Data structures allow for the efficient organization and storage of data in a computer's memory. They include primitive structures like integers and strings, as well as non-primitive structures like arrays, linked lists, stacks, queues, trees, and graphs. Operations on data structures include traversing, searching, inserting, deleting, sorting, and merging. Abstract data types define the data type and operations that can be performed without specifying how they are implemented, separating the interface from the underlying implementation.
This document discusses different data structures and their characteristics. It defines data structures as ways of organizing data that consider the relationships between data elements. Data structures are divided into primitive and non-primitive categories. Primitive structures like integers are directly supported by programming languages, while non-primitive structures like linked lists, stacks, queues, trees and graphs are built from primitive types. Common operations on data structures include creation, selection, updating, searching, sorting, merging and deletion.
1.Introduction to Data Structures and Algorithms.pptxBlueSwede
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This document provides an introduction to data structures and algorithms. It defines basic terminology like data, entities, information, and operations. It categorizes data structures as primitive and non-primitive. Primitive types include integers and characters, while non-primitive types are arrays, structures, and classes. Linear data structures like arrays maintain adjacency, while non-linear structures like trees and graphs do not. Common data structures like stacks, queues, linked lists, trees, and graphs are described along with their implementations and operations. The advantages of using abstract data types to encapsulate implementation details are also discussed.
This document provides an introduction to data structures. It defines data structures as representations of logical relationships between data elements. Data structures can be primitive, like integers and floats, or non-primitive, like lists, stacks, queues, trees and graphs. Non-primitive data structures are built from primitive structures and emphasize structuring groups of homogeneous or heterogeneous data. The document describes common data structures like arrays, lists, stacks, queues and trees, and explains their properties and implementations.
This document discusses data structures and algorithm efficiency. It defines data structures as representations of logical relationships between data elements. Data structures are classified as primitive (basic types like integers) and non-primitive (derived types like lists, stacks, queues, trees, graphs). The document explains various non-primitive data structures and their implementations. It also discusses measuring algorithm efficiency, including analyzing best, worst, and average cases. Asymptotic analysis using Big O notation is introduced as a machine-independent way to compare algorithm growth rates and determine asymptotic complexity classes.
This document defines and describes different types of data structures. It begins by defining primitive data structures as basic structures directly operated on by the machine, such as integers and floats, and non-primitive data structures as more sophisticated structures derived from primitive ones, such as lists, stacks, queues, trees and graphs. It then provides examples and descriptions of common non-primitive data structures like arrays, lists, stacks, queues, trees and graphs, highlighting their key characteristics and common operations.
Data structures allow for the efficient organization and storage of data in a computer's memory. They include primitive structures like integers and strings, as well as non-primitive structures like arrays, linked lists, stacks, queues, trees, and graphs. Operations on data structures include traversing, searching, inserting, deleting, sorting, and merging. Abstract data types define the data type and operations that can be performed without specifying how they are implemented, separating the interface from the underlying implementation.
This document discusses different data structures and their characteristics. It defines data structures as ways of organizing data that consider the relationships between data elements. Data structures are divided into primitive and non-primitive categories. Primitive structures like integers are directly supported by programming languages, while non-primitive structures like linked lists, stacks, queues, trees and graphs are built from primitive types. Common operations on data structures include creation, selection, updating, searching, sorting, merging and deletion.
1.Introduction to Data Structures and Algorithms.pptxBlueSwede
Ā
This document provides an introduction to data structures and algorithms. It defines basic terminology like data, entities, information, and operations. It categorizes data structures as primitive and non-primitive. Primitive types include integers and characters, while non-primitive types are arrays, structures, and classes. Linear data structures like arrays maintain adjacency, while non-linear structures like trees and graphs do not. Common data structures like stacks, queues, linked lists, trees, and graphs are described along with their implementations and operations. The advantages of using abstract data types to encapsulate implementation details are also discussed.
