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This document describes a study that uses machine learning algorithms to predict whether students will be placed in jobs after graduating. The researchers used data on past students' test scores, skills, and placement outcomes to train Naive Bayes and K-Nearest Neighbor classifiers. These algorithms were then used to predict placements for current students based on their profiles. The goal is to help students and institutions focus on improving skills and increasing placement rates, which are important for university reputation. The models use factors like test scores, skills, and course grades to classify students as placed or not placed after training on historical placement data.
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This document describes a study that uses machine learning algorithms to predict whether students will be placed in jobs after graduating. Specifically, it uses Naive Bayes and K-Nearest Neighbors classifiers to analyze historical student data and predict placements. The algorithms consider parameters like academic results, skills, and previous placement data to make predictions. This system aims to help institutions increase placement percentages by identifying students' strengths and areas for improvement. It is intended to benefit both students in preparing for careers and placement cells in targeting support. Accurately predicting placements could boost a school's reputation by demonstrating career outcomes.
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This document discusses using k-means clustering to analyze student behavior and performance based on factors like exam scores, assignments, tests, and attendance. The goal is to evaluate students accurately and help professors reduce failure rates and improve performance. It provides background on data clustering and how it can be applied in education. A proposed model is described that uses students' previous grades, quiz scores, assignment completion, lab performance, class test scores and attendance to predict their final grades. The k-means clustering algorithm is explained and results are presented showing how students were clustered into groups based on GPA and whether they passed or failed. The clustering aims to identify weaker students before exams to help improve their performance.
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This document discusses research on using machine learning techniques to predict student performance and career outcomes. It provides an overview of various studies that have used methods like decision trees, naive Bayes classification, neural networks, and clustering algorithms. The studies aimed to identify factors influencing student performance and predict outcomes like course grades, dropout risk, and placement success. The document also compares the different techniques, finding that deep neural networks and ensemble methods can achieve relatively high prediction accuracy, above 80% in some cases. Overall, the research aims to help educational institutions identify at-risk students and improve student performance.
This document summarizes research analyzing student exam results from the final semester of a civil engineering program in 2014 at Purbanchal University in Nepal using the WEKA data mining tool. The research aimed to identify decision rules that control exam outcomes. For most colleges, the course "Hydropower Engineering" was found to be the key factor in whether students passed or failed. However, for one college, exam results were best predicted by performance in "Hydropower Engineering" and "Construction Management". The research demonstrates how data mining can help educational institutions understand factors influencing student success.
Unit 1 Introduction to Data Structures(1).pptxvaibhavparjane
1. The document discusses data structures, defining them as a particular way of storing and organizing data in a computer so that it can be used efficiently.
2. A data structure consists of a set of data values, relationships between the data values, and functions that operate on the data. It provides an organized way to store and manipulate data.
3. Data structures are needed to facilitate efficient storage, retrieval, and manipulation of data. They help speed up processes by organizing data in a way that allows fast searching and access.
IRJET- Stabilization of Black Cotton Soil using Rice Husk Ash and LimeIRJET Journal
This document describes a study that uses machine learning algorithms to predict whether students will be placed in jobs after graduating. The researchers used data on past students' test scores, skills, and placement outcomes to train Naive Bayes and K-Nearest Neighbor classifiers. These algorithms were then used to predict placements for current students based on their profiles. The goal is to help students and institutions focus on improving skills and increasing placement rates, which are important for university reputation. The models use factors like test scores, skills, and course grades to classify students as placed or not placed after training on historical placement data.
IRJET- Student Placement Prediction using Machine LearningIRJET Journal
This document describes a study that uses machine learning algorithms to predict whether students will be placed in jobs after graduating. Specifically, it uses Naive Bayes and K-Nearest Neighbors classifiers to analyze historical student data and predict placements. The algorithms consider parameters like academic results, skills, and previous placement data to make predictions. This system aims to help institutions increase placement percentages by identifying students' strengths and areas for improvement. It is intended to benefit both students in preparing for careers and placement cells in targeting support. Accurately predicting placements could boost a school's reputation by demonstrating career outcomes.
