Advanced Mathematical Applications in Data Science E-Book

• Herausgeber: Bentham Science Publishers
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 293

Veröffentlichungsjahr: 2000

Beschreibung

Advanced Mathematical Applications in Data Science comprehensively explores the crucial role mathematics plays in the field of data science. Each chapter is contributed by scientists, researchers, and academicians. The 13 chapters cover a range of mathematical concepts utilized in data science, enabling readers to understand the intricate connection between mathematics and data analysis. The book covers diverse topics, including, machine learning models, the Kalman filter, data modeling, artificial neural networks, clustering techniques, and more, showcasing the application of advanced mathematical tools for effective data processing and analysis. With a strong emphasis on real-world applications, the book offers a deeper understanding of the foundational principles behind data analysis and its numerous interdisciplinary applications. This reference is an invaluable resource for graduate students, researchers, academicians, and learners pursuing a research career in mathematical computing or completing advanced data science courses.

Key Features:
Comprehensive coverage of advanced mathematical concepts and techniques in data science
Contributions from established scientists, researchers, and academicians
Real-world case studies and practical applications of mathematical methods
Focus on diverse areas, such as image classification, carbon emission assessment, customer churn prediction, and healthcare data analysis
In-depth exploration of data science's connection with mathematics, computer science, and artificial intelligence
Scholarly references for each chapter
Suitable for readers with high school-level mathematical knowledge, making it accessible to a broad audience in academia and industry.

Leseprobe

Table of Contents

BENTHAM SCIENCE PUBLISHERS LTD.

End User License Agreement (for non-institutional, personal use)

Usage Rules:

Disclaimer:

Limitation of Liability:

General:

FOREWORD

PREFACE

List of Contributors

The Role of Mathematics in Data Science: Methods, Algorithms, and Computer Programs

Abstract

INTRODUCTION

DATA SCIENCE

MAIN MATHEMATICAL PRINCIPLES AND METHODS IMPORTANT FOR DATA SCIENCE

Linear Algebra

Matrices

System of Linear Equation

The Number of Solutions

Vectors

Loss Function

Regularization

Support Vector Machine Classification

Statistics

Probability Theory

Normal Distribution

Z Scores

The Central Limit Theorem

Some Other Statistical Methods

Skewness

Kurtosis

Applications of Statistics in Data Science through Machine Learning Algorithms

Regression

Machine Learning Using Principal Component Analysis to Reduce Dimensionality

Mathematical Basis of PCA

Classification

K-Nearest Neighbor

Naive Bayes

Calculus

Optimization or Operational Research Methods

Dynamic Optimization Model

Stochastic Optimization Methods

Some Other Methods

Computer Programs

CONCLUDING REMARKS

REFERENCES

Kalman Filter: Data Modelling and Prediction

Abstract

INTRODUCTION

Why Kalman Filter?

UNDERSTANDING THE KALMAN FILTER

What is Kalman Filter?

State Space Approach

Mean Squared Error

KALMAN FILTER EQUATIONS

GENERAL APPLICATIONS OF KALMAN FILTER

KALMAN FILTER EQUATIONS IN ONE DIMENSION

EXAMPLE 1: FINDING THE TRUE VALUE OF TEMPERATURE

First Iteration

Second Iteration

EXAMPLE 2: FINDING THE TRUE VALUE OF ACCELERATION DUE TO GRAVITY

EXAMPLE 3: VERIFYING HUBBLE’S LAW

LIMITATIONS OF KALMAN FILTER

OTHER FILTERS

FUTURE PROSPECTS

CONCLUDING REMARKS- KALMAN FILTER IN A NUTSHELL

APPENDIX – BASIC CONCEPTS

A.1. LINEAR DYNAMIC SYSTEMS

A.2. ERROR COVARIANCE MATRIX

A.3. TULLY FISHER RELATION

A.4. RED SHIFTS AND RECESSIONAL VELOCITY

REFERENCES

The Role of Mathematics and Statistics in the Field of Data Science and its Application