This document provides an introduction to data structures. It defines data structures as representations of logical relationships between data elements. Data structures can be primitive, like integers and floats, or non-primitive, like lists, stacks, queues, trees and graphs. Non-primitive data structures are built from primitive structures and emphasize structuring groups of homogeneous or heterogeneous data. The document describes common data structures like arrays, lists, stacks, queues and trees, and explains their properties and implementations.
This document discusses data structures and algorithm efficiency. It defines data structures as representations of logical relationships between data elements. Data structures are classified as primitive (basic types like integers) and non-primitive (derived types like lists, stacks, queues, trees, graphs). The document explains various non-primitive data structures and their implementations. It also discusses measuring algorithm efficiency, including analyzing best, worst, and average cases. Asymptotic analysis using Big O notation is introduced as a machine-independent way to compare algorithm growth rates and determine asymptotic complexity classes.
This document defines and describes different types of data structures. It begins by defining primitive data structures as basic structures directly operated on by the machine, such as integers and floats, and non-primitive data structures as more sophisticated structures derived from primitive ones, such as lists, stacks, queues, trees and graphs. It then provides examples and descriptions of common non-primitive data structures like arrays, lists, stacks, queues, trees and graphs, highlighting their key characteristics and common operations.
Data can exist in various forms such as numbers, text, images, and more. Data itself has little meaning until it is processed to extract useful information. There are different types of data including categorical/qualitative data, which represents characteristics like gender, and numerical/quantitative data, which can be discrete like coin flips or continuous like measurements. Common data structures used to organize and store data include arrays, linked lists, stacks, queues, trees and graphs. Efficient searching of data structures is important and can be done using methods like linear search, which sequentially checks each element, and binary search, which can more quickly find elements in a sorted data set.
The document discusses different data structures including primitive and non-primitive structures. It defines data structures as representations of logical relationships between data elements. Primitive structures like integers are directly operated on by machines while non-primitive structures like arrays, lists, stacks, queues, trees and graphs are built from primitive structures. Arrays store homogeneous data in consecutive memory locations accessed via indexes. Lists use nodes of data and pointer fields, connected in a linear fashion. Stacks and queues follow LIFO and FIFO principles respectively for insertion and removal. Trees have hierarchical relationships and graphs model physical networks with vertices and edges.
In this you will learn about
1. Definitions
2. Introduction to Data Structures
3. Classification of Data structures
a. Primitive Data structures
i. int
ii. Float
iii. char
iv. Double
b. Non- Primitive Data structures
i. Linear Data structures
1. Arrays
2. Linked Lists
3. Stack
4. Queue
ii. Non Linear Data structures
1. Trees
2. Graphs
data structure details of types and .pptpoonamsngr
Ā
The document defines and describes various data structures. It begins by defining data structures as representations of logical relationships between data elements. It then discusses how data structures affect program design and how algorithms are paired with appropriate data structures. The document goes on to classify data structures as primitive and non-primitive, providing examples of each. It proceeds to describe several specific non-primitive data structures in more detail, including lists, stacks, queues, trees, and graphs.
This document provides an overview of unit 1 of a data structures course. It discusses prerequisites, contents including definitions of data structures, algorithms, and abstract data types. It also covers different types of arrays like one-dimensional, two-dimensional, and multidimensional arrays. Examples are provided to demonstrate initializing and accessing elements of one-dimensional arrays in C. Key concepts covered include linear and non-linear data structures, abstract data types versus data structures, and common data operations.
This document discusses different data structures and algorithms. It provides examples of common data structures like arrays, linked lists, stacks, queues, trees, and graphs. It describes what each data structure is, how it stores and organizes data, and examples of its applications. It also discusses abstract data types, algorithms, and how to analyze the time and space complexity of algorithms.
This document provides an overview of data structures and algorithms. It discusses primitive and non-primitive data structures. Non-primitive structures include arrays, files, lists (linear and non-linear), stacks, queues, graphs and trees. Common operations on data structures are creation, destruction, selection, and updation. Arrays have limitations such as static memory allocation and complex insertions/deletions. Lists allow dynamic memory allocation. The document also discusses merging arrays, two-dimensional arrays stored in row-major and column-major format, sparse matrices, and representing polynomials using arrays.