Data Clustering in Education for StudentsIRJET Journal
This document discusses using k-means clustering to analyze student behavior and performance based on factors like exam scores, assignments, tests, and attendance. The goal is to evaluate students accurately and help professors reduce failure rates and improve performance. It provides background on data clustering and how it can be applied in education. A proposed model is described that uses students' previous grades, quiz scores, assignment completion, lab performance, class test scores and attendance to predict their final grades. The k-means clustering algorithm is explained and results are presented showing how students were clustered into groups based on GPA and whether they passed or failed. The clustering aims to identify weaker students before exams to help improve their performance.
IRJET - A Study on Student Career PredictionIRJET Journal
This document discusses research on using machine learning techniques to predict student performance and career outcomes. It provides an overview of various studies that have used methods like decision trees, naive Bayes classification, neural networks, and clustering algorithms. The studies aimed to identify factors influencing student performance and predict outcomes like course grades, dropout risk, and placement success. The document also compares the different techniques, finding that deep neural networks and ensemble methods can achieve relatively high prediction accuracy, above 80% in some cases. Overall, the research aims to help educational institutions identify at-risk students and improve student performance.
This document summarizes research analyzing student exam results from the final semester of a civil engineering program in 2014 at Purbanchal University in Nepal using the WEKA data mining tool. The research aimed to identify decision rules that control exam outcomes. For most colleges, the course "Hydropower Engineering" was found to be the key factor in whether students passed or failed. However, for one college, exam results were best predicted by performance in "Hydropower Engineering" and "Construction Management". The research demonstrates how data mining can help educational institutions understand factors influencing student success.
Unit 1 Introduction to Data Structures(1).pptxvaibhavparjane
1. The document discusses data structures, defining them as a particular way of storing and organizing data in a computer so that it can be used efficiently.
2. A data structure consists of a set of data values, relationships between the data values, and functions that operate on the data. It provides an organized way to store and manipulate data.
3. Data structures are needed to facilitate efficient storage, retrieval, and manipulation of data. They help speed up processes by organizing data in a way that allows fast searching and access.
Student Performance Evaluation in Education Sector Using Prediction and Clust...IJSRD
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PREDICTING STUDENT ACADEMIC PERFORMANCE IN BLENDED LEARNING USING ARTIFICIAL ...ijaia
Along with the spreading of online education, the importance of active support of students involved in
online learning processes has grown. The application of artificial intelligence in education allows
instructors to analyze data extracted from university servers, identify patterns of student behavior and
develop interventions for struggling students. This study used student data stored in a Moodle server and
predicted student success in course, based on four learning activities - communication via emails,
collaborative content creation with wiki, content interaction measured by files viewed and self-evaluation
through online quizzes. Next, a model based on the Multi-Layer Perceptron Neural Network was trained to
predict student performance on a blended learning course environment. The model predicted the
performance of students with correct classification rate, CCR, of 98.3%.
CGPA otherwise called Cumulative Grade Points. Average is the normal of Grade Points acquired in every one of the subjects secured till date. It is trusted that it gives a general knowledge into the level of devotion, truthfulness and diligent work put by the understudy.
However there might be where an understudy who is remarkable at programming may not appreciate other hypothetical subjects like programming testing. Notwithstanding, CGPA comes up short when such a situation comes into picture.
Akshob Rao is pursuing a Bachelor of Science in Electrical Engineering from Texas A&M University in May 2016. He has relevant coursework in digital image processing, power electronics, optical engineering, and communications. Rao has skills in programming languages like C++, C, Java, HTML, JavaScript, SQL, MATLAB and computer-aided design tools. He held an internship in data management and has experience with academic projects involving microfluidic systems, amplifier design, database applications, gene expression analysis, and image segmentation. Rao is a member of IEEE and the Indian Students Association.
