ERODE SENGUNTHAR

ENGINEERING COLLEGE
Autonomous
(Approved by AICTE, New Delhi, Permanently Affiliated to Anna University, Chennai&
Accredited by National Board of Accreditation (NBA), New Delhi,
National Accreditation Assessment Council, Bangalore& IE(I), Kolkata)
PERUNDURAI, ERODE-638 057

DEPARTMENT OF CSE(AI&ML)
LESSON PLAN

Name of the Subject with Code : 23CD301- Data Structures using C

Course Coordinator : N.Krishnaveni, AP / CSE(AI&ML)
Academic Year : 2025-26 /Odd Semester
Year/ Semester : II/ III
Branch : B.E.-CSE(Cyber Security)
Course Objectives:
The purpose of learning this course is to
• Apply the concepts of List ADT in the applications of various linear and nonlinear data structures.
• Demonstrate the understanding of stacks, queues and their applications.
• Apply the concepts of Linked List in the applications of various nonlinear data structures.
• Understand the implementation of graphs and their applications.
• Able to incorporate various sorting and hashing techniques in real time scenarios.
Course outcomes:
At the end of this course, learners will be able to
CO1: Learn how to use different types of lists (like arrays and linked lists) and apply them to problems like working with polynomials.
CO2: Apply stack and queue data structures to solve computational problems including expression evaluation, conversion, and real-world
queuing scenarios.
CO3: Explore different kinds of trees like binary trees and AVL trees, and use them to organize and process hierarchical data.
CO4: Use graph structures to model and solve problems involving connections, such as route finding and task scheduling.
CO5: Compare and apply different techniques to find, arrange, and store data efficiently using searching, sorting,
and hashing methods.

Actually No of
S. Planned Topic to be Methodo ICT Tools
conducted Key terms Objective periods
No. Date covered logy adopted used
date required
UNIT I - LINEAR DATA STRUCTURE – LIST
To know the syllabus
Course Web Resources, PPT
1 14.07.25 of Data structures using Lecturing 1
Orientation Assessment,
C, web resources, Software
Program Scope
Assessment procedure
Abstract Data Separating To define a model of a PPT
2 16.07.25 Types (ADTs), interface from data structure based on Lecturing 1
Software
List ADT implementation behavior
Use contiguous To store list elements
Array-based memory (array) for in a fixed-size PPT
3 18.07.25 Lecturing 1
implementation list implementation continuous memory Software
with fixed size block
Efficient insertion
and deletion To store elements
Linked list PPT
4 18.07.25 operations dynamically using Lecturing 1
implementation Software
without shifting nodes and pointers
elements
 Implement
dynamic linear To link nodes in one
Singly linked PPT
5 21.07.25 list with nodes direction using a Lecturing 1
lists Software
pointing to the pointer to the next node
next.
Implement
dynamic linear To link nodes in one
Singly linked PPT
6 23.07.25 list with nodes direction using a Lecturing 1
lists Software
pointing to the pointer to the next node
next.
Last node points
To link the last node
Circularly linked to the head, PPT
7 25.07.25 back to the first, Lecturing 1
lists enabling circular Software
forming a loop
traversal.
Nodes have two To allow bi-directional
Doubly-linked links: one to the traversal using pointers PPT
8 25.07.25 Lecturing 1
lists next and one to to both next and Software
the previous node. previous nodes
Applications of
lists, Polynomial
To create and modify
Manipulation – Insert and delete Assignme
9 28.07.25 polynomials using list - 1
All operations polynomials nt
structures
(Insertion,
Deletion)
Polynomial
Manipulation – To combine two
Add and show PPT
10 30.07.25 All operations polynomials into one Lecturing 1
Polynomials Software
(Merge, by adding like terms.
Traversal)
Content beyond
the Syllabus: Best case,
Algorithms To understand the PPT
11 01.08.25 Average case, Lecturing 1
notations- Time efficiency of algorithm Software
Worst case
complexity
Number of hours specified in the Syllabus: 9
Number of Hours Planned: 10
Number of hours actually conducted:10
Reasons, if any deviation
UNIT II LINEAR DATA STRUCTURES – STACKS, QUEUES
To store and manage
01.08.25 Last In First Out data using the Last In PPT
12 Stack ADT Lecturing 1
(LIFO) Order First Out (LIFO) Software
method
To understand how to
04.08.25 Stack ADT- use a stack with PPT
13 Push,Pop,Peek Lecturing 1
Operations operations like push(), Software
pop(), and peek()
Applications -
To solve math
06.08.25 Evaluating Operator PPT
14 expressions using Lecturing 1
arithmetic precedence Software
stacks
expressions
Conversion of To change a math
08.08.25 Precedence & PPT
15 Infix to postfix expression from infix to Lecturing 1
associativity Software
expression postfix using a stack