1.1 introduction to Data Structures.pptAshok280385
Ā
Here are the algorithms for the given problems:
1. WAA to find largest of three numbers:
1. Start
2. Read three numbers a, b, c
3. If a > b and a > c then largest number is a
4. Else If b > a and b > c then largest number is b
5. Else largest number is c
6. Print largest number
7. Stop
2. WAA to find the sum of first 10 natural numbers using for loop:
1. Start
2. Declare variables i, sum
3. Initialize i=1, sum=0
4. For i=1 to 10
5. sum =
A data structure is a specialized format for organizing, processing, retrieving and storing data. There are several basic and advanced types of data structures, all designed to arrange data to suit a specific purpose
data structure programing language in c.pptLavkushGupta12
Ā
A data structure is a specialized format for organizing, processing, retrieving and storing data. There are several basic and advanced types of data structures, all designed to arrange data to suit a specific purpose
This document provides an overview of data structures and algorithms. It discusses topics like arrays, stacks, queues, sparse matrices, and analysis of algorithms. Key points include:
- Arrays allow storing elements in contiguous memory locations and accessing via indexes. Representations include one-dimensional, two-dimensional, and sparse arrays.
- Stacks follow LIFO while queues follow FIFO using operations like push, pop for stacks and enqueue, dequeue for queues.
- Sparse matrices store only non-zero elements to save space using representations like triplet format and linked lists.
- Algorithm analysis includes asymptotic analysis of time and space complexity using notations like Big O. Performance of common operations on data structures is also
This document provides an introduction to data structures. It discusses primitive and non-primitive data structures and their classifications. Linear data structures like arrays, stacks, queues and linked lists are covered, along with non-linear structures like trees and graphs. Common operations on data structures are also summarized such as traversing, searching, inserting and deleting. Finally, abstract data types and examples of common ADTs like lists, stacks and queues are introduced.
Data Structure & aaplications_Module-1.pptxGIRISHKUMARBC1
Ā
This document provides information about the course "Data Structures and Applications". The course aims to explain fundamentals of data structures and their applications for programming. It will cover linear and non-linear data structures like stacks, queues, lists, trees and graphs. Students will learn sorting and searching algorithms and how to select suitable data structures for application development and problem solving.
This document provides an introduction to data structures. It defines key terms like data, information, records and files. It also describes different types of data structures like arrays, linked lists, stacks, queues, trees and graphs. Linear and non-linear data structures are explained. Common operations on data structures like insertion, deletion and searching are outlined. The document also defines what an algorithm is and provides an example of an algorithm to add two numbers. It concludes by describing time and space complexity analysis of algorithms.
This document provides an introduction to data structures. It discusses primitive and non-primitive data structures and their classifications. Linear data structures like arrays, stacks, queues and linked lists are covered, along with non-linear structures like trees and graphs. Common operations on data structures like traversing, searching, inserting and deleting are also summarized. Finally, the document introduces abstract data types and provides examples of common ADT specifications for lists, stacks and queues.
This document discusses data structures and algorithms. It defines key terms like data, records, and structures. It then describes different types of arrays like one-dimensional, two-dimensional, and multi-dimensional arrays. It also discusses common array operations like insertion, deletion, traversal, reversing, sorting, and searching. Finally, it briefly introduces other data structures like linked lists, and categorizes data structures as linear or non-linear and describes common operations on data structures like traversing, searching, insertion, and deletion.
An In-Depth Exploration of Natural Language Processing: Evolution, Applicatio...DharmaBanothu
Ā
Natural language processing (NLP) has
recently garnered significant interest for the
computational representation and analysis of human
language. Its applications span multiple domains such
as machine translation, email spam detection,
information extraction, summarization, healthcare,
and question answering. This paper first delineates
four phases by examining various levels of NLP and
components of Natural Language Generation,
followed by a review of the history and progression of
NLP. Subsequently, we delve into the current state of
the art by presenting diverse NLP applications,
contemporary trends, and challenges. Finally, we
discuss some available datasets, models, and
evaluation metrics in NLP.