PREDICTING STUDENT ACADEMIC PERFORMANCE IN BLENDED LEARNING USING ARTIFICIAL ...gerogepatton
Along with the spreading of online education, the importance of active support of students involved in
online learning processes has grown. The application of artificial intelligence in education allows
instructors to analyze data extracted from university servers, identify patterns of student behavior and
develop interventions for struggling students. This study used student data stored in a Moodle server and
predicted student success in course, based on four learning activities - communication via emails,
collaborative content creation with wiki, content interaction measured by files viewed and self-evaluation
through online quizzes. Next, a model based on the Multi-Layer Perceptron Neural Network was trained to
predict student performance on a blended learning course environment. The model predicted the
performance of students with correct classification rate, CCR, of 98.3%.
This document summarizes a lecture on Naive Bayes classification. It introduces classification techniques including supervised and unsupervised classification. It discusses the Bayesian classifier approach which is based on Bayes' theorem of probability. The key concepts covered include conditional probability, joint probability, and total probability. It provides an example of applying Naive Bayes classification to an air traffic data set to predict flight arrival status.
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This document describes a student project titled "Student Part Time Job as Tutors Using K-Means Algorithm". The project involves developing a web-based application to help students find part-time tutoring jobs based on their academic achievement in particular subjects. The application will use K-means clustering algorithm to group tutors based on their subject grades and recommend suitable tutoring jobs. The document outlines the introduction, problem statement, objectives, scope, limitations, expected results, and literature review of the project.
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A WEB BASED APPLICATION FOR RESUME PARSER USING NATURAL LANGUAGE PROCESSING T...IRJET Journal
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
IRJET- Using Data Mining to Predict Students PerformanceIRJET Journal
This document describes a study that used logistic regression to predict student performance based on educational data. The researchers collected student data including exam scores, attendance, study hours, family income, etc. from a large dataset. Logistic regression achieved the best prediction accuracy of 82.03% compared to other models like naive bayes, K-nearest neighbor, and multi-layer perceptron. The results indicate that around 230 students would perform poorly, 600 would perform fairly, and 200 would perform well based on the predictive model. This analysis can help identify students needing extra support and help universities improve academic outcomes.
A Model for Predicting Students’ Academic Performance using a Hybrid of K-mea...Editor IJCATR
Higher learning institutions nowadays operate in a more complex and competitive due to a high demand from prospective
students and an emerging increase of universities both public and private. Management of Universities face challenges and concerns of
predicting students’ academic performance in to put mechanisms in place prior enough for their improvement. This research aims at
employing Decision tree and K-means data mining algorithms to model an approach to predict the performance of students in advance
so as to devise mechanisms of alleviating student dropout rates and improve on performance. In Kenya for example, there has been
witnessed an increase student enrolling in universities since the Government started free primary education. Therefore the Government
expects an increased workforce of professionals from these institutions without compromising quality so as to achieve its millennium
development and vision 2030. Backlog of students not finishing their studies in stipulated time due to poor performance is another
issue that can be addressed from the results of this research since predicting student performance in advance will enable University
management to devise ways of assisting weak students and even make more decisions on how to select students for particular courses.
Previous studies have been done Educational Data Mining mostly focusing on factors affecting students’ performance and also used
different algorithms in predicting students’ performance. In all these researches, accuracy of prediction is key and what researchers
look forward to try and improve.
A Model for Predicting Students’ Academic Performance using a Hybrid of K-mea...Editor IJCATR
Higher learning institutions nowadays operate in a more complex and competitive due to a high demand from prospective
students and an emerging increase of universities both public and private. Management of Universities face challenges and concerns of
predicting students’ academic performance in to put mechanisms in place prior enough for their improvement. This research aims at
employing Decision tree and K-means data mining algorithms to model an approach to predict the performance of students in advance
so as to devise mechanisms of alleviating student dropout rates and improve on performance. In Kenya for example, there has been
witnessed an increase student enrolling in universities since the Government started free primary education. Therefore the Government
expects an increased workforce of professionals from these institutions without compromising quality so as to achieve its millennium
development and vision 2030. Backlog of students not finishing their studies in stipulated time due to poor performance is another
issue that can be addressed from the results of this research since predicting student performance in advance will enable University
management to devise ways of assisting weak students and even make more decisions on how to select students for particular courses.
