DSA
Overview:
Data Structures and Algorithms (DSA) form the fundamental building blocks of efficient computer programming. Mastering DSA enables developers to solve complex problems, optimize code performance, and design scalable applications. This course covers essential topics such as arrays, linked lists, stacks, queues, trees, graphs, sorting algorithms, searching algorithms, recursion, dynamic programming, and more.
Course Objectives:
- To gain proficiency in fundamental Data Structures and Algorithms.
- To understand the principles and implementation of essential data structures like arrays, linked lists, stacks, queues, trees, and graphs.
- To learn various sorting algorithms (e.g., bubble sort, merge sort) and searching algorithms (e.g., linear search, binary search).
- To explore advanced topics such as recursion, dynamic programming, and graph algorithms.
- To apply DSA concepts to solve real-world problems efficiently.
- To analyze the time and space complexity of algorithms for performance optimization.
Prerequisite / Target Audience:
This course is tailored for individuals aspiring to become proficient in Data Structures and Algorithms, crucial for careers in software development and computer science. Participants should have a basic understanding of programming concepts such as variables, loops, functions, and conditional statements. Familiarity with any programming language is advantageous but not mandatory.
Course Modules
- Introduction to Data Structures
- Introduction to Algorithms
- Complexity Analysis (Big O notation)
- Asymptotic Notations: Θ, Ο, and Ω
- Space Complexity
- Arrays: Basics, Operations, and Problems
- String Manipulation and Problems
- Two Pointer Technique
- Sliding Window Technique
- Singly Linked Lists: Implementation and Operations
- Doubly Linked Lists
- Circular Linked Lists
- Linked List Problems: Insertion, Deletion, Reversal
- Stacks: Implementation, Operations, and Applications
- Queues: Implementation, Operations, and Applications
- Deque (Double-ended Queue)
- Understanding Recursion: Base Case, Recursive Case
- Recursive Algorithms
- Backtracking: Principles and Applications
- Problems: N-Queens, Subset Sum, Permutations
- Introduction to Trees: Types (Binary, Binary Search, AVL, etc.)
- Binary Tree: Traversal (Inorder, Preorder, Postorder)
- Binary Search Tree (BST): Operations, Search, Insertion, Deletion
- Balanced Binary Search Trees: AVL Trees
- Introduction to Heaps
- Heap Operations: Insert, Delete, Extract Max/Min
- Applications of Heaps: Heap Sort, Priority Queues
- Hash Tables: Operations and Collision Handling
- Hash Functions
- Applications of Hashing
- Graph Representation: Adjacency Matrix, Adjacency List
- Graph Traversal: Breadth-First Search (BFS), Depth-First Search (DFS)
- Shortest Path Algorithms: Dijkstra’s Algorithm
- Minimum Spanning Tree: Prim’s and Kruskal’s Algorithms
- Introduction to Dynamic Programming (DP)
- DP Approach: Memoization vs Tabulation
- Examples: Fibonacci Series, Longest Common Subsequence (LCS), Knapsack Problem
- Greedy Algorithms
- Divide and Conquer
- Bit Manipulation
- Time and Space Complexity Optimization
- Handling Edge Cases
- Practice Problems and Platforms
- Building an Application using DSA Concepts
- Performance Analysis
- Code Optimization
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