CD3291 Data Structures and Algorithms Notes - Anna University Regulation 2021

About CD3291 Data Structures and Algorithms

CD3291 Data Structures and Algorithms is a core subject for Information Technology students, introducing fundamental concepts and practical techniques for organizing, processing, and managing data efficiently. These CD3291 notes are designed to help you understand key concepts in algorithm analysis, data structures, and computational problem-solving. Whether you are preparing for internal assessments or university exams, our Anna University study materials and CD3291 important questions make revision faster and more effective. With clear explanations and practical examples, you can build a strong foundation in Data Structures and Algorithms and improve your exam scores.

Using these CD3291 notes Anna University resources, you can quickly revise all units, clarify doubts, and practice with repeated exam questions. The content covers algorithm analysis, recursion, linear and nonlinear data structures, hashing, sorting, trees, and graph algorithms, tailored for easy learning and better retention.

What You Get on This Page

• Easy-to-understand lecture notes for all units
• Handpicked important topics frequently asked in exams
• Quick links to previous year question papers and additional resources

These resources are perfect for last-minute revision, semester exam preparation, and internal tests. All materials are organized for IT and other engineering branches following Regulation 2021.

Important Topics (Unit-wise)

Unit I - Algorithm Analysis and Recursion

• Asymptotic Analysis: Big-O, Big-Ω, Big-Θ (definitions and examples)
• Best, Average, Worst case analysis and Time vs Space complexity
• Solving complexity of loops (single and nested)
• Recurrence Relations: Substitution method, Recursion tree method
• Master Theorem with Case 1, Case 2, Case 3 and applications
• Recursive function design and Tail vs Non-tail recursion
• Stack behavior in recursion and Converting recursion to iteration
• Algorithm Design Basics: Correctness, Time-space tradeoff, Divide and Conquer

Unit II - Linear Data Structures

• Stack ADT: Operations (push, pop, peek), Array vs Linked list implementation
• Stack Applications: Parenthesis checking and Expression evaluation
• Expression Conversion: Infix to Postfix, Infix to Prefix, Postfix evaluation
• Queue ADT: Simple queue, Circular queue, Dequeue (Double-ended queue)
• Queue Applications: CPU scheduling and buffering
• Linked Lists: Singly, Doubly, and Circular linked lists
• Key Operations: Insertion (beginning, middle, end), Deletion, Traversal

Unit III - Hashing and Sorting

• Hashing: Hash functions (Division, Mid-square, Folding methods)
• Collision Resolution: Chaining and Open addressing (Linear, Quadratic, Double hashing)
• Rehashing concept and Load factor
• Merge Sort: Divide and Conquer approach, Stable sorting, O(n log n) complexity
• Quick Sort: Partition methods (Lomuto/Hoare), Best vs Worst case
• Insertion Sort: Adaptive nature and Best case O(n)
• Comparison of Sorting Algorithms: Stability, Time complexity, Space usage

Unit IV - Trees

• Binary Trees: Tree terminology, Types (Full, Complete, Perfect)
• Traversals: Inorder, Preorder, Postorder
• Binary Search Tree: Insertion, Deletion, Searching, Finding min/max, Tree height
• AVL Trees: Balance factor, Rotations (LL, RR, LR, RL), Rebalancing after insertion
• Heap and Heap Sort: Min heap, Max heap, Heap operations (insert, delete)
• Heapify process and Heap sort steps

Unit V - Graph Algorithms

• Graph Basics: Representation (Adjacency matrix, Adjacency list)
• Directed vs Undirected graphs
• Traversal Algorithms: BFS (using Queue) and DFS (using Stack/Recursion)
• Minimum Spanning Tree: Prim's Algorithm (Greedy approach, uses priority queue)
• Kruskal's Algorithm: Uses sorting and Union-Find, Cycle detection
• Dijkstra's Algorithm: Single source shortest path with non-negative weights
• Comparison Topics: Prim vs Kruskal, BFS vs DFS, Dijkstra vs Bellman-Ford