Course Description

This course provides a comprehensive study of different concepts of data structures and algorithms, focusing on their design, implementation, and analysis. Students will learn algorithm analysis, linked lists, stacks, queues, trees, searching, hashing, sorting, and graphs. This course also focuses on algorithm design techniques and strategies such as recursion, divide-and-conquer, dynamic programming, and greedy.

Course Objectives

By the end of this course, students will be able to: Understand and implement fundamental data structures and their operations. Analyze algorithms for time and space efficiency. Understand and implement linked lists, stacks, queues, trees, and graph structures. Implement searching, sorting and hashing techniques. Understand and use algorithm design techniques and strategies.

Course Contents

1. Introduction to Algorithm Design

3 hrs

1.1. Introduction to Algorithms

1.2. Performance Analysis (Time Complexity, Space Complexity)

1.3. Asymptotic Notations (Theta Notation, Big O Notation, Omega Notation)

1.4. Amortized Analysis

1.5. Composing Complexity Classes

1.6. Computing Running Time Complexity of an Algorithm

2. Linked List

8 hrs

2.1. Array

2.2. Introducing Linked List (Nodes, Pointers)

2.3. Singly Linked Lists (Creating, Traversing, Inserting, Querying, Deleting)

2.4. Doubly Linked Lists (Creating, Traversing, Inserting, Querying, Deleting)

2.5. Circular Lists (Creating, Traversing, Inserting, Querying, Deleting)

2.6. Practical Applications

3. Stacks and Queues

6 hrs

3.1. Stacks (Introduction and Operations, Stack Implementation using Arrays, Stack Implementation using Linked Lists, Stack Applications (Bracket Matching, Infix to Postfix Conversion, Postfix Evaluation))

3.2. Queues (Introduction and Operations, Queue Implementation using Array, Queue Implementation using Linked List)

3.3. Priority Queue

3.4. Queue Applications

4. Trees

7 hrs

4.1. Introduction and Terminologies

4.2. Binary Tree (Node Implementation, Tree Traversal, Expression Trees)

4.3. Binary Search Tree (Introduction, Operations, Benefits)

4.4. AVL Tree

4.5. Heap Data Structure

5. Searching and Hashing

6 hrs

5.1. Sequential Search

5.2. Binary Search

5.3. Hashing

5.4. Hash Function

5.5. Perfect Hash Function

5.6. Collision Resolution Techniques (Linear Probing, Quadratic Probing, Double Hashing, Separate Chaining)

6. Sorting

5 hrs

6.1. Sorting Algorithms

8 hrs

7.1. Introduction and Terminologies

7.2. Directed and Undirected Graphs

7.3. Directed Acyclic Graphs

7.4. Weighted Graphs

7.5. Bipartite Graphs

7.6. Graph representations (Adjacency Lists, Adjacency Matrix)

7.7. Graph Traversals (Breadth-First Search, Depth-First Search)

7.8. Shortest-Path Algorithm

7.9. Minimum Spanning Tree (Kruskal's Algorithm, Prim's Algorithm)

8. Algorithm Design Techniques and Strategies

5 hrs

8.1. Algorithm Design Techniques

8.2. Recursion

8.3. Divide and Conquer

8.4. Dynamic Programming

8.5. Greedy Algorithms

Laboratory Works

Laboratory work includes writing programs using Python programming language to implement data structures and algorithms studied in each unit of the syllabus. Special focus will be given to implement linked list, stack, queue, tree, searching, sorting, hashing, graphs and related algorithms.

1.Laboratory Work

Reference Books

1.Agarwal, B. (2022). Hands-On Data Structures and Algorithms with Python. Third Edition. Packt Publishing.
2.Canning, J., Brode, A. and Lafore, R. (2023). Data Structure and Algorithms in Python. Pearson Education.
3.Goodrich, M.T., Tamassia, R. and Goldwasser, M. H. (2021). Data Structure and Algorithms in Python. Wiley.

Data Structure and Algorithms

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