To manage data using

08.08.25 FIFO (First In PPT
16 Queue ADT the First In First Out Lecturing 1
First Out) Software
(FIFO) method
Enqueue()
To perform actions
Dequeue()
11.08.25 Queue ADT- like enqueue() (add)
17 Front() Lecturing Blackboard 1
Operations and dequeue()
isEmpty()
(remove) on a queue
isFull()
 Last position is
To make better use of
13.08.25 connected back to
18 Circular Queue memory in a queue by Lecturing Blackboard 1
the first, forming
reusing empty spaces
a circle
To allow insertion and
18.08.25 Insert/Delete from
19 DEQUE deletion from both Seminar - 1
both ends
ends of the queue
CPU scheduling,
To use queues in real-
Printer queue,
18.08.25 Applications of world scenarios like PPT
20 Customer service Lecturing 1
queues scheduling and Software
systems,Breadth
buffering
First Search
Content beyond
To implement multiple
20.08.25 the Syllabus: PPT
21 Array Partitioning stacks using a single Lecturing 1
Implementation Software
array
of Multi-Stack
Number of hours specified in the Syllabus: 9
Number of Hours Planned: 10
Number of hours actually conducted:
Reasons, if any deviation
UNIT III - NONLINEAR DATA STRUCTURE –TREES
Node, Root,
22.08.25 Tree Parent, Leaf, To understand basic PPT
22 Lecturing 1
Terminologies child, height, words related to trees Software
depth, subtree

22.08.25 Binary Tree– Maximum 2 To learn how to store a PPT

23 Lecturing 1
Representation children binary tree in memory Software
Inorder – Left,
Root, Right
25.08.25 Preorder – Root, To visit every node in a PPT
24 Tree traversals Lecturing 1
Left, Right tree in a specific order Software
Postorder – Left,
Right, Root
Operators =
To represent and
08.09.25 Internal nodes PPT
25 Expression trees evaluate arithmetic Lecturing 1
Operands = Leaf Software
expressions using a tree
nodes
To store data so that it
10.09.25 Binary Search Left < Root <
26 is easy to search, insert, Lecturing Blackboard 1
Tree Right
and delete
Balanced BST
12.09.25 To keep the BST
27 AVL Trees Height difference Lecturing Blackboard 1
balanced automatically
≤1
Uses rotations To move recently
28 12.09.25 Splay Trees (Zig, Zig-Zag, accessed items to the Surprise Test - 1
Zig-Zig) top of the tree
Min-Heap: Parent To create a complete
15.09.25 ≤ Children binary tree used for PPT
29 Binary Heap Lecturing 1
Max-Heap: Parent priority-based Software
≥ Children operations
Priority Queue,
Heap Sort,
15.09.25 Binary Heap - Job Scheduling, To use heaps in real- PPT
30 Lecturing 1
Applications Dijkstra’s world applications Software
Algorithm
(shortest path)
To implement and
Type of self-
17.09.25 Content beyond understand the PPT
31 balancing binary Lecturing 1
the Syllabus: operations of a Red- Software
search tree
Red-Black Trees Black Tree
Number of hours specified in the Syllabus: 9
Number of Hours Planned: 10
Number of hours actually conducted:
Reasons, if any deviation
UNIT IV - NONLINEAR DATA STRUCTURE –GRAPH
To understand the basic
19.09.25 Graph Vertex,edge,degree, building blocks and PPT
32 Lecturing 1
Terminologies path definitions used in Software
graph theory.

Adjacency To learn how graphs

19.09.25 Representation of PPT
33 Matrix, can be represented in Lecturing 1
Graph Software
Adjacency List memory for processing.