Abstract

INTRODUCTION

Data Science

DATA SCIENCE IN MATHEMATICS

MATH AND DATA SCIENCE IN EDUCATION

TYPES OF DATA SCIENCE IN MATH

Linear Algebra

APPLICATION OF LINEAR ALGEBRA IN DATA SCIENCE

Loss Function

Mean Squared Error

MEAN ABSOLUTE ERROR

COMPUTER VISION

CALCULUS

CALCULUS IN MACHINE LEARNING

APPLICATIONS IN MEDICAL SCIENCE

APPLICATION IN ENGINEERING

APPLICATIONS IN RESEARCH ANALYSIS

APPLICATIONS IN PHYSICS

STATISTICS

Types of Statistics in Data Science

Descriptive Statistics

Inferential Statistics

Application of Statistics in the Field of Study

VITAL STATISTICS IDEAS OBTAINING STARTED

DISTRIBUTION OF DATA POINT

APPLIED MATH EXPERIMENTS AND SIGNIFICANCE TESTING

NONPARAMETRIC STATISTICAL METHODS

APPLICATION OF STATISTICS IN DATA SCIENCE ANALYZING AND CATEGORIZING DATA

NUMERIC DATA & CATEGORICAL DATA

EXPLORATORY KNOWLEDGE ANALYSIS

SIGNIFICANCE TESTS

Null Hypotheses

Alternative Hypotheses

CHI-SQUARED CHECK

STUDENT’S T-TEST

ANALYSIS OF VARIANCE CHECK (ANOVA)