Data can exist in various forms such as numbers, text, images, and more. Data itself has little meaning until it is processed to extract useful information. There are different types of data including categorical/qualitative data, which represents characteristics like gender, and numerical/quantitative data, which can be discrete like coin flips or continuous like measurements. Common data structures used to organize and store data include arrays, linked lists, stacks, queues, trees and graphs. Efficient searching of data structures is important and can be done using methods like linear search, which sequentially checks each element, and binary search, which can more quickly find elements in a sorted data set.
The document discusses different data structures including primitive and non-primitive structures. It defines data structures as representations of logical relationships between data elements. Primitive structures like integers are directly operated on by machines while non-primitive structures like arrays, lists, stacks, queues, trees and graphs are built from primitive structures. Arrays store homogeneous data in consecutive memory locations accessed via indexes. Lists use nodes of data and pointer fields, connected in a linear fashion. Stacks and queues follow LIFO and FIFO principles respectively for insertion and removal. Trees have hierarchical relationships and graphs model physical networks with vertices and edges.
In this you will learn about
1. Definitions
2. Introduction to Data Structures
3. Classification of Data structures
a. Primitive Data structures
i. int
ii. Float
iii. char
iv. Double
b. Non- Primitive Data structures
i. Linear Data structures
1. Arrays
2. Linked Lists
3. Stack
4. Queue
ii. Non Linear Data structures
1. Trees
2. Graphs
data structure details of types and .pptpoonamsngr
Ā
The document defines and describes various data structures. It begins by defining data structures as representations of logical relationships between data elements. It then discusses how data structures affect program design and how algorithms are paired with appropriate data structures. The document goes on to classify data structures as primitive and non-primitive, providing examples of each. It proceeds to describe several specific non-primitive data structures in more detail, including lists, stacks, queues, trees, and graphs.
This document provides an overview of unit 1 of a data structures course. It discusses prerequisites, contents including definitions of data structures, algorithms, and abstract data types. It also covers different types of arrays like one-dimensional, two-dimensional, and multidimensional arrays. Examples are provided to demonstrate initializing and accessing elements of one-dimensional arrays in C. Key concepts covered include linear and non-linear data structures, abstract data types versus data structures, and common data operations.
This document discusses different data structures and algorithms. It provides examples of common data structures like arrays, linked lists, stacks, queues, trees, and graphs. It describes what each data structure is, how it stores and organizes data, and examples of its applications. It also discusses abstract data types, algorithms, and how to analyze the time and space complexity of algorithms.
This document provides an overview of data structures and algorithms. It discusses primitive and non-primitive data structures. Non-primitive structures include arrays, files, lists (linear and non-linear), stacks, queues, graphs and trees. Common operations on data structures are creation, destruction, selection, and updation. Arrays have limitations such as static memory allocation and complex insertions/deletions. Lists allow dynamic memory allocation. The document also discusses merging arrays, two-dimensional arrays stored in row-major and column-major format, sparse matrices, and representing polynomials using arrays.
1.1 introduction to Data Structures.pptAshok280385
Ā
Here are the algorithms for the given problems:
1. WAA to find largest of three numbers:
1. Start
2. Read three numbers a, b, c
3. If a > b and a > c then largest number is a
4. Else If b > a and b > c then largest number is b
5. Else largest number is c
6. Print largest number
7. Stop
2. WAA to find the sum of first 10 natural numbers using for loop:
1. Start
2. Declare variables i, sum
3. Initialize i=1, sum=0
4. For i=1 to 10
5. sum =
A data structure is a specialized format for organizing, processing, retrieving and storing data. There are several basic and advanced types of data structures, all designed to arrange data to suit a specific purpose
data structure programing language in c.pptLavkushGupta12
Ā
A data structure is a specialized format for organizing, processing, retrieving and storing data. There are several basic and advanced types of data structures, all designed to arrange data to suit a specific purpose
This document provides an overview of data structures and algorithms. It discusses topics like arrays, stacks, queues, sparse matrices, and analysis of algorithms. Key points include:
- Arrays allow storing elements in contiguous memory locations and accessing via indexes. Representations include one-dimensional, two-dimensional, and sparse arrays.
