This repository contains a collection of Data Structures and Algorithms (DSA) snippets implemented in various programming languages. The snippets cover topics from the course "Introduction to Data Structures" and include code samples and explanations for better understanding.
Structured programming emphasizes the use of clear and efficient programming structures. It encourages the use of modular design and programming practices that enhance code readability and maintainability.
Understanding data structures and pointers is fundamental for efficient programming. Pointers allow manipulation of memory, and data structures provide organized ways of storing and managing data.
Dynamic memory management in C involves allocating and deallocating memory during program execution. This section explores the use of functions like malloc, calloc, realloc, and free for managing memory dynamically.
Algorithm analysis involves evaluating the efficiency of algorithms in terms of time and space complexity. We discuss the Big-O notation and analyze algorithm efficiency in various scenarios.
Abstract Data-Types (ADTs) provide high-level descriptions of data structures and operations. They encapsulate the essential characteristics of data structures, such as stacks, queues, and lists.
Arrays provide a contiguous block of memory to store elements of the same type. They offer efficient random access to elements using indices.
Stacks have diverse applications, such as Expression Evaluation, Conversion of Infix to Postfix and Prefix expressions, and the classic Tower of Hanoi problem.
Queues follow the First-In-First-Out (FIFO) principle. We explore different types of queues, including Circular Queue and Double-Ended Queue (deQueue). Priority Queue implementation using arrays is also covered.
Linked lists come in various forms, such as singly linked lists, doubly linked lists, and circular linked lists. Each type has its unique advantages and use cases.
Linked lists find applications in various scenarios, including the implementation of stacks, queues, and circular queues. We also explore polynomial manipulation using linked lists.
Linked lists provide an elegant implementation for stacks and queues. We cover insertion, deletion, search, display, and reversal operations in both single and doubly linked lists.
Linear search and binary search are fundamental searching algorithms. We discuss their implementations and applications, such as finding the square root of 'n' and finding the Longest Common Prefix.
Searching algorithms have practical applications, such as finding elements in a dataset, spell checking, and more.
Insertion sort, selection sort, and bubble sort are simple sorting algorithms suitable for small datasets. We discuss their implementations and analyze their time complexity.
Advanced sorting algorithms, including quick sort, merge sort, shell sort, radix sort, and heap sort, are explored. We analyze their efficiency and use cases.
Different file organization techniques, such as sequential, direct, indexed sequential, and hashed, are discussed. Various types of accessing schemes are also explored.
Graphs are versatile data structures with various representations, such as adjacency list and adjacency matrix. We explore basic graph definitions and properties.
Breadth-First Search (BFS) and Depth-First Search (DFS) are essential graph traversal techniques. We discuss their implementations and applications.
Minimum Spanning Trees (MST) are crucial in network design. We cover Prim’s and Kruskal’s algorithms for MST, connected components, and Dijkstra's Algorithm for Single Source Shortest Paths.
Binary trees are hierarchical data structures with unique properties. We discuss terminology, tree traversals, expression trees, and operations in Binary Search Trees (BST).
AVL trees are self-balancing binary search trees. We explore the rotations involved in maintaining balance in AVL trees.
Hashing involves mapping keys to indices using hash functions. We discuss open hashing and closed hashing techniques.
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