Unidirectional

Two-ways

RESERVATION AND PREDICTION

Linear Regression

Logistic Regression

CLASSIFICATION OF KNOWLEDGE SCIENCE IN STATISTICS

Naive Mathematician

K-nearest Neighbors

PROBABILITY

FREQUENCY TABLES

HISTOGRAM

CONTINUOUS RANDOM VARIABLES

SKEWNESS DISTRIBUTION

RIGHT SKEW DISTRIBUTION

LEFT SKEW DISTRIBUTION

NORMAL DISTRIBUTION

EXPONENTIAL DISTRIBUTION

UNIFORM DISTRIBUTION

POISSON DISTRIBUTION

IMPORTANT OF INFORMATION SCIENCE

DATA WHILE NOT KNOWLEDGE SCIENCE

DATA CAN PRODUCE HIGHER CLIENT EXPERTISE

DATA USED ACROSS VERTICALS

POWER OF INFORMATION SCIENCE

FUTURE OF INFORMATION SCIENCE

DATA SCIENCE IN TRADE

BENEFITS OF KNOWLEDGE SCIENCE

STATISTICAL INFORMATION

DATA SCIENCE IS VERY IMPORTANT IN THE MODERN WORLD

DATA INDIVIDUAL

DATA SCIENCE WORKS

CONCLUDING REMARKS

REFERENCES

Bag of Visual Words Model - A Mathematical Approach

Abstract

INTRODUCTION

HISTOGRAM REWEIGHTING – TF – IDF APPROACH

COST MATRIX GENERATION

EUCLIDEAN DISTANCE AND COSINE DISTANCE

MODEL DESCRIPTION

Histogram Generation for Image

Computation of Cost Matrix

Reweighting of Histogram using TF – IDF

Visualization of Original Euclidean, Reweighted Euclidean

Normalization of Original Histogram

Checking for Similarity of the Normalized Histogram

Visual Comparison of Histograms

CONCLUSION

REFERENCES

A Glance Review on Data Science and its Teaching: Challenges and Solutions

Abstract

INTRODUCTION

THE IMPACT OF DATA SCIENCE ON THE SOCIETY

EDUCATIONAL GOALS OF DATA SCIENCE

DATA SCIENCE IN PRACTICE AS A PROBLEM SOLVING

LITERATURE REVIEW

DEMANDS OF THE DATA SCIENCE INDUSTRY AND THE DATA SCIENCE CURRICULUM

INHERENT PROBLEMS IN DATA SCIENCE CURRICULA DEVELOPMENT

TEACHING DATA SCIENCE

CONCLUDING REMARKS

REFERENCES

Optimization of Various Costs in Inventory Management using Neural Networks

Abstract

INTRODUCTION

RELATED WORK

ASSUMPTION AND NOTATIONS

MATHEMATICAL FORMULATION OF MODEL AND ANALYSIS

MULTILAYER-FEED FORWARD NEURAL NETWORKS

WORKING ON PROPOSED SYSTEM

EXPERIMENTAL RESULTS AND ANALYSIS

CONCLUDING REMARKS

REFERENCES

Cyber Security in Data Science and its Applications

Abstract

INTRODUCTION

DATA SCIENCE TODAY

MOTIVE AND SIGNIFICANCE OF DATA SCIENCE

IMPORTANCE OF DATA

IMPORTANCE OF DATA SCIENCE

MOTIVATION OF DATA IMPORTANT INDUSTRIES

DATA SCIENCE FOR PREFERABLE TRADE

DATA ANALYTICS FOR CLIENT ACQUISITION

DATA ANALYTICS FOR REVOLUTION

DATA SCIENCE FOR ENHANCESURVIVAL

PART OF DATA SCIENCE IN CYBER SECURITY

CONNECTION ALLYING SUBSTANTIAL DATA AND CYBER SECURITY

DATA SCIENCE USED IN CYBER SECURITY

Negative Hoping on “Lab-based” Order

Utilize Entrance to Sufficient Data

Specialize in this Irregularity

Utilize Data Science in a Logical Approach

UPCOMING CHALLENGES IN CYBER SECURITY DATA SCIENCE

OPERATE CLASSIFICATION ISSUES IN CYBERSECURITY DATAFILE

RELIABILITY SCHEME RULE

AMBIENCE PERCEPTON IN CYBER SECURITY

ATTRIBUTE ENGINEERING IN CYBER SECURITY

PROMINENT SECURITY ACTIVE CREATION AND ARRAY

DISCUSSION

CONCLUDING REMARKS

REFERENCES

Artificial Neural Networks for Data Processing: A Case Study of Image Classification

Abstract

INTRODUCTION

ARCHITECTURE OF ANN

Input Layer

Hidden Layer

Output Layer

BENEFITS OF ARTIFICIAL NEURAL NETWORK (ANN)

Ability for Processing

Network-based Data Storage

Capacity to Function Despite a Lack of Knowledge

Transmission of Memory

Acceptance for Faults

DISADVANTAGES

Ensure that the Network Structure is Correct

Network Activity that has Gone Unnoticed

Network's Life Expectancy is Unknown

WORKING OF ANN

TYPES OF ANN

Feedback ANN

Feed-Forward

SIMPLE NEURAL NETWORK

LITERATURE REVIEW

PROPOSED SYSTEM

RESULTS AND DISCUSSION

CONCLUSION

REFERENCES

Carbon Emission Assessment by Applying Clustering Technique to World’s Emission Datasets

Abstract

INTRODUCTION

Research Methodology

Limitations of the Study

Feature Extraction and Engineering

Data Extraction

Standardizing and Scaling

Identification of Clusters by Elbow Method

Cluster Formation

RESULTS AND ANALYSIS

Cluster One – High Rainfall

Cluster Two

Cluster Three

Cluster Four

Cluster Five

Cluster Six

CONCLUSION

REFERENCES

A Machine Learning Application to Predict Customer Churn: A Case in Indonesian Telecommunication Company

Abstract

INTRODUCTION

LITERATURE REVIEW AND CONTRIBUTION

RESEARCH DESIGN

Dataset

Data Preparation

Exploratory Data Analysis

Features Selection

MACHINE LEARNING APPLICATION

Ridge Classifier

Gradient Booster

Adaptive Boosting

Bagging Classifier

k-Nearest Neighbor

Decision Tree

Logistic Regression

Random Forest

MODEL PERFORMANCE AND EVALUATION

RESULT

CONCLUDING REMARKS

REFERENCES

A State-Wise Assessment of Greenhouse Gases Emission in India by Applying K-mean Clustering Technique

Abstract

INTRODUCTION

Introduction to Cluster Analysis

Research Methodology

Data Source

Period of Study

Software used for Data Analysis

Model Applied

Limitations of the Study

Future Scope

Research is Carried Out in Five Steps

Feature Extraction and Engineering

Data Extraction

Standardizing and Scaling

Identification of Clusters by Elbow Method

Cluster formation

RESULTS AND ANALYSIS

Cluster One

Cluster Two

Cluster Three

CONCLUSION

REFERENCES

Data Mining Techniques: New Avenues for Heart Disease Prediction

Abstract

INTRODUCTION

ADVERSE IMPACT OF CARDIOVASCULAR DISEASES IN INDIA

Smoking

Hyperglycaemia

Hypertension

Obesity

Dyslipidaemia

Dietary Habits and Exercise

Genetic Risk Factors

Treatment Gaps

The Multilayer Perceptron (MLP)

Coactive Neuro-Fuzzy Inference System (CANFIS)

Aptamer Biochip-based CDSS –ensemble (Apta CDSS-E)

Intelligent Heart Disease Prediction System (IHDPS)

Intelligent and Effective Heart Attack Prediction System (IEHPS)

Decision Tree Fuzzy System (DTFS)

CONCLUDING REMARKS

REFERENCES

Data Science and Healthcare

Abstract

INTRODUCTION

So, What is Data Science?