- Stacks follow LIFO while queues follow FIFO using operations like push, pop for stacks and enqueue, dequeue for queues.
- Sparse matrices store only non-zero elements to save space using representations like triplet format and linked lists.
- Algorithm analysis includes asymptotic analysis of time and space complexity using notations like Big O. Performance of common operations on data structures is also
This document provides an introduction to data structures. It discusses primitive and non-primitive data structures and their classifications. Linear data structures like arrays, stacks, queues and linked lists are covered, along with non-linear structures like trees and graphs. Common operations on data structures are also summarized such as traversing, searching, inserting and deleting. Finally, abstract data types and examples of common ADTs like lists, stacks and queues are introduced.
Data Structure & aaplications_Module-1.pptxGIRISHKUMARBC1
Ā
This document provides information about the course "Data Structures and Applications". The course aims to explain fundamentals of data structures and their applications for programming. It will cover linear and non-linear data structures like stacks, queues, lists, trees and graphs. Students will learn sorting and searching algorithms and how to select suitable data structures for application development and problem solving.
This document provides an introduction to data structures. It defines key terms like data, information, records and files. It also describes different types of data structures like arrays, linked lists, stacks, queues, trees and graphs. Linear and non-linear data structures are explained. Common operations on data structures like insertion, deletion and searching are outlined. The document also defines what an algorithm is and provides an example of an algorithm to add two numbers. It concludes by describing time and space complexity analysis of algorithms.
This document provides an introduction to data structures. It discusses primitive and non-primitive data structures and their classifications. Linear data structures like arrays, stacks, queues and linked lists are covered, along with non-linear structures like trees and graphs. Common operations on data structures like traversing, searching, inserting and deleting are also summarized. Finally, the document introduces abstract data types and provides examples of common ADT specifications for lists, stacks and queues.
This document discusses data structures and algorithms. It defines key terms like data, records, and structures. It then describes different types of arrays like one-dimensional, two-dimensional, and multi-dimensional arrays. It also discusses common array operations like insertion, deletion, traversal, reversing, sorting, and searching. Finally, it briefly introduces other data structures like linked lists, and categorizes data structures as linear or non-linear and describes common operations on data structures like traversing, searching, insertion, and deletion.
Similar to DS.ppt Datatastructures notes presentation (20)
An In-Depth Exploration of Natural Language Processing: Evolution, Applicatio...DharmaBanothu
Ā
Natural language processing (NLP) has
recently garnered significant interest for the
computational representation and analysis of human
language. Its applications span multiple domains such
as machine translation, email spam detection,
information extraction, summarization, healthcare,
and question answering. This paper first delineates
four phases by examining various levels of NLP and
components of Natural Language Generation,
followed by a review of the history and progression of
NLP. Subsequently, we delve into the current state of
the art by presenting diverse NLP applications,
contemporary trends, and challenges. Finally, we
discuss some available datasets, models, and
evaluation metrics in NLP.
Covid Management System Project Report.pdfKamal Acharya
Ā
CoVID-19 sprang up in Wuhan China in November 2019 and was declared a pandemic by the in January 2020 World Health Organization (WHO). Like the Spanish flu of 1918 that claimed millions of lives, the COVID-19 has caused the demise of thousands with China, Italy, Spain, USA and India having the highest statistics on infection and mortality rates. Regardless of existing sophisticated technologies and medical science, the spread has continued to surge high. With this COVID-19 Management System, organizations can respond virtually to the COVID-19 pandemic and protect, educate and care for citizens in the community in a quick and effective manner. This comprehensive solution not only helps in containing the virus but also proactively empowers both citizens and care providers to minimize the spread of the virus through targeted strategies and education.
