Presentation Title: 1 - Introduction to Data Structures (Beginner) Description: This beginner-level PowerPoint presentation, "Introduction to Data Structures", is designed to provide a comprehensive and engaging introduction to one of the foundational topics in computer science: data structures. It serves as the first module in a larger series focused on understanding, analyzing, and implementing data structures using a beginner-friendly approach, making it suitable for high school students, college freshmen, or self-learners starting their programming journey. The presentation begins by explaining the importance of data structures in solving computational problems efficiently. It sets the stage by highlighting how data structures form the backbone of every software system — from mobile apps and websites to databases and operating systems. The core sections of the presentation are organized to gradually build foundational knowledge: What is a Data Structure? A clear definition is provided, explaining that a data structure is a method of organizing and storing data to perform operations efficiently. Real-life analogies, such as organizing books in a library or names in a phone book, are used to make the concept relatable. Classification of Data Structures: The presentation categorizes data structures into two main types: Primitive Data Structures (int, float, char, boolean) Non-Primitive Data Structures, which are further divided into: Linear Data Structures (Arrays, Linked Lists, Stacks, Queues) Non-Linear Data Structures (Trees, Graphs) Characteristics of Data Structures: Key characteristics such as memory usage, time complexity, data access patterns, and abstraction are introduced in simple terms to help learners appreciate the trade-offs involved in choosing the right structure. Need for Data Structures: This section explains the practical necessity of data structures in real-world programming. Topics include performance optimization, data management, and algorithm support. Examples from search engines, social media platforms, and game development demonstrate the role data structures play behind the scenes. Introduction to Algorithm Analysis: A brief mention of algorithm analysis (time and space complexity) is included to prepare students for future topics like Big O notation, which will be explored in later presentations. Common Operations: The basic operations on data structures — insertion, deletion, traversal, searching, and sorting — are introduced to help students understand what can be done with different structures and why these operations matter. Comparison Table of Basic Structures: A visual comparison of arrays, linked lists, stacks, and queues is presented, highlighting differences in usage, performance, and flexibility. Conclusion and What’s Next: The presentation wraps up with a summary and a sneak peek into upcoming topics such as arrays and linked lists, setting a logical flow for the continuation of the series.

1. DATA STRUCTURES
SUBJECT CODE -18B11CI512
L-T scheme: 3-0-0 and Credits: 3

2. Prerequisite
Students must have already registered for the course, “Software
Development Fundaments” (18B11CI111).
Scope and Objectives:
This course develops:
 problem solving ability using programming
 ability to express solutions to problems clearly and precisely
 ability to design and analyze algorithms
 introduces with fundamental data structures
 Strengthen ability to design and evaluate ADTs, nonlinear
temporary and persistent data structures and
related algorithms.

3. Course Outcome
Course Outcome Description
CO1 List various types of data structures with respect to their
requirements in different fields.
CO2 Describe the various methods to evaluate the algorithms.
CO3 Develop algorithms based on linear and non-linear data structures
CO4 Identify the suitability of the data structures and algorithms as per
the requirements.
CO5 Apply data structures to develop efficient algorithms.
CO6 Demonstrate the learning of the course to solve the real life
programming problems.
Learning Outcome:
The students shall acquire the generic skills to design and
implement data structures and related algorithms for a
broad-based set of computing problems.

4. Syllabus
UNIT 1: Introduction to Data Structures, Algorithm and Complexity
Data structure overview, need of data structure and how to select relevant data
structure for given problem, basic C data types and ADT. Algorithm overview and
its properties, problem analysis and construction of algorithm, difference among
algorithm, program, and software, algorithm analysis and complexity, asymptotic
notations to represent the time complexity.
UNIT II: Searching and Sorting
Overview, memory representation of 1D and 2D array, sparse matrix, operation
supported by an array, Linear search, binary search (iterative), binary search
(recursive), Sorting, Types of sorting algorithms, bubble sort, selection sort,
insertion sort, quick sort, merge sort, heap sort, radix sorting, counting sorting,
Bucket Sorting.

5. Syllabus
UNIT III: Linear data structures
Linked list, types of linked list, and operations on linked lists. Stack overview, stack
implementation using array and linked list, basic operations on stack, applications of
stack – evaluation of mathematical expression, conversion of expression from one
form to another (Polish Notation), Tower of Hanoi problem. Queue overview, basic
operations on queue – enqueue, dequeue, implementation of queue using array and
linked list, types of queue - linear queue, circular queue.
UNIT IV: Non-linear data structures
Tree definition and its terminology, representation of graph using array and linked
list, tree traversals – preorder, inorder and postorder, binary search tree (BST) with
insertion, deletion and searching operations, extended binary tree and its application
in Huffman tree,
Introduction to graph, types of graph, traversal algorithms in graph – breadth first
search, depth first search.
Unit V: Heap, Priority Queues, B-Tree, AVL, Splay Tree, Red-Black Tree, Threaded
Tree. Elementary Graph algorithms: Minimum spanning trees, Kruskal’s algorithm,
Prim’s algorithm. Single source shortest path, all pair shortest path.