Data Science Techniques vs. Data Mining

Now, Why is Data Essential?

What is an Ideal Data Scientist?

Technical and Soft Skills for Healthcare Data Scientists

Technical Skills

Soft Skills

Why is Data Science so Crucial for Organizations?

HEALTHCARE DATA: CHALLENGES AND OPPORTUNITIES

Opportunities

Defining Big Data

Challenges

Data Science Opportunities for Healthcare

HEALTHCARE LEADERSHIP

Transactional leader

Transformational leadership

CONCLUDING REMARKS

REFERENCES

Advanced Mathematical Applications in Data Science

Edited by

Biswadip Basu Mallik

Department of Basic Science and Humanities

Institute of Engineering & Management, Kolkata

West Bengal, India

Kirti Verma

Department of Engineering Mathematics

Lakshmi Narain College of Technology, Jabalpur

Madhya Pradesh, India

Rahul Kar

Department of Mathematics

Kalyani Mahavidyalaya, Kalyani

West Bengal, India

Ashok Kumar Shaw

Department of Basic Sciences and Humanities

Budge Budge Institute of Technology

Budge Budge, Kolkata

West Bengal, India

&

Sardar M. N. Islam (Naz)

ISILC, Victoria University

Melbourne, Australia

BENTHAM SCIENCE PUBLISHERS LTD.

End User License Agreement (for non-institutional, personal use)

This is an agreement between you and Bentham Science Publishers Ltd. Please read this License Agreement carefully before using the book/echapter/ejournal (“Work”). Your use of the Work constitutes your agreement to the terms and conditions set forth in this License Agreement. If you do not agree to these terms and conditions then you should not use the Work.

Bentham Science Publishers agrees to grant you a non-exclusive, non-transferable limited license to use the Work subject to and in accordance with the following terms and conditions. This License Agreement is for non-library, personal use only. For a library / institutional / multi user license in respect of the Work, please contact: [email protected].

Usage Rules:

All rights reserved: The Work is the subject of copyright and Bentham Science Publishers either owns the Work (and the copyright in it) or is licensed to distribute the Work. You shall not copy, reproduce, modify, remove, delete, augment, add to, publish, transmit, sell, resell, create derivative works from, or in any way exploit the Work or make the Work available for others to do any of the same, in any form or by any means, in whole or in part, in each case without the prior written permission of Bentham Science Publishers, unless stated otherwise in this License Agreement.You may download a copy of the Work on one occasion to one personal computer (including tablet, laptop, desktop, or other such devices). You may make one back-up copy of the Work to avoid losing it.The unauthorised use or distribution of copyrighted or other proprietary content is illegal and could subject you to liability for substantial money damages. You will be liable for any damage resulting from your misuse of the Work or any violation of this License Agreement, including any infringement by you of copyrights or proprietary rights.

Disclaimer:

Bentham Science Publishers does not guarantee that the information in the Work is error-free, or warrant that it will meet your requirements or that access to the Work will be uninterrupted or error-free. The Work is provided "as is" without warranty of any kind, either express or implied or statutory, including, without limitation, implied warranties of merchantability and fitness for a particular purpose. The entire risk as to the results and performance of the Work is assumed by you. No responsibility is assumed by Bentham Science Publishers, its staff, editors and/or authors for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products instruction, advertisements or ideas contained in the Work.

Limitation of Liability:

In no event will Bentham Science Publishers, its staff, editors and/or authors, be liable for any damages, including, without limitation, special, incidental and/or consequential damages and/or damages for lost data and/or profits arising out of (whether directly or indirectly) the use or inability to use the Work. The entire liability of Bentham Science Publishers shall be limited to the amount actually paid by you for the Work.

General:

Any dispute or claim arising out of or in connection with this License Agreement or the Work (including non-contractual disputes or claims) will be governed by and construed in accordance with the laws of Singapore. Each party agrees that the courts of the state of Singapore shall have exclusive jurisdiction to settle any dispute or claim arising out of or in connection with this License Agreement or the Work (including non-contractual disputes or claims).Your rights under this License Agreement will automatically terminate without notice and without the need for a court order if at any point you breach any terms of this License Agreement. In no event will any delay or failure by Bentham Science Publishers in enforcing your compliance with this License Agreement constitute a waiver of any of its rights.You acknowledge that you have read this License Agreement, and agree to be bound by its terms and conditions. To the extent that any other terms and conditions presented on any website of Bentham Science Publishers conflict with, or are inconsistent with, the terms and conditions set out in this License Agreement, you acknowledge that the terms and conditions set out in this License Agreement shall prevail.

