INTRODUCTION TO DAA
Welcome to the Design and Analysis of Algorithms learning portfolio! This course is all about learning how to solve problems efficiently using computer algorithms. It focuses on designing new algorithms, analyzing their performance, and applying them to real-world problems.
The course teaches how to design algorithms using techniques like divide-and-conquer, dynamic programming, and greedy methods. It also helps in understanding how to measure their efficiency in terms of time and space, ensuring they work well for different kinds of problems.
Data Structures and Algorithms
Linear Data Structures
Arrays, Stacks, Queues, Linked Lists etc.
Non-Linear Data Structures
Binary Trees, Binary Search Trees, AVL Trees, 2-3 Tree, Heaps etc.
Advanced Concepts
Trie, Segment Tree, Fenwick Tree (Binary Indexed Tree).
Sorting and Searching Algorithms.
Buuble Sort, Selection Sort, Insertion Sort, Merge Sort, Quick Sort, Heap Sort, Binary Search, Linear Search etc.
Graph Algorithms
DFS, BFS, Dijkstra’s, Kruskal’s, Prim’s, Floyd-Warshall.
String Matching Algorithms.
Brute-Force String Matching, Boyer-Moore, Knuth-Morris-Pratt, Rabin-Karp
Real Life Applications
Algorithms are at the heart of technological innovations and solve real-world problems:
Web Search Engines: Page Ranking algorithms and efficient search techniques rely on graph traversal and optimization algorithms.
Navigation Systems: Shortest path algorithms like Dijkstra’s and A* are used in GPS navigation.
E-commerce Platforms: Recommendation systems employ graph-based algorithms and dynamic programming for personalized shopping experiences.
Cybersecurity: Cryptographic algorithms ensure secure communication over networks.Healthcare: Dynamic programming and optimization algorithms are applied in genome sequencing and medical imaging.
Social Media: Algorithms for friend recommendations and content ranking use graph theory and machine learning.