javatpoint dsa Tutorials for Beginners

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Structures of naming the data keep in memory organized.

Many ways can organize the data in memory and have seen just one of those data structures – that is an array in C language. An array is a set of memory elements through which data can be stored sequentially, i.e., one after another. To put the definition of an array in another way, a continuous manner of storage of the elements is given. The above organization of data is done with the help of arrays and data structures. Organizing data can be done in yet more ways. Now comes the different types of data structure.

Declaring a data structure doesn’t mean that it’s a programming language like C, C++, java, etc. It is a set of algorithms that you can use in whatever programming language to structure the data in the memory.

To structure the data in memory, ‘n’ number of algorithms have been proposed, and all these algorithms are called Abstract data types.

Why Learn DSA?

Programming acquires great versatility, problem-solving skills, and growth in one’s career with the learning of data structures and algorithms (dsa). Their knowledge enables the writing of highly optimized codes, thus making applications faster and using fewer efforts in their resource consumption. This is an important area as tech companies measure their applicants under exams for DSA, which is vital for getting a software job offer. Enhanced logical thinking and problem-solving capabilities due to firm mastery of DSA content will contribute significantly to competitive programming. The use of efficient algorithms brings time complexity down to the minimum possible, thus rendering the programs scalable. In addition, mastering the concepts of DSA, such as trees, graphs, and dynamic programming, so that one can understand how they apply to the real world in areas such as search engines, AI, and databases. Whether it’s LeetCode or whether it’s CodeChef for all DSA practice, it will prove productive and fruitful.

javatpoint Data Structures tutorials

Array

An array is a basic data structure that keeps a linearly fixed-size number of homogeneous elements in adjacent memory locations. Arrays provide constant-time access (O(1)) to retrieve elements by index, making them very efficient for getting data. In contrast, the insertion or deletion of an element needs shifting, which leads to O(n) complexity. Arrays may be one-dimensional, multi-dimensional (2D, 3D), or dynamic using ArrayList in Java. They find their meaning in various ways: searching and sorting algorithms, databases, graphics processing. Contrasted with linked lists, arrays are fixed in size and quite certainly have contiguous memory, thus it may lead to the wastage of memory or allocation problems.

An array is a basic data structure where a fixed number of the same type of elements are stored in a contiguous memory address location. Arrays provide constant-time access (O(1)) from index to the element, which is highly efficient for retrieval. Its requirement goes to O(n) complexity when insertion or deletion has to shift elements. Arrays can be one-dimensional, multi-dimensional (2D, 3D), or dynamic (using ArrayList in java). Their uses are very many: searching and sorting algorithms, databases, graphics processing, and so on. Fixed Size as Contrasted with Linked Lists because arrays require memory in contiguous format, which might lead to wastage or allocation problems.

There are two types of two types: 1-D form to 1-D array: 1-D array has size one. A one-dimensional array has an address where it starts and length to specify its size. Two-dimensional arrays are arrays of type one-dimensional arrays. Such an array is called a one-dimensional array. It has only one row and any number of columns. Arrays can be accessed and interpreted as matrices. In terminology, an array is described as a single reference identifier to a certain memory address where data elements will be identified at that internal memory address. In practice, however, in terms of simple terminology competition, it has fixed sizes and might require memory contiguously, which can lead to waste or allocation problems.

Linked list

Linked lists form a dynamic data structure in which the elements one calls nodes are connected through pointers. Unlike arrays, linked lists don’t require contiguous memory, allowing them to put memory to good use. Each node has two parts: data and a pointer to the next node. Fast insertion and deletion (O(1)) operations in the beginning or in the midst of the linked list are possible, while searching has O(n) complexity, as the list must first be traversed. There are three main types of linked lists: singly linked list, doubly linked list, and circular linked list. Linked lists find many applications in memory management, undos, and implementations of hash tables, all due to their flexibility.

Types Of Linked list

• Singly Linked List
• Doubly Linked List
• Circular Linked List

Example:

class Node {
    int data;
    Node next;
    Node(int data) {
        this.data = data;
        this.next = null;
    }
}

Advantages of linked list in dsa

Here are some essential features of a linked list. It can also be dynamically constructed by allowing its size to change without requiring memory reallocation. Compared with arrays, it is more efficient for insertion and deletion because shifting of elements is not required. It is best suited for applications such as management, undo in text editors, and representation of graphs. However, it consumes extra pointers’ memory and has an O(n) search complexity, which is slow in comparison with that of arrays for random access.

Stack in DSA

A stack can be defined as a linear data structure that enforces the LIFO (Last In, First Out) principle, meaning that the last element inserted is the first one to leave. It is utilized in various applications like function call management, expression evaluation, and backtracking algorithms. The three operations supported by a stack are-

• Push: Place an element on the top of the stack.
• Pop: Remove the top element from the stack.
• Peek/Top: Get the top element without removing it.

Stacks can be implemented using arrays (fixed size) or linked lists (variable size). Stacks are also very important aspects in recursion, undo mechanisms, and syntax parsing.

Queue in dsa

Queue, a linear data structure that functions based on the First In First Out (FIFO) system in order to insert at one end (rear) and remove it from the other end (front), is utilized highly in scheduling algorithms, CPU task handling, and network buffering. This data structure can be implemented on an array, linked list, or stack and spirits like simple queue, circular queue, priority queue, and deque (double-ended queue). It possesses enqueue (insertion), dequeue (removal), front (peek), and isEmpty (check whether the queue is empty) operations. Queues are widely used in breadth-first search (BFS), printer job scheduling, and Operating System.

Tree in dsa

A tree is a traversable structure formed by nodes and edges. One node (the root) is selected as a starting point from which other nodes (children) develop in all directions. Each node contains data and pointers to child nodes. The tree is a widely adopted model for indexing databases, file systems, artificial intelligence, and routing algorithms.

TYPES OF TREES

• Binary Tree: A tree in which each node has a maximum of two children.
• Binary Search Tree (BST): A binary search tree, which stays sorted and enables searches in O(log n).
• AVL Tree: An automatically balancing BST.
• B-Trees: Structures used for searching and inserting records in a database.

Would you want a detailed explanation of any specific type?

Graph in dsa

The graph is a non-linear data frame consisting of vertices (nodes) and edges (connections between nodes). Goliath applications working in networking, social media, recommendation systems, and AI.

Types of Graphs:

• Directed Graph: The edges have directions (one-way roads, for instance).
• Undirected Graph: The edges have no directions (two-way roads).
• Weighted Graph: Edges carry weight (for example, in shortest path algorithms).
• Unweighted Graph: All edges are of the same cost.

Graphs can be represented using adjacency lists, adjacency matrices, or edge lists. Common algorithms are BFS, DFS, Dijkstra’s, and Floyd Warshall to tackle real-world problems such as navigation systems and web crawling.