Impartiality as per ISO /IEC 17025:2017 StandardMuhammadJazib15
Ā
This document provides basic guidelines for imparitallity requirement of ISO 17025. It defines in detial how it is met and wiudhwdih jdhsjdhwudjwkdbjwkdddddddddddkkkkkkkkkkkkkkkkkkkkkkkwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwioiiiiiiiiiiiii uwwwwwwwwwwwwwwwwhe wiqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq gbbbbbbbbbbbbb owdjjjjjjjjjjjjjjjjjjjj widhi owqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq uwdhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhwqiiiiiiiiiiiiiiiiiiiiiiiiiiiiw0pooooojjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj whhhhhhhhhhh wheeeeeeee wihieiiiiii wihe
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This study Examines the Effectiveness of Talent Procurement through the Imple...DharmaBanothu
Ā
In the world with high technology and fast
forward mindset recruiters are walking/showing interest
towards E-Recruitment. Present most of the HRs of
many companies are choosing E-Recruitment as the best
choice for recruitment. E-Recruitment is being done
through many online platforms like Linkedin, Naukri,
Instagram , Facebook etc. Now with high technology E-
Recruitment has gone through next level by using
Artificial Intelligence too.
Key Words : Talent Management, Talent Acquisition , E-
Recruitment , Artificial Intelligence Introduction
Effectiveness of Talent Acquisition through E-
Recruitment in this topic we will discuss about 4important
and interlinked topics which are
This is an overview of my current metallic design and engineering knowledge base built up over my professional career and two MSc degrees : - MSc in Advanced Manufacturing Technology University of Portsmouth graduated 1st May 1998, and MSc in Aircraft Engineering Cranfield University graduated 8th June 2007.
Data Communication and Computer Networks Management System Project Report.pdfKamal Acharya
Ā
Networking is a telecommunications network that allows computers to exchange data. In
computer networks, networked computing devices pass data to each other along data
connections. Data is transferred in the form of packets. The connections between nodes are
established using either cable media or wireless media.
Online train ticket booking system project.pdfKamal Acharya
Ā
Rail transport is one of the important modes of transport in India. Now a days we
see that there are railways that are present for the long as well as short distance
travelling which makes the life of the people easier. When compared to other
means of transport, a railway is the cheapest means of transport. The maintenance
of the railway database also plays a major role in the smooth running of this
system. The Online Train Ticket Management System will help in reserving the
tickets of the railways to travel from a particular source to the destination.
Cricket management system ptoject report.pdfKamal Acharya
Ā
The aim of this project is to provide the complete information of the National and
International statistics. The information is available country wise and player wise. By
entering the data of eachmatch, we can get all type of reports instantly, which will be
useful to call back history of each player. Also the team performance in each match can
be obtained. We can get a report on number of matches, wins and lost.
Sri Guru Hargobind Ji - Bandi Chor Guru.pdfBalvir Singh
Ā
Sri Guru Hargobind Ji (19 June 1595 - 3 March 1644) is revered as the Sixth Nanak.
ā¢ On 25 May 1606 Guru Arjan nominated his son Sri Hargobind Ji as his successor. Shortly
afterwards, Guru Arjan was arrested, tortured and killed by order of the Mogul Emperor
Jahangir.
ā¢ Guru Hargobind's succession ceremony took place on 24 June 1606. He was barely
eleven years old when he became 6th Guru.
ā¢ As ordered by Guru Arjan Dev Ji, he put on two swords, one indicated his spiritual
authority (PIRI) and the other, his temporal authority (MIRI). He thus for the first time
initiated military tradition in the Sikh faith to resist religious persecution, protect
peopleās freedom and independence to practice religion by choice. He transformed
Sikhs to be Saints and Soldier.