Previous studies have been done Educational Data Mining mostly focusing on factors affecting students’ performance and also used
different algorithms in predicting students’ performance. In all these researches, accuracy of prediction is key and what researchers
look forward to try and improve.
Data Science Training | Data Science Tutorial | Data Science Certification | ...Edureka!
This Edureka Data Science Training will help you understand what is Data Science and you will learn about different Data Science components and concepts. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. What is Data Science?
2. Job Roles in Data Science
3. Components of Data Science
4. Concepts of Statistics
5. Power of Data Visualization
6. Introduction to Machine Learning using R
7. Supervised & Unsupervised Learning
8. Classification, Clustering & Recommenders
9. Text Mining & Time Series
10. Deep Learning
To take a structured training on Data Science, you can check complete details of our Data Science Certification Training course here: https://goo.gl/OCfxP2
Veldandi Srikanth has worked as a Business Development Manager and Project Developer for various companies. He has a M.Tech in Computer Science, MCA, and other qualifications. His technical skills include Java, C/C++, databases, and other technologies. He completed academic projects on image steganography and spatial approximate string search.
IRJET- Comparative Study of PCA, KPCA, KFA and LDA Algorithms for Face Re...IRJET Journal
This document compares the performance of four face recognition algorithms - PCA, KPCA, KFA, and LDA - on three standard datasets: AT&T, Yale, and UMIST. It finds that KFA generally achieves the highest recognition rates, particularly for the AT&T and Yale datasets which involve changes in facial expressions and lighting. The Yale dataset, with its variations, yields the best results overall for KFA and LDA. The UMIST dataset, with its profile images, produces lower recognition rates across algorithms due to less similarity between training and test images.
THE IMPLICATION OF STATISTICAL ANALYSIS AND FEATURE ENGINEERING FOR MODEL BUI...ijcseit
This document discusses various statistical analysis and feature engineering techniques that can be used for model building in machine learning algorithms. It describes how proper feature extraction through techniques like correlation analysis, principal component analysis, recursive feature elimination, and feature importance can help improve the accuracy of machine learning models. The document provides examples of applying different feature selection methods like univariate selection, recursive feature elimination, and principal component analysis on a diabetes dataset. It also explains the mathematics behind principal component analysis and how feature importance is estimated using an extra trees classifier. Overall, the document emphasizes how statistical analysis and feature engineering are important for effective model building in machine learning.
THE IMPLICATION OF STATISTICAL ANALYSIS AND FEATURE ENGINEERING FOR MODEL BUI...IJCSES Journal
Scrutiny for presage is the era of advance statistics where accuracy matter the most. Commensurate between algorithms with statistical implementation provides better consequence in terms of accurate prediction by using data sets. Prolific usage of algorithms lead towards the simplification of mathematical models, which provide less manual calculations. Presage is the essence of data science and machine learning requisitions that impart control over situations. Implementation of any dogmas require proper feature extraction which helps in the proper model building that assist in precision. This paper is predominantly based on different statistical analysis which includes correlation significance and proper categorical data distribution using feature engineering technique that unravel accuracy of different models of machine learning algorithms.
