Data Structure and Algorithm

Instructor: Hafiz Abdul Rehman

Lecture 01: Introduction

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About Me
• MS (CS)
• COMSAT University Islamabad (Lahore Campus)
• 2020-2022
• Research Officer
• AL-Khwarizmi Institute of Computer Science (KICS) UET
Lahore
• 2019-2023
• Lecturer 2 2
• Computer Science Department, SST
About Me
• Instructor: Hafiz Abdul Rehman
• Email: hafiz.rehman@umt.edu.pk
• Office: Room No 404, STD
• Google Scholar Profile: Hafiz Abdul Rehman
• GitHub Profile: Hafiz Abdul Rehman
• LinkedIn Profile: https://www.linkedin.com/in/ha-rehman/

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About Me : Research Profile

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Course Details
• Course Code: CC2042
• Assessment
• 25% Coursework
• 25% Midterm,
• 30% Final
• 20% Project
• Office Hours: 10:00am – 12:00pm or When Door Open
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Textbooks - DSA

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Pre Requisites - DSA

Good Programming Skills

Basic Knowledge of OOP

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Course Outline
• Introduction to DSA
• Operations of Data Structures
• Types of Algorithms
• Arrays
• Dynamic Memory Allocation of Arrays
• Queue and Stack
• Searching and Sorting Algorithms
• Link List (Single, Double, and Circular)
• Operations of Data Structures
• Trees, Graphs 8 8
• Heap and Hashing
Data Structure
• A data structure is a fundamental concept in computer science
and programming that defines how data is organized, stored, and
manipulated in a computer's memory or storage.
• It provides a way to efficiently manage and access data, enabling
various operations and algorithms to be performed on that data.
• Data structures are essential for solving complex problems and
optimizing the use of computer resources.
• A data structure is a collection of basic data types combined
according to the rules of the programming language to create a
new user-defined data types. 9 9
Operations on Data Structures
The data in a data structure is processed using certain operations.

Inserting: Adding new data items into a data structure is called inserting.
Deleting: Removing new data items into a data structure is called
Deleting.
Searching: Finding specific data items in a data structure is called
Searching.
Traversing: Accessing each record or item in a data structure exactly once
for processing is called traversing. It is also called visiting.
Sorting: Arranging data items in a data structure into a specific order is
called sorting. 10 10
Merging: Combining two lists of data items into a single data list is called
merging.
 Types of Data Structures

Data Structure

Primitive Data Types Non-Primitive Data Types

Linear Data Non-Linear Data

Integer Float Character Boolean
Structure Structures

Trees Graphs

Arrays Link List Stack Queue

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Primitive and Non-Primitive Data Types
Primitive Data Types
• Fundamental building blocks for representing simple values
• Built into the programming language itself
• Used to represent basic data, such as numbers, characters, and
Boolean values.
• Primitive data types are directly supported by the hardware.
• They have fixed sizes, which means they use a specific amount of
memory.
• E.g. Integer contain 4 bytes and Boolean Contain 1 bit.

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Primitive and Non-Primitive Data Types
Non-Primitive Data Types
• Also known as reference data types
• Not store the actual data directly but instead store references
(memory addresses) to the data.
• Typically created by the programmer and are not fixed in size.
• Common examples of non-primitive data types include:
• Arrays: Ordered collections of elements of the same or different
data types.
• Objects: Instances of user-defined classes that can encapsulate data
and behavior. 13 13

• Strings: A sequence of characters. In some languages, strings are

treated as non-primitive data types.
Linear and Non-Linear Data Structures
These categories describe how data elements are organized, stored, and
accessed within the data structure.
Linear Data structures
• Data elements are organized sequentially one after the other.
• Each element has a unique predecessor and successor, except for the
first and last elements.
• Linear or one-dimensional arrangement of elements.
Non-Linear Data Structure
• Data elements are organized in a hierarchical or non-sequential
manner.
• Elements may have multiple predecessors or successors.
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• More complex and versatile compared to linear data structures.
Contiguous and Non- Contiguous Data Structures
Contiguous Data Structures
• data elements are stored in memory as a continuous block or
sequence.
• element follows the previous one without any gaps or spaces

Non-contiguous Data Structures

• data elements are not stored as a continuous sequence in memory.
• they may be scattered or dispersed across memory.
• provide more flexibility in terms of memory allocation and dynamic
resizing
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Contiguous and Non- Contiguous Data Structures
Contiguous Data Structures
• data elements are stored in memory as a continuous block or
sequence.
• element follows the previous one without any gaps or spaces

Non-contiguous Data Structures

• data elements are not stored as a continuous sequence in memory.
• they may be scattered or dispersed across memory.
• provide more flexibility in terms of memory allocation and dynamic
resizing
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Algorithms
• step by step procedure to solve a particular problem is called
algorithm
• algorithms notations are used to represent different steps
• It is the general solution of the problem.
• After solving the general problem, the program is written in a
programming language
• Each Program have the time complexity, memory consumption and
efficiency.

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Classification of Algorithms
• Recursion or Iteration: Calls itself repeatedly until a base condition
is satisfied
• Procedural: we have to specify the exact steps to get the result
• Declarative (non-Procedural): we say what we want without having
to say how to do it.
• Serial: execute one instruction at a time
• Parallel: process several instructions at a time.
• divide the problem into subproblem
• serve them to several processors or threads.
• Distributed: If the parallel algorithms are distributed to different
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machines, then we call such algorithms distributed algorithms.
Classification of Algorithms
• Deterministic or Non-Deterministic: Deterministic algorithms solve
the problem with a predefined process, whereas non-deterministic
algorithms guess the best solution at each step through the use of
heuristics.
• Divide and Conquer
• Divide: Breaking the problem into subproblems that are themselves
smaller instances of the same type of problem.
• Recursion: Recursively solving these subproblems.
• Conquer: Appropriately combining their answers.
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• Examples: merge sort and binary search algorithms.
Thank You

I wish for your better Career!

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