
The non-linear data structure Data will not be organised linearly in these data structures. example: Trees, Graphs, etc.
In the past, programmers had to utilise many variables to store related data, which made the code harder to develop, read, and maintain. Additionally, this made it more challenging to sort or search for data using mathematical processes.
In other words, arrays are data structures that hold a group of elements of any data type in a continuous memory area.
The following fundamental operations can be performed on elements in an array: accessing an element, traversing the array, searching for an element, adding or deleting an element, sorting, reversing the array, etc.
The necessity for matrices evolved from the need to effectively store and handle massive amounts of data with a two-dimensional structure.
A matrix is a two-dimensional collection of elements, most frequently composed of numbers or expressions. The row and column indices of a matrix's cells serve as placeholders for each matrix cell, and this notation is used to access any of those cells. Two-dimensional tables, arrays, and grids are other names for matrices.
The various matrix operations include addition, subtraction, scalar multiplication, multiplication of matrices, transposition, determinant, inverse, trace, multiplication of eigenvalues, and multiplication of eigenvectors.
When elements needed to be added and deleted in a certain order, with the most recently added element being removed first, there was no effective way to store and handle elements in that order. Thus, the requirement for stacking arose.
A detailed explanation can be found in a data structure course, available online.
The Last In First Out (LIFO) principle is used to stacks, which are linear data structures. The "top" of the stack, also known as the same end, is where pieces are added and deleted. It is claimed that an element is "pushed" into the stack when it is added, and that an element is "popped" off of the stack when it is removed.
When storing and manipulating data, there was a time when the oldest piece had to be the first to be eliminated. The necessity for the queue data structure developed in this manner. An element is added to the rear of a queue and withdrawn from the front of a queue in a queue, which is a linear data structure that adheres to the First In First Out (FIFO) principle. The frequently used operations are enqueue, dequeue, peek, isEmpty, size, and clear.
For this reason, binary trees were created. Hierarchical data, where each item has a parent-child connection with other objects, needed to be efficiently stored and manipulated. It is a node-based data structure, where each node has a maximum of two child nodes.
Insertion, deletion, searching, traversal, and other operations are examples of common operations.
The desire to effectively store and search for data in a fashion that allows for quick search times gave rise to the need for binary search trees.
A sort of binary tree called a binary search tree (BST) is created specifically to store and retrieve data quickly. A BST's primary benefit is its capacity for quick searches. Because of the way the tree is set up, all of the values in a node's left subtree are lower than its value, and all of its right subtree values are higher.
Insertion, deletion, searching, traversal, tree cloning, serialisation and deserialization are a few examples of common operations.
If you want to get detailed information, do check out the online data structures and algorithms course, offered by Learnbay.
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