CSE 2101: Data Structures

A comprehensive repository for the university course on Data Structures

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📚 Course Overview

Course Code: CSE 2101
Course Title: Data Structures
Credit Hours: 3.00 (3 hours per week)
Textbook: Data Structures with C by Seymour Lipschutz (Schaum's Outlines)

This repository contains comprehensive materials, implementations, and resources for the Data Structures course. The course provides an in-depth study of fundamental and advanced data structures, their operations, and algorithmic analysis.

🗺️ Course Roadmap

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        C4[Arrays & Records]:::topic --> C5[Linked Lists]:::topic
        C5 --> C6[Stacks & Queues]:::topic
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    subgraph P3 [Phase 3: Advanced Structures]
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        C7[Trees & Heaps]:::topic --> C8[Graphs]:::topic
        C8 --> C9[Sorting & Searching]:::topic
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🧠 Course Topics

• Internal data representation
• Abstract data types
• Elementary asymptotic analysis: growth of functions, O, Ω and Θ notations
• Elementary data structures: arrays, linked lists, stacks, queues, trees and tree traversals, graphs and graph representations, heaps, binary search trees
• Sorting algorithms: heap sort, merge sort, quick sort
• Data structures for set operations
• Advanced data structures: balanced binary search trees (AVL trees, red-black trees, splay trees, skip lists), advanced heaps (Fibonacci heaps, binomial heaps)
• Hashing

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    Linear --> St[Stacks]:::sub
    Linear --> Qu[Queues]:::sub
    
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    Trees --- Heaps[Heaps]:::sub
    
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    Graphs --- Undir[Undirected]:::sub

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👨‍🏫 Instructors

Md. Jahidul Islam
Chairman, Department of Computer Science and Engineering
Chandpur Science and Technology University

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📋 Course Content

1. 📘 INTRODUCTION AND OVERVIEW (1.1-1.19)

Click to view topics

• 1.1 Introduction
• 1.2 Basic Terminology; Elementary Data Organization
• 1.3 Data Structures
• 1.4 Data Structure Operations
• 1.5 Algorithms: Complexity, Time-Space Tradeoff

2. 📐 PRELIMINARIES (2.1-2.31)

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• 2.1 Introduction
• 2.2 Mathematical Notation and Functions
• 2.3 Algorithmic Notation
• 2.4 Control Structures
• 2.5 Complexity of Algorithms
• 2.6 Other Asymptotic Notations (Ω, O, Θ)
• 2.7 Subalgorithms
• 2.8 Variables, Data Types

3. 🧵 STRING PROCESSING (3.1-3.31)

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• 3.1 Introduction
• 3.2 Basic Terminology
• 3.3 Storing Strings
• 3.4 Character Data Type
• 3.5 String Operations
• 3.6 Word Processing
• 3.7 Pattern Matching Algorithms

4. 🔢 ARRAYS, RECORDS AND POINTERS (4.1-4.56)

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• 4.1 Introduction
• 4.2 Linear Arrays
• 4.3 Representation of Linear Arrays in Memory
• 4.4 Traversing Linear Arrays
• 4.5 Inserting and Deleting
• 4.6 Sorting; Bubble Sort
• 4.7 Searching; Linear Search
• 4.8 Binary Search
• 4.9 Multidimensional Arrays
• 4.10 Pointers; Pointer Arrays
• 4.11 Records; Record Structures
• 4.12 Representation of Records in Memory; Parallel Arrays
• 4.13 Matrices
• 4.14 Sparse Matrices

5. 🔗 LINKED LISTS (5.1-5.57)

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• 5.1 Introduction
• 5.2 Linked Lists
• 5.3 Representation of Linked Lists in Memory
• 5.4 Traversing a Linked List
• 5.5 Searching a Linked List
• 5.6 Memory Allocation; Garbage Collection
• 5.7 Insertion into a Linked List
• 5.8 Deletion from a Linked List
• 5.9 Header Linked Lists
• 5.10 Two-way Lists

6. 📚 STACKS, QUEUES, RECURSION (6.1-6.66)

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• 6.1 Introduction
• 6.2 Stacks
• 6.3 Array Representation of Stacks
• 6.4 Linked Representation of Stacks
• 6.5 Arithmetic Expressions; Polish Notation
• 6.6 Quicksort, an Application of Stacks
• 6.7 Recursion
• 6.8 Towers of Hanoi
• 6.9 Implementation of Recursive Procedures by Stacks
• 6.10 Queues
• 6.11 Linked Representation of Queues
• 6.12 Deques
• 6.13 Priority Queues

7. 🌲 TREES (7.1-7.101)

