B.Sc. CSIT 3rd Semester – Data Structures and Algorithms (DSA) Complete Notes, Syllabus & Important Questions

Are you preparing for the B.Sc. CSIT 3rd Semester exam and looking for complete notes on Data Structures and Algorithms (DSA)? In this blog, you will find:

• 📘 Chapter-wise complete notes

• 📚 Full syllabus coverage (TU, 3rd Semester)

• 🎯 Important questions for each chapter

• 📝 Exam tips and preparation guide

This article is written in a student-friendly, human tone and is SEO-optimized, so you can easily understand concepts and revise quickly.

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📘 Course Overview

• Subject: Data Structures and Algorithms (DSA)

• Course Code: CSC206

• Credit Hours: 3 (Theory + Lab)

• Semester: 3rd (B.Sc. CSIT, Tribhuvan University)

Data Structures and Algorithms form the backbone of computer science. They help in solving problems efficiently, organizing data, and improving programming skills.

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📚 Chapter 1: Introduction to Data Structures and Algorithms

What is Data Structure?

A data structure is a method of organizing and storing data in a computer so that it can be used efficiently.

• Linear structures: Arrays, Linked Lists, Stacks, Queues

• Non-linear structures: Trees, Graphs

What is an Algorithm?

An algorithm is a step-by-step method to solve a problem.

Properties of a good algorithm:

• Input and Output

• Clear steps (definiteness)

• Must stop (finiteness)

• Practical and effective

Complexity of Algorithms

• Time Complexity: How long an algorithm takes

• Space Complexity: How much memory it uses

Asymptotic Notations:

• O (Big-O): Worst case

• Ω (Omega): Best case

• Θ (Theta): Average case

👉 Important Questions:

• Define Data Structure with examples.

• What are asymptotic notations? Explain.

• Differentiate between algorithm and program.

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📚 Chapter 2: Arrays and Strings

Arrays

An array is a collection of elements of the same type stored in consecutive memory locations.

Types of Arrays:

• One-dimensional array

• Two-dimensional array (matrix)

• Multi-dimensional array

Operations: Traversal, Insertion, Deletion, Searching, Sorting.

Strings

A string is a sequence of characters ending with a null character.

Common operations:

• Finding length

• Concatenation

• Comparison

• Substring extraction

👉 Important Questions:

• Differentiate between 1D and 2D arrays.

• Explain operations on arrays.

• What are string operations?

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📚 Chapter 3: Linked List

A linked list is a collection of nodes where each node contains data and a reference (pointer) to the next node. Unlike arrays, it is dynamic in size.

Types of Linked Lists

1. Singly Linked List – Each node points to the next node.

2. Doubly Linked List – Each node points to both previous and next nodes.

3. Circular Linked List – Last node connects back to the first node.

Operations on Linked Lists

• Traversal

• Insertion (beginning, middle, end)

• Deletion

• Searching

Advantages: Dynamic size, easy insertion and deletion.
Disadvantages: Extra memory for pointers, sequential access only.

👉 Important Questions:

• Differentiate between arrays and linked lists.

• Explain types of linked lists.

• Advantages and disadvantages of linked lists.

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📚 Chapter 4: Stack and Queue

Stack

A stack is a linear data structure that works on the LIFO (Last In, First Out) principle.

• Insertion = Push

• Deletion = Pop

Applications: Expression evaluation, undo operations, recursion.

Queue

A queue is a linear data structure that works on the FIFO (First In, First Out) principle.

Types of Queues:

• Linear Queue

• Circular Queue

• Double-ended Queue (Deque)

• Priority Queue

👉 Important Questions:

• Define stack and list its applications.

• Differentiate between stack and queue.

• Explain types of queues.

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📚 Chapter 5: Trees

A tree is a hierarchical data structure made up of nodes.

Types of Trees

• Binary Tree: Each node has at most two children.

• Binary Search Tree (BST): Left child < root < right child.

• AVL Tree: A self-balancing BST.

• Heap: Complete binary tree used in sorting.

Tree Traversals

• Inorder: Left → Root → Right

• Preorder: Root → Left → Right

• Postorder: Left → Right → Root

👉 Important Questions:

• Define Binary Tree and its properties.

• Explain different tree traversal methods.

• What is a Binary Search Tree?

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📚 Chapter 6: Graphs

A graph is a set of vertices (nodes) and edges (connections).

Graph Representation

• Adjacency Matrix

• Adjacency List

Graph Traversal

• Breadth-First Search (BFS): Level-wise traversal

• Depth-First Search (DFS): Deep traversal before backtracking

Applications: Networking, maps, shortest path algorithms.

👉 Important Questions:

• Define graph and explain its representation.

• Differentiate between BFS and DFS.

• Applications of graphs.

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📚 Chapter 7: Searching and Sorting

Searching Techniques

• Linear Search: Sequential checking.

• Binary Search: Efficient method for sorted data.

Sorting Techniques

• Bubble Sort – Swap adjacent elements

• Selection Sort – Select smallest and place in order

• Insertion Sort – Insert elements one by one in correct position

• Merge Sort – Divide and conquer method

• Quick Sort – Partition around a pivot

👉 Important Questions:

• Compare Linear and Binary Search.

• Explain Quick Sort.

• Differentiate between Merge Sort and Insertion Sort.

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📚 Chapter 8: Hashing and File Structures

Hashing

A method of mapping data using a hash function into a hash table.

Hashing Methods: Division, Mid-square, Folding.
Collision Handling: Chaining, Open Addressing.

File Structures

• Sequential file organization – Records stored one after another.

• Indexed file organization – Index for faster access.

• Hashed file organization – Records placed using hashing.

👉 Important Questions:

• What is hashing?

• Explain collision resolution methods.

• Differentiate between static and dynamic hashing.

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📝 Exam Preparation Tips

1. Focus on definitions, diagrams, and differences.

2. Revise tree traversals and graph algorithms multiple times.

3. Practice sorting and searching with dry runs.

4. Write answers in this format: Definition → Explanation → Example → Applications.

5. Solve old TU question papers.

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🎯 Final Words

The subject of Data Structures and Algorithms (DSA) may seem difficult at first, but once you understand the logic behind storing and processing data, it becomes very interesting. Regular revision and practice will help you score well in exams and also prepare you for future programming challenges.

This blog gives you complete notes + chapter-wise important questions for the B.Sc. CSIT 3rd Semester DSA course (TU syllabus). Use it for quick revision and structured study.

Good luck with your exams! 🚀