Desing_of_Data_Structures

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Desing_of_Data_Structures

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🎓 Design of Data Structures– Semester 3 💡 Theory + Lab course with C and Python implementations via Google Colab 📘 Topics: Arrays, Stacks, Queues, Linked Lists, Trees, Sorting, Hashing, Graphs 🧪 Includes 12 hands-on labs aligned with the official BTech syllabus 🚀 Learn and practice core data structures interactively!

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# 📘 Lesson Plan 
Module 1: Introduction to Data Structures

Lesson 1: Course Introduction, Importance of Data Structures

Lesson 2: Primitive & Non-Primitive Data Structures, Operations

Lesson 3: Arrays and Structures, Unions, Self-Referential Structures

Lesson 4: Pointers, Dynamic Memory Allocation

Lesson 5: Representation of Linear Arrays in Memory

Lesson 6: Algorithm Performance Analysis, Time & Space Complexity

Module 2: Stacks, Recursion, and Queues

Lesson 7: Stack Definition & Operations, Array Representation

Lesson 8: Stack using Dynamic Arrays

Lesson 9: Applications: Polish Notation, Infix to Postfix

Lesson 10: Evaluation of Postfix Expression

Lesson 11: Recursion: Factorial, GCD, Fibonacci

Lesson 12: Recursion: Tower of Hanoi

Lesson 13: Queues: Definition, Operations, Circular Queues

Lesson 14: Deque, Priority Queues

Module 3: Linked Lists

Lesson 15: Introduction & Memory Representation of Linked Lists

Lesson 16: Linked List Operations: Traverse, Search, Insert, Delete

Lesson 17: Circular, Doubly, and Header Linked Lists

Lesson 18: Linked Stacks and Queues

Lesson 19: Applications of Linked Lists

Module 4: Searching and Sorting

Lesson 20: Interpolation Search

Lesson 21: Selection, Insertion, and Bubble Sort

Lesson 22: Quick Sort and Merge Sort

Lesson 23: Radix Sort

Lesson 24: Review & Practice Problems

Module 5: Trees

Lesson 25: Binary Trees, Properties, Array/Linked Representation

Lesson 26: Tree Traversals: Inorder, Preorder, Postorder

Lesson 27: Binary Search Trees: Insertion, Deletion, Search

Module 6: Balanced Trees and Hashing

Lesson 28: AVL Trees and Red-Black Trees: Operations

Module 7: Hashing

Lesson 29: Hashing Techniques, Static vs Dynamic, Collision Resolution
    
Module 8: Graphs

Lesson 30: Graph Representations & Traversals (BFS, DFS)

## 🧪 Lab Problem Highlights

Official lab problems aligned with BTech Semester 3 syllabus (303105202) and supported by concepts from 303105201 – Design of Data Structures.
| 💻 Lab No. | 🔧 Problem Statement                                                                                             |
| ---------- | ---------------------------------------------------------------------------------------------------------------- |
| 1          | Implement **Stack** and its operations: `push`, `pop`, `peek`, `traverse`, `search` using linear data structures |
| 2          | Convert **Infix to Postfix** expression using Stack                                                              |
| 3          | Evaluate a **Postfix Expression** using Stack                                                                    |
| 4          | Solve **Towers of Hanoi** problem using Stack/Recursion                                                          |
| 5          | Implement **Queue** with operations: `enqueue`, `dequeue`, `traverse`, `search`                                  |
| 6          | Create and manipulate a **Singly Linked List**: `insert`, `delete`, `search`, `reverse`, `traverse`              |
| 7          | Implement a **Doubly Linked List** with full operations                                                          |
| 8          | Implement **Binary Search** and **Interpolation Search**                                                         |
| 9          | Implement **Sorting Algorithms**: Bubble, Selection, Insertion, Quick, Merge                                     |
| 10         | Build and manage a **Binary Search Tree (BST)**: `create`, `insert`, `delete`                                    |
| 11         | Perform **Tree Traversals** on BST: Inorder, Preorder, Postorder                                                 |
| 12         | Implement **Graphs** using adjacency list and matrix, and perform **BFS** and **DFS** traversals                 |

### 🎯 **Course Outcomes**

* 🧠 Understand core **data structure concepts** and algorithm efficiency
* 🛠️ Implement data structures using **Python, C, C++, and Java**
* 🔍 Analyze **time and space complexity** of programs
* 🧪 Gain practical skills through **12 hands-on lab experiments**
* 🌳 Work with **trees, graphs, hashing, and sorting techniques**
* 💻 Use **Google Colab** for coding, testing, and experimentation
* 🚀 Apply data structures to **real-world problem solving & interviews**

### 👥 **Who This Course Is For**

* 🎓 **BTech / BE / Engineering students** studying Computer Science, IT, or related branches
* 🧑‍💻 **Beginners in programming** who want a strong foundation in data structures
* 📘 **Students preparing for exams** based on the official BTech syllabus
* 🧪 Learners who prefer **hands-on, lab-based learning** with real implementations
* 🚀 **Aspiring software developers** and **competitive programmers**
* 💼 Students preparing for **technical interviews** and coding tests
* 🔄 Learners who want to practice **Python, C, C++, and Java** in one course

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