Koushik Modayur Chandramouleeswaran has extensive experience developing software using various programming languages and technologies. He is pursuing a Master of Science in Computer Science from The University of Texas at Arlington with a GPA of 3.62/4.0 and holds a Bachelor of Technology in IT from Bharath University in India with a GPA of 8.44/10. He has worked as an Associate and Programmer Analyst at Cognizant Technology Solutions, developing tools and reports to help clients save over $120k. Koushik has strong skills in Python, Java, R, databases, big data technologies and machine learning. He has completed several projects applying techniques like decision
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
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Along with the spreading of online education, the importance of active support of students involved in
online learning processes has grown. The application of artificial intelligence in education allows
instructors to analyze data extracted from university servers, identify patterns of student behavior and
develop interventions for struggling students. This study used student data stored in a Moodle server and
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through online quizzes. Next, a model based on the Multi-Layer Perceptron Neural Network was trained to
predict student performance on a blended learning course environment. The model predicted the
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CGPA otherwise called Cumulative Grade Points. Average is the normal of Grade Points acquired in every one of the subjects secured till date. It is trusted that it gives a general knowledge into the level of devotion, truthfulness and diligent work put by the understudy.
However there might be where an understudy who is remarkable at programming may not appreciate other hypothetical subjects like programming testing. Notwithstanding, CGPA comes up short when such a situation comes into picture.
Akshob Rao is pursuing a Bachelor of Science in Electrical Engineering from Texas A&M University in May 2016. He has relevant coursework in digital image processing, power electronics, optical engineering, and communications. Rao has skills in programming languages like C++, C, Java, HTML, JavaScript, SQL, MATLAB and computer-aided design tools. He held an internship in data management and has experience with academic projects involving microfluidic systems, amplifier design, database applications, gene expression analysis, and image segmentation. Rao is a member of IEEE and the Indian Students Association.
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Along with the spreading of online education, the importance of active support of students involved in
online learning processes has grown. The application of artificial intelligence in education allows
instructors to analyze data extracted from university servers, identify patterns of student behavior and
develop interventions for struggling students. This study used student data stored in a Moodle server and
predicted student success in course, based on four learning activities - communication via emails,
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Higher learning institutions nowadays operate in a more complex and competitive due to a high demand from prospective
students and an emerging increase of universities both public and private. Management of Universities face challenges and concerns of
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employing Decision tree and K-means data mining algorithms to model an approach to predict the performance of students in advance
so as to devise mechanisms of alleviating student dropout rates and improve on performance. In Kenya for example, there has been
witnessed an increase student enrolling in universities since the Government started free primary education. Therefore the Government
expects an increased workforce of professionals from these institutions without compromising quality so as to achieve its millennium
development and vision 2030. Backlog of students not finishing their studies in stipulated time due to poor performance is another
issue that can be addressed from the results of this research since predicting student performance in advance will enable University
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Higher learning institutions nowadays operate in a more complex and competitive due to a high demand from prospective
students and an emerging increase of universities both public and private. Management of Universities face challenges and concerns of
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so as to devise mechanisms of alleviating student dropout rates and improve on performance. In Kenya for example, there has been
witnessed an increase student enrolling in universities since the Government started free primary education. Therefore the Government
expects an increased workforce of professionals from these institutions without compromising quality so as to achieve its millennium
development and vision 2030. Backlog of students not finishing their studies in stipulated time due to poor performance is another
issue that can be addressed from the results of this research since predicting student performance in advance will enable University
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1. What is Data Science?
2. Job Roles in Data Science
3. Components of Data Science
4. Concepts of Statistics
5. Power of Data Visualization
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7. Supervised & Unsupervised Learning
8. Classification, Clustering & Recommenders
9. Text Mining & Time Series
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To take a structured training on Data Science, you can check complete details of our Data Science Certification Training course here: https://goo.gl/OCfxP2
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Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
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A high-Speed Communication System is based on the Design of a Bi-NoC Router, ...DharmaBanothu
The Network on Chip (NoC) has emerged as an effective
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Blood finder application project report (1).pdfKamal Acharya
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DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
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The Rapid growth of technology and infrastructure has made our lives easier. The
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This study Examines the Effectiveness of Talent Procurement through the Imple...DharmaBanothu
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
1. SANJIVANI K. B. P. POLYTECHNIC,
KOPARGAON
With NBA ACCREDIATED programs , Approved by AICTE, New Delhi,
Recognized by Govt. of Maharashtra, Affiliated to Maharashtra State Board of
Technical Education, Mumbai, ISO 9001:2015 Certified Institute
Name of Faculty: Prof. Vaibhav A. Parjane
1
2. [6MARKS ]
Sanjivani K. B. P. Polytechnic, Kopargaon Department of Computer Technology V. A. Parjane 2
Unit-1
Introduction to Data Structure
3. Unit Outcome
After going through this unit, the student will be able to:
1a. Classify the given type of Data Structures based on their characteristics.