6. Recommended Books
Text Books:
1. Sartaj Sahni, “Fundamentals of Data Structures”, Tata Mc Graw Hill, New York
2. Seymour Lipschutz., “Data Structures with C”, Schaum's Outline Series
3. Narasimha Karumanchi, “Data Structures and Algorithms” Made Easy
4. Corman et al: “Introduction to Computer Algorithms”, the Massachusetts institute of
Technology, Cambridge, Massachusetts
Reference Books:
5. Langsam, Augestein, Tenenbaum: Data Structures using C and C++
6. Weiss: Data Structures and Algorithm Analysis in C/C++
7. Samir K. Bandyopadhyay,” Data Structures using C”
8. Hopcraft, Ullman: Data Structures and Algorithms

7. Marks Distribution(Data Structures)
Component & Nature Duration Marks /
Weightage
T1 1 hr 15
T2 1&1/2 hrs 25
T3 2hrs 35
Tutorials 05
Attendance 05
Quiz 05
Assignments 10
Total 100

8. Prerequisite Knowledge
1. Basic programming constructs from C programming, like
data types, operators, decision control, loops, string and
functions etc.
2. Dynamic memory allocation
3. Pointers
4. Structures

9. Introduction
to
Data Structures

10. Why This Course?
Students will be able to write fast programs
Students will be able to solve new problems
Students will be able to evaluate the quality of a program (Analysis of Algorithms:
Running time and memory space )
Students will be able to give non-trivial methods to solve problems.
Your algorithm (program) will be faster than others.

11. Preface of Data structure
Data: simply a value or a set of values of different type which is called data types like string,
integer, char etc.
Structure: Way of organizing information, so that it is easier to use.
Data Structure ?
A data structure is a particular way of organizing data in a
computer memory so that it can be used efficiently.
Although computers can perform millions of mathematical computations per second, when
a problem gets larger and complicated, the performance can be an important consideration.
One of the most crucial aspects to how quickly a problem can be solved is how the data is
stored in memory.
Unorganized
room
Well
organized
room

12. Why Data Structures
Human requirement with computers are going to complex day by
day.
To solve the complex requirements in efficient way we need this
study.
 Provide fastest solution of human requirements.
Provide efficient solution of complex problem
Space
 Time

13. Classification of Data Structure: Classification 1
Primitive data structure/types: Basic data types available in programming
languages; examples: integers, floats ,doubles, etc.
Simple data structure: Simple data structure can be constructed with the help of
primitive data structures. Examples: Array and Linked list.
 Compound Data Structure: Compound data structure can be constructed with
the help of any one of the primitive or simple data structure and it is having a
specific functionality.

14. Classification of Data Structure: : Classification 2

15. Linear Vs Non-linear Data Structures
Linear Data Structures: A linear data structure traverses the data
elements sequentially, in which only one data element can directly be
reached. Ex: Arrays, Linked Lists
Non-Linear Data Structures: Every data item is attached to several
other data items. The data items are not arranged in a sequential
structure. Ex: Trees, Graphs

16. Operations on Data Structures
Basic operations –
1. Traversing – Accessing and processing record exactly once
2. Insertion – Adding new record to data structure
3. Deletion – Removing particular record from the data structure
4. Searching – Finding the location of the record in data structure
Special operations –
1. Sorting – Arranging the record in some specific order
2. Merging – Combining the records from different data structures
to a single data structure

18. Commonly used data structures
1. Array
2. Linked list
3. Stack
4. Queue
5. Tree
6. Graph
Note: No single data structure works well for all purposes, so need to
learn several of them:

25. Abstract Data Type (ADT)
A data type is considered abstract when it is defined in terms of operations on
it, and its implementation is hidden. i.e. an ADT is implementation independent.
Hence, in case of ADT we know what it does, but not necessarily how it does.
Examples of ADT:
1. Stack: operations are “Push: insert an item onto the stack", “Pop: delete an
item from the stack", "ask if the stack is empty"; stack implementation may be
done using array or linked list or any other data structure.
2. Queue: operations are “Enqueue: add to the end of the queue", “Dequeue:
delete from the beginning of the queue", "ask if the queue is empty"; The
implementation may be as array or linked list or heap.

27. Cost of Data Structures
Cost of data structure or algorithm is always calculated in terms of following
parameters –
1. Time Complexity: Time required to perform a basic operation on data
2. Space Complexity: Memory space required to store the data items of data
structure
3. Programming efforts to write code for basic operations.

28. Space – Time Tradeoff
Reduction of required memory space could increase the time of execution.
Example –
1. Compressed and uncompressed data –
A space–time tradeoff can be applied to the problem of data storage. If data is stored
uncompressed, it takes more space but less time than if the data were stored compressed
(since compressing the data reduces the amount of space it takes, but it takes time to run
the decompression algorithm).
Depending on the particular instance of the problem, either way is practical.
2. Smaller code and loop unrolling –
 Larger code size can be traded for higher program speed when applying loop unrolling (A
basic compiler optimization is known as 'loop unrolling. Loop unrolling means going back
to some previous statement.)
 This technique makes the code longer for each iteration of a loop, but saves the
computation time required for jumping back to the beginning of the loop at the end of
each iteration.

30. Selection of data structure
 Organization of data in memory affects the performance of algorithm
 So, selection of data structure plays important role to make algorithm efficient
But, it depends on the nature of problem and operations to be supported.
Typically, following steps are to be followed to select an efficient data structure –
1. Analyze the problem very carefully and estimate the resources so that solution
must meet.
2. Determine the basic operation to be supported
3. Select the best data structure which meets the above requirements.