ā¢ He had a long tenure as Guru, lasting 37 years, 9 months and 3 days
1. Definition
ā¢ Data structure is representation of the logical relationship existing
between individual elements of data.
ā¢ In other words, a data structure is a way of organizing all data items
that considers not only the elements stored but also their relationship to
each other.
2. Introduction
ā¢ Data structure affects the design of both structural & functional aspects
of a program.
Program=algorithm + Data Structure
ā¢ You know that a algorithm is a step by step procedure to solve a
particular function.
3. Introduction
ā¢ That means, algorithm is a set of instruction written to carry out
certain tasks & the data structure is the way of organizing the data
with their logical relationship retained.
ā¢ To develop a program of an algorithm, we should select an appropriate
data structure for that algorithm.
ā¢ Therefore algorithm and its associated data structures from a program.
4. Classification of Data Structure
ā¢ Data structure are normally divided into two broad categories:
ā¢Primitive Data Structure
ā¢Non-Primitive Data Structure
5. Classification of Data Structure
Data structure
Primitive DS Non-Primitive DS
Integer Float Character Pointer
Float
Integer Float
6. Classification of Data Structure
Non-Primitive DS
Linear List Non-Linear List
Array
Link List Stack
Queue Graph Trees
7. Primitive Data Structure
ā¢ There are basic structures and directly operated upon by the machine
instructions.
ā¢ In general, there are different representation on different computers.
ā¢ Integer, Floating-point number, Character constants, string constants,
pointers etc, fall in this category.
8. Non-Primitive Data Structure
ā¢ There are more sophisticated data structures.
ā¢ These are derived from the primitive data structures.
ā¢ The non-primitive data structures emphasize on structuring of a group
of homogeneous (same type) or heterogeneous (different type) data
items.
9. Non-Primitive Data Structure
ā¢ Lists, Stack, Queue, Tree, Graph are example of non-primitive data
structures.
ā¢ The design of an efficient data structure must take operations to be
performed on the data structure.
10. Non-Primitive Data Structure
ā¢ The most commonly used operation on data structure are broadly
categorized into following types:
ā¢ Create
ā¢ Selection
ā¢ Updating
ā¢ Searching
ā¢ Sorting
ā¢ Merging
ā¢ Destroy or Delete
11. Different between them
ā¢ A primitive data structure is generally a basic structure that is usually
built into the language, such as an integer, a float.
ā¢ A non-primitive data structure is built out of primitive data structures
linked together in meaningful ways, such as a or a linked-list, binary
search tree, AVL Tree, graph etc.
12. Description of various
Data Structures : Arrays
ā¢ An array is defined as a set of finite number of homogeneous
elements or same data items.
ā¢ It means an array can contain one type of data only, either all integer,
all float-point number or all character.
13. Arrays
ā¢ Simply, declaration of array is as follows:
int arr[10]
ā¢ Where int specifies the data type or type of elements arrays stores.
ā¢ āarrā is the name of array & the number specified inside the square
brackets is the number of elements an array can store, this is also
called sized or length of array.
14. Arrays
ā¢ Following are some of the concepts to be remembered about arrays:
ā¢The individual element of an array can be
accessed by specifying name of the array,
following by index or subscript inside
square brackets.
ā¢The first element of the array has index
zero[0]. It means the first element and last
element will be specified as:arr[0] & arr[9]
Respectively.
15. Arrays
ā¢The elements of array will always be stored
in the consecutive (continues) memory
location.
ā¢The number of elements that can be stored
in an array, that is the size of array or its
length is given by the following equation:
(Upperbound-lowerbound)+1
16. Arrays
ā¢For the above array it would be
(9-0)+1=10,where 0 is the lower bound of
array and 9 is the upper bound of array.
ā¢Array can always be read or written
through loop. If we read a one-dimensional
array it require one loop for reading and
other for writing the array.
