🔰 1.

Introduction to Basic Concepts of Data Structures

 What are Data Structures?
Understanding the meaning and purpose of data structures in programming.
 Why do we need Data Structures?
Real-life applications and necessity in software development.
 Philosophy of Data Structures
Logic and principles behind using structured data storage.
 Elementary Data Organization
Basic ways to organize data: primitive types, arrays, linked structures, etc.
 Data Structure Selection & Algorithm Development
How to choose the right data structure for a problem and write algorithms accordingly.
 Data Structure Operations
Operations like insert, delete, search, traversal on different structures.
 Space-Time Complexity Analysis
How to analyze memory usage and time required by algorithms (Big-O notation basics).

💻 2. Object-Oriented Programming (OOP) Concepts in

Java
 Introduction to Java Programming
Basics of Java language and setting up Java environment.
 Objects and Classes
Core of OOP; defining blueprints (classes) and creating real-world instances (objects).
 Messages and Methods
Calling functions on objects using methods.
 OOP Principles:
o Encapsulation: Hiding data using access modifiers.
o Inheritance: Reusing code from one class to another.
o Polymorphism: Same method behaving differently in different contexts.
 Java Environment, Bytecode, and JVM (Java Virtual Machine)
How Java code is compiled and executed.
 Java Syntax Basics:
o Comments, Whitespaces
o Identifiers, Keywords, Reserved Words
o Variables and Data Types
o Operators (Arithmetic, Logical, Relational, etc.)
o Expressions, Escape Sequences
o Type Conversion and Type Casting
� 3. Application of Arrays
 Array as an Abstract Data Type (ADT)
Understanding arrays as structured data containers.
 Array Representation in Memory
How arrays are stored in memory.
 Array Operations
Insertion, Deletion, Traversal, and Searching.
 Multi-Dimensional Arrays
2D and 3D arrays: matrices and their usage.
 Records and Unions
Grouping data of different types using structures.
 Nested Structures
Structures inside structures.
 Arrays of Structures
Creating arrays of complex data types (like students, employees, etc.).

🔗 4. Linked Lists
 What are Linked Lists?
Dynamic memory-based lists with nodes and pointers.
 Types of Linked Lists:
o Singly Linked List
o Doubly Linked List
o Circular Linked List (if taught)
 Operations on Linked Lists:
o Traversal
o Searching
o Inserting a Node
o Deleting a Node
 Advantages & Disadvantages of Linked Lists
Compared to arrays in terms of memory, performance, etc.
📦 5. Stacks, Queues, and Recursion
 Stacks:
o Definition and real-life examples (Undo, Back Button, etc.)
o Stack Operations: Push, Pop, Peek
o Stack Implementation (Array and Linked List)
 Queues:
o Definition and Applications (Printer Queue, Customer Line)
o Queue Operations: Enqueue, Dequeue
o Circular Queue, Double-Ended Queue (if taught)
 Recursion:
o Recursive Function Basics
o When and Why to Use Recursion
o Applications (Factorial, Fibonacci, Tree Traversal, etc.)

🔍 6. Sorting, Searching, and Algorithm Efficiency

 Why is Sorting Important?
Helps in faster searching and better data organization.
 Elementary Sorting Algorithms:
o Selection Sort
o Insertion Sort
o Bubble Sort
 Advanced Sorting Algorithms:
o Quick Sort
o Merge Sort
o Heap Sort
 External Sorting
Sorting large files that don’t fit into memory.
 Searching Algorithms:
o Linear Search
o Binary Search
 Hashing
Introduction to hash functions and hash tables.
 Algorithm Efficiency Analysis:
o Time Complexity (Big-O, Big-Theta, Big-Omega)
o Best Case, Worst Case, and Average Case
o Control Structures and Runtime Impact
🌳 7. Trees and Graphs
 Tree Basics:
o Terminologies: Root, Leaf, Child, Parent, etc.
o Binary Tree: Properties and Types
o Binary Search Tree (BST)
o Binary Tree Traversal:
 Inorder
 Preorder
 Postorder
 Expression Trees
Using trees to represent algebraic expressions.
 Sorting with Binary Trees
How binary trees help in sorting elements.
 General Trees
Trees with more than two children per node.
 Graphs:
o Graph Terminologies
o Representation (Adjacency List & Matrix)
o Graph Traversals:
 Breadth-First Search (BFS)
 Depth-First Search (DFS)
 Shortest Path Algorithms
Basic intro to shortest path (e.g., Dijkstra’s Algorithm if taught)
📚 4-Month (16 Weeks) Study Plan
Routine: 5 din padhai (Mon–Fri), 2 din revision/practice (Sat–Sun)

� Week 1: Introduction to Data Structures

Day Topic

Day 1 What is Data Structure? Need, Uses, Real-life examples

Day 2 Types of Data Structures (Linear, Non-Linear, Static, Dynamic)

Day 3 Data Structure Operations (Traverse, Insert, Delete, Search, Sort)