Undirected To classify graphs

22.09.25 Graph, Weighted based on direction, Case
34 Types of graph - 1
Graph, Cyclic / weight, and Study
Acyclic Graph connectivity.
To explore a graph
24.09.25 Breadth-first Level by level, PPT
35 level by level using the Lecturing 1
traversal Uses queue Software
BFS algorithm.
To explore a graph
26.09.25 Depth-first Goes deep first, deeply along each PPT
36 Lecturing 1
traversal Uses stack branch using DFS Software
algorithm.
To order the vertices of
a Directed Acyclic
26.09.25 Graph (DAG) so that PPT
37 Topological Sort Ordering of tasks Lecturing 1
all dependencies are Software
satisfied.
To find the shortest
Shortest path - Finds shortest
06.10.25 distance from a source PPT
38 Dijikstra's path in weighted Lecturing 1
node to all other nodes Software
Algorithm graph
in a weighted graph.
To connect all nodes in
08.10.25 Minimum Connects all a graph with the PPT
39 Lecturing 1
Spanning Tree vertices, no cycles minimum possible total Software
edge weight.
To build a Minimum
Adds nearest Spanning Tree by
10.10.25 PPT
40 Prim's Algorithm neighbor expanding from the Lecturing 1
Software
(minimum edge) starting node to nearest
unvisited nodes.
To implement the
Content beyond Floyd–Warshall
10.10.25 the Syllabus: All-Pairs Shortest PPT
41 algorithm to find the Lecturing 1
Floyd–Warshall Path Software
shortest paths between
Algorithm all pairs of vertices
Number of hours specified in the Syllabus: 9
Number of Hours Planned: 10
Number of hours actually conducted:
Reasons, if any deviation
UNIT V - SEARCHING, SORTING AND HASHING TECHNIQUES
To search for an
13.10.25 Searching- Linear element in an unsorted PPT
42 Checks each item Lecturing 1
Search list by checking each Software
element sequentially.
To efficiently search
15.10.25 PPT
43 Binary Search Sorted data only for an element in a Lecturing 1
Software
sorted list
To sort elements by
17.10.25 Sorting - Bubble Compare & swap PPT
44 repeatedly swapping Lecturing 1
sort neighbors Software
adjacent elements
 To sort elements by
repeatedly selecting the
17.10.25 Finds min & PPT
45 Selection sort minimum (or Lecturing 1
places Software
maximum) from
unsorted part

To sort elements by
24.10.25 Inserts in correct inserting each new Online
46 Insertion sort - 1
place element into its correct Test
position
Gap-based
24.10.25 To sort data using gap- PPT
47 Shell sort insertion, Reduces 1
based insertion Lecturing Software
comparisons
To sort data by
27.10.25 Pivot-based PPT
48 Quick sort partitioning the array Lecturing 1
partitioning Software
around a pivot
To sort data using a
29.10.25 PPT
49 Merge Sort. Divide and merge divide-and-conquer Lecturing 1
Software
approach
Converts key to To map keys to indices
31.10.25 Hashing- Hash PPT
50 index in a hash table using a Lecturing 1
Functions Software
Fast lookup deterministic function.
Collision resolution To resolve hash
31.10.25 strategies- Separate Multiple keys per collisions by using PPT
51 Lecturing 1
Chaining slot linked lists at each Software
index.
To resolve hash
Finds next empty
03.11.25 Open Addressing , collisions by probing PPT
52 slot, Resize hash Lecturing 1
Rehashing. for next available slot Software
table
in the array.
To implement Radix
Content Beyond Digit-by-Digit Sort and demonstrate PPT
53 03.11.25 the Syllabus: Lecturing 1
Sorting how it efficiently sorts Software
Radix Sort integers
Number of hours specified in the Syllabus: 9
Number of Hours Planned: 11
Number of hours actually conducted:
Reasons, if any deviation
Total no. of Hours 53
Total hours prescribed in the syllabus 45

TENTATIVE DATES OF EVENTS:

Reopening : 14.07.2025
Last Working Day : 21.11.2025
Internal Assessment Test I : 01.09.2025 to 09.09.2025
Internal Assessment Test II : 05.11.2025 to 14.11.2025
TEXT BOOKS:
1. Michael T. Goodrich, Roberto Tamassia, and Michael H. Goldwasser, “Data Structures & Algorithms in
Python”, An Indian Adaptation, John Wiley & Sons Inc., 2021
2. Mark Allen Weiss, Data Structures and Algorithm Analysis in C, 2nd Edition, Pearson Education, 2016.
3. Reema Thareja, Data Structures Using C, Second Edition, Oxford University Press, 2014.

REFERENCES
1. Narasimha Karumanchi,”Data Structures and Algorithms Made Easy:Data Structures and Algorithmic Puzzles”,
5th Edition, Career Monk, 2016.
2. Debasis Samanta,“Classic Data Structures”, Prentice Hall of India, 2nd Edition, 2014.
3. Seymour Lipschutz,“Data Structures by Schaum Series”,2nd Edition,Tata McGraw Hill, 2013.
4. Thomas H. Cormen, Charles E. Leiserson, Ronald L.Rivest and Clifford Stein, Introduction to Algorithms,
Second Edition, McGraw Hill, 2002.
WEB LEARNING RESOURCES:
1. https://onlinecourses.nptel.ac.in/noc25_cs81/preview, Prof. Nitin Saxena, IIT Kanpur

COURSE COORDINATOR HoD/CSE(AI&ML) PRINCIPAL