Welcome to the foundational guide for Data Structures and Algorithms (DSA). This document provides a concise yet comprehensive overview of key data structures that form the core of computer science and software development.
Data Structures and Algorithms (DSA) is a crucial part of programming that deals with organizing, managing, and storing data for efficient access and modification. Mastering DSA enhances your ability to write optimized code and solve complex problems effectively.
- Definition: A collection of elements stored in contiguous memory locations.
- Properties:
- Fixed size.
- Indexed access (0-based in most languages).
- Use Cases: Static lists, matrix operations, caching.
- Definition: A sequence of elements (nodes), where each node points to the next.
- Types:
- Singly Linked List
- Doubly Linked List
- Circular Linked List
- Advantages: Dynamic size, efficient insertions/deletions.
- Use Cases: Implementing stacks, queues, memory management.
- Definition: A linear data structure following Last-In, First-Out (LIFO).
- Operations:
push(),pop(),peek() - Use Cases: Expression evaluation, backtracking, undo functionality.
- Definition: A linear data structure following First-In, First-Out (FIFO).
- Variants: Circular Queue, Priority Queue, Deque.
- Use Cases: Scheduling, buffering, handling requests.
- Definition: A special Tree-based structure satisfying the heap property.
- Types:
- Min-Heap (parent ≤ children)
- Max-Heap (parent ≥ children)
- Use Cases: Priority queues, heap sort, shortest path algorithms.
- Definition: A hierarchical structure consisting of nodes with a root node and child nodes.
- Types:
- Binary Tree
- Binary Search Tree (BST)
- AVL Tree, Red-Black Tree
- Use Cases: Searching, sorting, database indexing, hierarchical data.
- Definition: A collection of nodes (vertices) connected by edges.
- Types:
- Directed / Undirected
- Weighted / Unweighted
- Use Cases: Network routing, social networks, pathfinding.
- Definition: A structure that maps keys to values using a hash function.
- Advantages: Constant-time complexity for search, insert, and delete (on average).
- Use Cases: Caching, associative arrays, implementing sets/maps.
In future sections, we’ll dive into:
- Time & Space Complexity Analysis
- Searching and Sorting Algorithms
- Recursion and Dynamic Programming
- Greedy Algorithms, Backtracking, and more...
This README is a beginner-friendly guide to build a strong foundation in DSA. Practice implementing these structures and solving real problems to sharpen your skills.
"Data Structures are the building blocks of efficient algorithms. Learn them well, and everything else becomes easier." 💡
📁 Happy Coding!