Bentham Science Publishers Pte. Ltd. 80 Robinson Road #02-00 Singapore 068898 Singapore Email: [email protected]

FOREWORD

There is a need to provide a new, up-to-date, comprehensive, and innovative review of the developments to show, integrate, synthesize and provide future research directions in the applications of advanced mathematics in data science. Therefore, this book has made a valuable contribution to the literature by providing systematic reviews on the interrelationships between mathematics, statistics, and computer science.

Data Science is one of the most significant advances of this century. It deals with the collection, preparation, analysis, visualization, management, and preservation of this data – both structured and unstructured. Data science incorporates several technologies and academic disciplines to discover, extract, compile, process, analyze, interpret, and visualize data. It includes mathematics, statistics, computer science and programming, statistical modeling, database technologies, signal processing, data modeling, artificial intelligence, machine learning, natural language processing, visualization, and predictive analytics.

Mathematics is very important in the field of data science as concepts within mathematics aid in identifying patterns and assist in creating algorithms. Understanding various statistics and probability theory notions is key to implementing such algorithms in data science.

This book provides a comprehensive account of the areas of the applications of advanced mathematics in data science. It has covered many significant issues, methods, and applications of data science and mathematics in some crucial areas, such as The Role of Mathematics in Data Science, Mathematical Modeling in Data Science, Mathematical Algorithms for Artificial Intelligence and Big Data, Soft Computing in Data Science, Data Analytics: Architecture, Opportunities, And Open Research Challenges, Linear Regression, Logistic Regression, Neural Networks, and a Review on Data Science Technologies.

The book has implications for data science modeling and many real-life applications. Many readers, including undergraduate university students, evening learners, and learners participating in online data science courses, will be benefitted from this book.

I recommend this book to all interested in data science technologies, mathematical modeling, and applications.

S.B. Goyal Faculty of Information Technology City University Petaling Jaya, 46100, Malaysia

PREFACE

The title of our book is Advanced Mathematical Applications in Data Science. The book is dealing specially Data Analysis – Mining and analysis of Big Data, Mathematical modelling in Data science, Mathematical Algorithms for Artificial Intelligence and Big Data, using MATLAB with Big Data from sensors and IOT devices, the relationship between Big data and Mathematical modelling, Big IOT Data analytics, Architecture, opportunities and open research challenges, the role of Mathematics in Data science, linear regression, logistic regression, Neural networks, Decision tree, applications of linear algebra in Data science, Big Data and Big Data analytics, concepts, types and techniques, foundation of Data science, fifty year of Data sciences, Health Bank – a world health for Data science applications in Healthcare, Radio frequency identification, a new opportunity for Data science, towards a system building agenda for data, semantic representation of Data science properly, a review on Data science techniques, Big Data: the next era of Information and Data science in medical imaging, Data science and healthcare, soft computing in Data science, foundation for private, fair and robust Data science, Data science fundamental principles, practical Data sciences for Actuarial task etc.

The scope of this book is not only limited to above highlighted areas but much more than that. Today as all of us are aware that most of the decision making and marketing strategies are data driven. So the research in this field is very much important and useful for any kind of day to day decision making and for marketing strategies etc. Finally we would thank the Bentham Science publishing house for giving us an opportunity to explore this field.

Biswadip Basu Mallik Department of Basic Science & Humanities Institute of Engineering & Management Kolkata, West Bengal IndiaKirti Verma Department of Engineering Mathematics Lakshmi Narain College of Technology Jabalpur Madhya Pradesh IndiaRahul Kar Department of Mathematics Kalyani Mahavidyalaya, Kalyani West Bengal, IndiaAshok Kumar Shaw Department of Basic Sciences and Humanities Budge Budge Institute of Technology, Budge Budge Kolkata, West Bengal India &