Day 4 Algorithm Development, Role of Data Structures in Algorithms

Day 5 Time and Space Complexity (Big-O Notation Basics)

Sat–Sun 🔁 Revise Week 1 + Practice Questions

� Week 2: Java Basics

Day Topic

Day 6 Java Setup: JDK, IDE (IntelliJ/Eclipse), Hello World Program

Day 7 Java Syntax, Comments, Whitespaces, Identifiers

Day 8 Data Types, Variables, Constants, Type Casting

Day 9 Operators (Arithmetic, Relational, Logical)

Day 10 Control Structures: if, else, switch, while, for, do-while

Sat–Sun 🔁 Revise Java Basics + Small Code Practice

� Week 3: OOP Concepts in Java
Day Topic

Day 11 Classes, Objects, Methods (with examples)

Day 12 Encapsulation & Access Modifiers

Day 13 Inheritance & Polymorphism

Day 14 Constructors, Overloading, Overriding

Day 15 Java Environment: JVM, Bytecode, Class Libraries

Sat–Sun 🔁 Revise OOP + Practice Java Classes

� Week 4: Arrays
Day Topic

Day 16 Array Introduction, Declaration, Initialization

Day 17 Array Operations: Insert, Delete, Traverse, Update

Day 18 Multidimensional Arrays (2D Arrays)

Day 19 Records, Unions, Nested Structures

Day 20 Arrays of Structures in Java

Sat–Sun 🔁 Array Code Practice + MCQs

� Week 5: Linked Lists Basics
Day Topic

Day 21 Introduction to Linked Lists — Concept, Types (Singly, Doubly, Circular)

Day 22 Singly Linked List: Node Structure and Creation in Java

Day 23 Traversing a Singly Linked List

Day 24 Insertion in Singly Linked List (Beginning, End, Middle)

Day 25 Deletion in Singly Linked List (Beginning, End, Middle)

Sat–Sun 🔁 Revise + Practice Linked List coding problems

� Week 6: Linked Lists Advanced & Comparison

Day Topic

Day 26 Doubly Linked List: Structure and Operations

Day 27 Circular Linked List: Concept and Implementation

Day 28 Searching in Linked Lists

Day 29 Advantages & Disadvantages of Linked Lists vs Arrays

Day 30 Practical Implementation: Linked List Use Cases and Problems

Sat–Sun 🔁 Practice Linked List Problems + Revision

� Week 7: Stacks Introduction & Implementation
Day Topic

Day 31 Stack Concept, LIFO Principle, Applications

Day 32 Stack Implementation using Arrays in Java

Day 33 Stack Implementation using Linked Lists

Day 34 Stack Operations: Push, Pop, Peek

Day 35 Stack Applications: Expression Evaluation (Infix, Postfix)

Sat–Sun 🔁 Practice Stack Coding + Revision

� Week 8: Queues Introduction & Implementation

Day Topic

Day 36 Queue Concept, FIFO Principle, Applications

Day 37 Queue Implementation using Arrays

Day 38 Queue Implementation using Linked Lists

Day 39 Circular Queue and Dequeue (Double-ended Queue) Concepts

Day 40 Queue Applications and Problems

Sat–Sun 🔁 Practice Queue Coding + Revision

� Week 9: Recursion Basics & Practice
Day Topic

Day 41 What is Recursion? Base Case and Recursive Case

Day 42 Recursive Functions: Factorial, Fibonacci

Day 43 Recursive Traversal: Arrays and Strings

Day 44 Understanding Stack Frames in Recursion

Day 45 Practical Problems using Recursion

Sat–Sun 🔁 Practice Recursion Problems

� Week 10: Searching Algorithms

Day Topic

Day 46 Linear Search Algorithm: Concept & Implementation

Day 47 Binary Search Algorithm: Theory and Pre-requisites

Day 48 Binary Search Implementation in Java

Day 49 Practice Searching Problems

Day 50 Applications & Limitations of Searching Algorithms

Sat–Sun 🔁 Revise and Solve Searching Questions

� Week 11: Elementary Sorting Algorithms
Day Topic

Day 51 Bubble Sort: Algorithm and Code

Day 52 Insertion Sort: Concept and Implementation

Day 53 Selection Sort: Concept and Implementation

Day 54 Compare Bubble, Insertion, and Selection Sorts

Day 55 Practice Sorting Problems

Sat–Sun 🔁 Revision + Practice Coding

� Week 12: Advanced Sorting Algorithms

Day Topic

Day 56 Merge Sort: Divide and Conquer Approach

Day 57 Merge Sort Implementation in Java

Day 58 Quick Sort: Theory and Implementation

Day 59 Heap Sort: Concepts and Code

Day 60 External Sorting (Conceptual Overview)

Sat–Sun 🔁 Practice Advanced Sorting + Revision

� Week 13: Trees Introduction
Day Topic

Day 61 Trees Terminology: Nodes, Edges, Root, Leaves

Day 62 Binary Trees: Definition and Properties

Day 63 Binary Search Trees (BST)