17. Arrays
ā¢For example: Reading an array
For(i=0;i<=9;i++)
scanf(ā%dā,&arr[i]);
ā¢For example: Writing an array
For(i=0;i<=9;i++)
printf(ā%dā,arr[i]);
18. Arrays
ā¢If we are reading or writing two-
dimensional array it would require two
loops. And similarly the array of a N
dimension would required N loops.
ā¢Some common operation performed on
array are:
ā¢Creation of an array
ā¢Traversing an array
19. Arrays
ā¢Insertion of new element
ā¢Deletion of required element
ā¢Modification of an element
ā¢Merging of arrays
20. Lists
ā¢ A lists (Linear linked list) can be defined as a collection of variable
number of data items.
ā¢ Lists are the most commonly used non-primitive data structures.
ā¢ An element of list must contain at least two fields, one for storing data or
information and other for storing address of next element.
ā¢ As you know for storing address we have a special data structure of list
the address must be pointer type.
21. Lists
ā¢ Technically each such element is referred to as a node, therefore a list
can be defined as a collection of nodes as show bellow:
Head
AAA BBB CCC
Information field Pointer field
[Linear Liked List]
22. Lists
ā¢ Types of linked lists:
ā¢ Single linked list
ā¢ Doubly linked list
ā¢ Single circular linked list
ā¢ Doubly circular linked list
23. Stack
ā¢ A stack is also an ordered collection of elements like arrays, but it has
a special feature that deletion and insertion of elements can be done
only from one end called the top of the stack (TOP)
ā¢ Due to this property it is also called as last in first out type of data
structure (LIFO).
24. Stack
ā¢ It could be through of just like a stack of plates placed on table in a party, a
guest always takes off a fresh plate from the top and the new plates are
placed on to the stack at the top.
ā¢ It is a non-primitive data structure.
ā¢ When an element is inserted into a stack or removed from the stack, its base
remains fixed where the top of stack changes.
25. Stack
ā¢ Insertion of element into stack is called PUSH and deletion of
element from stack is called POP.
ā¢ The bellow show figure how the operations take place on a stack:
PUSH POP
[STACK]
26. Stack
ā¢ The stack can be implemented into two ways:
ā¢Using arrays (Static implementation)
ā¢Using pointer (Dynamic implementation)
27. Queue
ā¢ Queue are first in first out type of data structure (i.e. FIFO)
ā¢ In a queue new elements are added to the queue from one end called
REAR end and the element are always removed from other end called
the FRONT end.
ā¢ The people standing in a railway reservation row are an example of
queue.
28. Queue
ā¢ Each new person comes and stands at the end of the row and
person getting their reservation confirmed get out of the row from
the front end.
ā¢ The bellow show figure how the operations take place on a stack:
10 20 30 40 50
front rear
29. Queue
ā¢ The queue can be implemented into two ways:
ā¢Using arrays (Static implementation)
ā¢Using pointer (Dynamic implementation)
30. Trees
ā¢ A tree can be defined as finite set of data items (nodes).
ā¢ Tree is non-linear type of data structure in which data items are
arranged or stored in a sorted sequence.
ā¢ Tree represent the hierarchical relationship between various elements.
31. Trees
ā¢ In trees:
ā¢ There is a special data item at the top of hierarchy called the Root of
the tree.
ā¢ The remaining data items are partitioned into number of mutually
exclusive subset, each of which is itself, a tree which is called the sub
tree.
ā¢ The tree always grows in length towards bottom in data structures,
unlike natural trees which grows upwards.
32. Trees
ā¢ The tree structure organizes the data into branches, which related the
information.
A
B C
D E F G
root
33. Graph
ā¢ Graph is a mathematical non-linear data structure capable of
representing many kind of physical structures.
ā¢ It has found application in Geography, Chemistry and Engineering
sciences.
ā¢ Definition: A graph G(V,E) is a set of vertices V and a set of edges E.
34. Graph
ā¢ An edge connects a pair of vertices and many have weight such as
length, cost and another measuring instrument for according the
graph.
ā¢ Vertices on the graph are shown as point or circles and edges are
drawn as arcs or line segment.