1b. Explain complexity of the given algorithm in terms of time and space.
1c. Explain the given operations to be performed on the given type of data
Sanjivani K. B. P. Polytechnic Kopargaon Department of Computer Technology V. A. Parjane 3
4. Data
Data is a collection of numbers, alphabets and symbols combined to
represent information.
A computer takes input as raw data, processes it and produces output
as refined data.
Data in computer is represented in binary format.
Example:
1. An integer number is represented by its binary equivalent.
2. A negative number is represented using 2’s complement
representation.
3. A character is represented using its ASCII code.
Sanjivani K. B. P. Polytechnic Kopargaon Department of Computer Technology V. A. Parjane 4
5. Atomic data
It is a data that is a single, non-decomposable entity.
For Eg:
An integer value 786 may be considered as a single
integer value.
The value 786 can be divided in three digits ‘7’, ‘8’, ‘6’, but
then the meaning is lost.
Character value ‘A’ cannot be further divided.
Sanjivani K. B. P. Polytechnic Kopargaon Department of Computer Technology V. A. Parjane 5
6. Composite data
Opposite of atomic data is composite data.
It can be broken out into sub fields that have meaning i.e. it
can be divided into atomic data.
For Eg:
Date of Birth ( say 15/8/1995) can be separated into 3
atomic values as –
1) Day of month
2) Month
3) Year
Sanjivani K. B. P. Polytechnic Kopargaon Department of Computer Technology V. A. Parjane 6
Birthdate
Day Month Year
Atomic data
Composite
data
7. Data Structure
A Data structure is an arrangement of data, either
in the computer’s memory or on the disk
storage.
A D.S is an aggregation of atomic and composite
data types into a set with defined relationships.
It means a set of rules that holds the data
together.
Sanjivani K. B. P. Polytechnic Kopargaon Department of Computer Technology V. A. Parjane
7
8. Data Structure: Concept
In computer science, a data structure
is a particular way of storing and organizing data in a
computer so that it can be used efficiently.
is about collection of data values, the relationships
among them, and the functions or operations that can
be applied to the data.
Sanjivani K. B. P. Polytechnic Kopargaon Department of Computer Technology V. A. Parjane 8
9. Data Structure: Concept
A data structure is made of:
A set of data values
A set of functions specifying the operations permitted
on the data values.
A set of axioms describing how these operations work.
Sanjivani K. B. P. Polytechnic Kopargaon Department of Computer Technology V. A. Parjane 9
10. Akash is 17 years old.
Akash is in the SYIF Class.
Akash got 85 marks in Programming in 'C'.
Let ‘name’, ‘age’, ‘class’, ‘marks’ and ‘subject’ be some defined variables. Now,
let us assign a value to each of these variables from the above statements.
𝑵𝒂𝒎𝒆 = 𝑨𝒌𝒂𝒔𝒉
𝑪𝒍𝒂𝒔𝒔 = 𝑺𝒀𝑰𝑭
𝑨𝒈𝒆 = 𝟏𝟕
𝑴𝒂𝒓𝒌𝒔 = 𝟖𝟓
𝑺𝒖𝒃𝒋𝒆𝒄𝒕 = 𝑷𝒓𝒐𝒈𝒓𝒂𝒎𝒎𝒊𝒏𝒈 𝒊𝒏 ′𝑪′
Sanjivani K. B. P. Polytechnic Kopargaon Department of Computer Technology V. A. Parjane
10
For example, consider the following statements:
Fig 1.1: Relation between data and information