Welcome "Data Structures" project repository! This repository serves as a comprehensive guide to the fundamental data structures used in computer science.
Data structures are the building blocks of any computer program. They provide efficient ways to organize and manage data, enabling developers to write more effective and optimized code. In this project, we aim to explore a variety of data structures, understand their properties, and implement them in various programming languages.
Arrays are the most fundamental data structure, providing a way to store a collection of elements of the same data type. In this section, we'll cover:
- Basic array operations (access, insert, delete, search)
- Dynamic arrays and resizing
- Multi-dimensional arrays
- Time and space complexity analysis
Linked lists are a dynamic data structure where each element (called a node) contains data and a reference (or link) to the next node in the sequence. We'll explore:
- Singly Linked Lists
- Doubly Linked Lists
- Circular Linked Lists
- Implementing basic operations (insert, delete, search)
Stacks are a Last-In-First-Out (LIFO) data structure, where elements are added and removed from the top of the stack. We'll cover:
- Basic stack operations (push, pop, peek)
- Applications of stacks (expression evaluation, function calls, undo/redo)
- Implementing stacks using arrays and linked lists
Queues are a First-In-First-Out (FIFO) data structure, where elements are added to the rear and removed from the front. We'll explore:
- Basic queue operations (enqueue, dequeue, peek)
- Priority Queues
- Implementing queues using arrays and linked lists
Trees are a hierarchical data structure that organizes data in a tree-like fashion. We'll cover:
- Binary Trees
- Binary Search Trees
- AVL Trees
- Red-Black Trees
- Traversal Algorithms (Preorder, Inorder, Postorder, Level-Order)
Graphs are a data structure that represents a set of objects (vertices) and the connections between them (edges). We'll explore:
- Graph Representations (Adjacency Matrix, Adjacency List)
- Graph Traversal Algorithms (Depth-First Search, Breadth-First Search)
- Shortest Path Algorithms (Dijkstra's, Bellman-Ford)
Hashing is a technique for efficiently storing and retrieving data. We'll cover:
- Hash Functions and Collision Handling
- Hash Tables
- Applications of Hashing (Caching, Cryptography)
Heaps are a specialized tree-based data structure that satisfies the heap property. We'll explore:
- Min Heaps and Max Heaps
- Heap Operations (Insert, Extract, Heapify)
- Priority Queues using Heaps
Sorting is a fundamental operation in computer science. We'll cover various sorting algorithms, including:
- Bubble Sort
- Insertion Sort
- Selection Sort
- Merge Sort
- Quick Sort
- Heap Sort
- Radix Sort
We welcome contributions to this project! If you have any suggestions, bug reports, or would like to add new content, please feel free to open an issue or submit a pull request.
This project is licensed under the MIT License.