Syllabus for Data Structures and Algorithms

Course Overview

- Objective: To provide a deep understanding of data structures and algorithms, enabling students to design efficient algorithms and use appropriate data structures for problem-solving.

- Prerequisites: Basic programming knowledge (preferably in Python, Java, or C++).

Module 1: Introduction to Data Structures

- Week 1: Introduction

  - What are Data Structures?

  - Importance of DSA in Software Development

  - Time and Space Complexity Analysis

- Week 2: Arrays

  - Definition and Types (1D, 2D)

  - Operations (Insertion, Deletion, Traversal)

  - Dynamic Arrays and ArrayList

Module 2: Linked Lists

- Week 3: Singly Linked Lists

  - Definition and Implementation

  - Operations (Insertion, Deletion, Search)

- Week 4: Doubly and Circular Linked Lists

  - Definition and Implementation

  - Operations and Applications

Module 3: Stacks and Queues

- Week 5: Stacks

  - Definition and Implementation

  - Applications (Expression Evaluation, Backtracking)

- Week 6: Queues

  - Definition and Implementation

  - Types (Circular Queue, Priority Queue)

  - Applications (Scheduling, BFS)

Module 4: Trees

- Week 7: Introduction to Trees

  - Definition and Terminology

  - Binary Trees and Traversal Methods (Pre-order, In-order, Post-order)

- Week 8: Binary Search Trees (BST)

  - Definition and Properties

  - Operations (Insertion, Deletion, Search)

- Week 9: Balanced Trees

  - AVL Trees, Red-Black Trees

  - Operations and Rotations

Module 5: Hashing

- Week 10: Hash Tables

  - Definition and Hash Functions

  - Collision Resolution Techniques (Chaining, Open Addressing)

Module 6: Graphs

- Week 11: Introduction to Graphs

  - Definition and Representation (Adjacency Matrix, List)

  - Types of Graphs (Directed, Undirected, Weighted)

- Week 12: Graph Traversal Algorithms

  - Depth-First Search (DFS)

  - Breadth-First Search (BFS)

- Week 13: Shortest Path Algorithms

  - Dijkstra’s Algorithm

  - Bellman-Ford Algorithm

  - Floyd-Warshall Algorithm

Module 7: Searching and Sorting Algorithms

- Week 14: Searching Algorithms

  - Linear Search and Binary Search

  - Complexity Analysis

- Week 15: Sorting Algorithms

  - Bubble Sort, Selection Sort, Insertion Sort

  - Merge Sort, Quick Sort, Heap Sort

  - Complexity Analysis and Best Use Cases

Module 8: Advanced Topics

- Week 16: Dynamic Programming

  - Introduction and Principles

  - Common Problems (Knapsack, Fibonacci, Longest Common Subsequence)

- Week 17: Greedy Algorithms

  - Principles and Common Problems (Activity Selection, Huffman Coding)

- Week 18: Backtracking

  - Principles and Common Problems (N-Queens, Sudoku Solver)

Module 9: Practical Applications and Project Work

- Week 19: Real-world Applications

  - Use cases of Data Structures in Software Engineering

  - Problem Solving and Competitive Programming

- Week 20: Capstone Project

  - Design and implement a project using learned concepts

  - Present findings and solutions

Assessment

- Quizzes: Weekly quizzes to assess understanding of topics.

- Assignments: Programming assignments to implement data structures and algorithms.

- Midterm Exam: An exam covering the first half of the syllabus.

- Final Exam: A comprehensive exam covering all topics.

- Project Presentation: Presenting the capstone project.

Recommended Resources

- Books: 

  - "Introduction to Algorithms" by Cormen, Leiserson, Rivest, and Stein

  - "Data Structures and Algorithms Made Easy" by Narasimha Karumanchi

- Platforms for Practice: 

  - LeetCode, HackerRank, CodeSignal for algorithm challenges.