Day 64 Tree Traversals: Preorder, Inorder

Day 65 Tree Traversals: Postorder

Sat–Sun 🔁 Practice Tree Traversal Problems

� Week 14: Graphs Introduction

Day Topic

Day 66 Graph Terminology: Vertices, Edges, Degree, Directed/Undirected

Day 67 Graph Representations: Adjacency List & Matrix

Day 68 Breadth First Search (BFS) Algorithm

Day 69 Depth First Search (DFS) Algorithm

Day 70 Shortest Path Basic Concepts

Sat–Sun 🔁 Practice Graph Traversals

� Week 15: Algorithm Analysis & Efficiency
Day Topic

Day 71 Big-O Notation: Detailed Understanding

Day 72 Time Complexity: Best, Worst, Average Cases

Day 73 Space Complexity

Day 74 Analyzing Algorithm Efficiency using Examples

Day 75 Practical Problems on Complexity Analysis

Sat–Sun 🔁 Revision + Practice Questions

� Week 16: Final Revision & Case Study

Day Topic

Day 76 Revision: Arrays + Linked Lists + Stacks + Queues

Day 77 Revision: Sorting + Searching + Recursion

Day 78 Revision: Trees + Graphs

Day 79 Case Study / Project Implementation

Day 80 Mock Exam / Practice Test

Sat–Sun 🔁 Final Revision & Doubt Clearing

📘 Subject Overview
 Title of Subject: Data Structures and Algorithms (BSSE201)
 Discipline: BS Software Engineering (SE) / Artificial Intelligence (AI) – 3rd Semester
 Effective: Batch 23 se aage walay students ke liye
 Pre-requisite: Koi bhi subject pehle zaroori nahi
 Assessment:
o Theory: 20% Sessional, 30% Midterm, 50% Final
o Practical: 50% Sessional, 50% Final Practical Exam
 Credit Hours: 03 (theory) + 01 (lab) = 4 credit hours total
 Contact Hours: 48 (theory) + 48 (lab)
 Total Marks: 100 (theory) + 50 (practical)

🎯 Course Learning Outcomes (CLOs)

Yeh outcomes batatay hain ke student ko course ke end par kya aana chahiye:

CLO Bloom’s Level

Kya seekhoge?
# (Taxonomy)
1 Arrays, lists, trees, graphs waghera ko classify karna C2
2 Har data structure ka related algorithm samajhna C4
Algorithm ka code likhna (programming mein implement
3 C3
karna)
4 Khud se problem-specific algorithms banana P4

🎓 Program Learning Outcomes (PLOs)

Ye wo skills hain jo university degree complete karte waqt aani chahiyein:
✅ PLO 3: Problem Analysis
✅ PLO 4: Design/Development of Solutions
✅ PLO 7: Communication
✅ PLO 9: Ethics
✅ PLO 10: Life-long Learning

(Baaki boxes khali hain, iska matlab wo is course mein cover nahi ho rahe.)

📚 Course Outline – Topics

1. Introduction
o Data Structures kya hoti hain, kyun zaroori hain
o Space-Time Analysis
o Algorithm Development basics
2. OOP Concepts and Java
o Java programming ka intro
o Objects, Classes, OOP Concepts (Encapsulation, Inheritance, Polymorphism)
o Java basics: variables, data types, expressions, type casting, etc.
3. Arrays
o Array as ADT
o 1D, 2D arrays
o Structures and Unions with arrays
4. Linked Lists
o Singly Linked List
o Node insert/delete, search, traversal
o Advantages of dynamic memory
5. Stacks, Queues & Recursion
o Stack & Queue definitions
o Arithmetic expressions
o Recursion: kab use karna hai aur kaise
6. Sorting & Searching + Algorithm Analysis
o Sorting: Bubble, Insertion, Selection, Quick, Merge, Heap
o Searching: Linear, Binary
o Big-O Notation, Run-time analysis
7. Trees & Graphs
o Binary Tree, BST
o Tree Traversals (Inorder, Preorder, Postorder)
o Graphs: BFS, DFS, Shortest Path

� Lab Work (Practical Topics)

Java mein following cheezein practice karni hain:

 If-else, loops
 Arrays
 Classes & Objects
 Linear & Binary Search
 Merge, Heap, Bubble, Quick, Insertion Sort
 Linked List
 Queues & Stacks
 Trees (Binary)
 Graphs
 Recursion
 Final Case Study (project or analysis)

📖 Recommended Books
1. Robert Lafore – Data Structures & Algorithms in Java
2. Mark Allen Weiss – Data Structures & Algorithm Analysis
3. Herbert Schildt – The Complete Reference: Java

✅ Approval History
 IAB Approval: 5 Jan 2023
 BoS Approval: 18 Jan 2023
 Faculty Approval: 22 Feb 2023