List of Contributors

Armel DjangoneDakota State University, Business Analytics and Decision Support, Washington Ave N, Madison, United StatesArnob SarkarNational Atmospheric Research Laboratory, Department of Space, Andhra Pradesh, Government of IndiaAgus Tri WibowoDepartment of Consumer Service, PT Telekomunikasi Indonesia, Jakarta, IndonesiaAndi Chaerunisa Utami PutriDepartment of Consumer Service, PT Telekomunikasi Indonesia, Jakarta, IndonesiaBhim SinghDepartment of Basic Science, Sardar Vallabh Bhai Patel University of Agriculture and Technology, Meerut (U.P.), IndiaCharanarur PanemDepartment of Cyber Security and Digital Forensics, National Forensic Sciences University Tripura Campus, Tripura, IndiaJ. VijaylaxmiPVKK Degree & PG College, Anantapur, Andhra Pradesh, IndiaJayaraj RamasamyDepartment of IT, Botho University, Gaborone, BotswanaM. VaralakshmiMarudhar Kesari Jain College for Women, Vaniyambadi, Tirupattur(dt), Tamilnadu, IndiaMeetu LuthraDepartment of Physics, Bhaskaracharya College of Applied Sciences, University of Delhi, Delhi, IndiaMaheswariDepartment of Computer Applications, Fatima College, Madurai, IndiaMuhammad Reza TribosniaDepartment of Consumer Service, PT Telekomunikasi Indonesia, Jakarta, IndonesiaM. Mujiya UlkhaqDepartment of Industrial Engineering, Diponegoro University, Kota Semarang, Indonesia Department of Economics and Management, University of Brescia, Brescia BS, ItalyNeha BhardwajDepartment of Mathematics, School of Basic Sciences and Research, Sharda University, Noida, Uttar Pradesh, IndiaNitin Jaglal UntwalMaharashtra Institute of Technology, Aurangabad, IndiaI. P. ThulasiMarudhar Kesari Jain College for Women, Vaniyambadi, Tirupattur(dt), Tamilnadu, IndiaPriya PanneerDepartment of Mathematics, Mathematics Marudhar Kesari Jain College for Women, Vaniyambadi, Tirupattur, Tamilnadu, IndiaPrerna SharmaDepartment of Basic Science, Sardar Vallabh Bhai Patel University of Agriculture and Technology, Meerut (U.P.), IndiaRashmi SinghAmity Institute of Applied Sciences, Amity University, Noida, Uttar Pradesh, IndiaR. N. RavikumarDepartment of Computer Engineering, Marwadi University, Gujarat, IndiaRevalda PutawaraDepartment of Consumer Service, PT Telekomunikasi Indonesia, Jakarta, IndonesiaSardar M. N. Islam (Naz)ISILC, Victoria University, Melbourne, AustraliaSathiyapriya MuraliDepartment of Mathematics, Mathematics Marudhar Kesari Jain College for Women, Vaniyambadi, Tirupattur, Tamilnadu, IndiaSrinivasa Rao GunduDepartment of Digital Forensics, Malla Reddy University, Dhulapally, Hyderabad, Telangana, IndiaS. ShitharthDepartment of Computer Science, Kebri Dehar University, Kebri Dehar, EthiopiaSoma DasLife Sciense, B.Ed. Department, Syamaprasad Institute of Education and Training, Kolkata, India. Honorary Guest Faculty, Sports Science Department, University of Calcutta, Kolkata, India

The Role of Mathematics in Data Science: Methods, Algorithms, and Computer Programs

Rashmi Singh1,*,Neha Bhardwaj2,Sardar M. N. Islam (Naz)3

1 Amity Institute of Applied Sciences, Amity University, Noida, Uttar Pradesh, India

2 Department of Mathematics, School of Basic Sciences and Research, Sharda University, Noida, Uttar Pradesh, India

3 ISILC, Victoria University, Melbourne, Australia

Abstract

The field of data science relies heavily on mathematical analysis. A solid foundation in certain branches of mathematics is essential for every data scientist already working in the field or planning to enter it in the future. In whatever area we focus on, data science, machine learning engineering, business intelligence development, data architecture, or another area of expertise, it is important to examine the several kinds of mathematical prerequisites and insights and how they're applied in the field of data science. Machine learning algorithms, data analysis and analyzing require mathematics. Mathematics is not the only qualification for a data science education and profession but is often the most significant. Identifying and translating business difficulties into mathematical ones are a crucial phase in a data scientist's workflow. In this study, we describe the different areas of mathematics utilized in data science to understand mathematics and data science together.

Keywords: Baye's theorem, Classification, Computer programs, Data science, Linear algebra, Machine learning, Matrices, Normal distribution, Optimization, Regression, System of linear equations, Vectors.

*Corresponding author Rashmi Singh: Amity Institute of Applied Sciences, Amity University, Noida, Uttar Pradesh, India; E-mail: [email protected]