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• 7.1 Introduction
• 7.2 Binary Trees
• 7.3 Representing Binary Trees in Memory
• 7.4 Traversing Binary Trees
• 7.5 Traversal Algorithms using Stacks
• 7.6 Header Nodes; Threads
• 7.7 Binary Search Trees
• 7.8 Searching and Inserting in Binary Search Trees
• 7.9 Deleting in a Binary Search Tree
• 7.10 AVL Search Trees
• 7.11 Insertion in an AVL Search Tree
• 7.12 Deletion in an AVL Search Tree
• 7.13 m-way Search Trees
• 7.14 Searching, Insertion and Deletion in an m-way Search Tree
• 7.15 B Trees
• 7.16 Searching, Insertion and Deletion in a B-tree
• 7.17 Heap; Heapsort
• 7.18 Path Lengths; Huffman's Algorithm
• 7.19 General Trees

8. 🕸️ GRAPHS AND THEIR APPLICATIONS (8.1-8.47)

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• 8.1 Introduction
• 8.2 Graph Theory Terminology
• 8.3 Sequential Representation of Graphs; Adjacency Matrix; Path Matrix
• 8.4 Warshall's Algorithm; Shortest Paths
• 8.5 Linked Representation of a Graph
• 8.6 Operations on Graphs
• 8.7 Traversing a Graph
• 8.8 Posets; Topological Sorting

9. ⚡ SORTING AND SEARCHING (9.1-9.27)

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• 9.1 Introduction
• 9.2 Sorting
• 9.3 Insertion Sort
• 9.4 Selection Sort
• 9.5 Merging
• 9.6 Merge-Sort
• 9.7 Radix Sort
• 9.8 Searching and Data Modification
• 9.9 Hashing

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🚀 Getting Started

Prerequisites

To use the code examples in this repository, you need:

• C Compiler (GCC recommended)
• Text Editor or IDE (VS Code, Code::Blocks, Dev-C++, or any C IDE)
• Basic understanding of C programming

Installation

1. Clone the repository

   git clone https://github.com/M-F-Tushar/CSE-2101-Data-Structures.git
   cd CSE-2101-Data-Structures

2. Compile a C program

   gcc filename.c -o output
   ./output

3. Navigate to specific chapters

   cd Chapter-04-Arrays-Records-Pointers/examples

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💻 Usage

Each chapter folder contains:

• README.md: Detailed explanation of concepts covered in that chapter
• examples/: Working code examples demonstrating the concepts
• exercises/: Practice problems with solutions

Running Examples

# Navigate to a chapter
cd Chapter-05-Linked-Lists/examples

# Compile the program
gcc linked_list_operations.c -o linked_list

# Run the program
./linked_list

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🎯 Learning Outcomes

By the end of this course, students will be able to:

1. Understand and implement various data structures in C
2. Analyze algorithm complexity using Big O, Omega, and Theta notations
3. Choose appropriate data structures for specific problems
4. Implement searching and sorting algorithms
5. Work with advanced data structures like AVL trees, B-trees, and heaps
6. Apply graph algorithms for real-world problems
7. Design efficient algorithms with optimal time-space tradeoffs

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📚 Reference Materials

Primary Textbook

• Seymour Lipschutz - Data Structures with C (Schaum's Outlines)

Additional Resources

• Thomas H. Cormen et al. - Introduction to Algorithms (CLRS)
• Ellis Horowitz & Sartaj Sahni - Fundamentals of Data Structures in C
• Robert Sedgewick - Algorithms in C

Online Resources

• GeeksforGeeks - Data Structures
• Visualgo - Algorithm Visualizations
• Big-O Cheat Sheet

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🤝 Contributing

Contributions are welcome! Here's how you can help:

1. Fork the repository
2. Create a new branch (git checkout -b feature/improvement)
3. Make your changes
4. Commit your changes (git commit -m 'Add some feature')
5. Push to the branch (git push origin feature/improvement)
6. Open a Pull Request

Contribution Guidelines

• Follow the existing code structure and naming conventions
• Add comments to explain complex logic
• Test your code before submitting
• Update documentation if necessary
• Write clear commit messages

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📝 Course Progress

• Chapter 1: Introduction and Overview
• Chapter 2: Preliminaries
• Chapter 3: String Processing
• Chapter 4: Arrays, Records and Pointers
• Chapter 5: Linked Lists
• Chapter 6: Stacks, Queues, Recursion
• Chapter 7: Trees
• Chapter 8: Graphs and Their Applications
• Chapter 9: Sorting and Searching

Note: Currently, 4 chapters have been completed. The repository will be updated as the course progresses.

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📧 Contact

Mahir Faysal Tusher

• 📧 Email: www.mahirfaysaltushar@gmail.com
• 🐙 GitHub: @M-F-Tushar
• 📍 Location: Chandpur, Bangladesh

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🌟 Acknowledgments

• Seymour Lipschutz for the excellent textbook
• Course instructors and teaching assistants
• Fellow students for discussions and collaboration
• Open source community for inspiration

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📊 Repository Stats

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