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Advanced Mathematical Applications in Data Science
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Editors: Biswadip Basu Mallik, Kirti Verma, Rahul Kar, Ashok Kumar Shaw, Sardar M. N. Islam (Naz)

Advanced Mathematical Applications in Data Science

eBook: US $59 Special Offer (PDF + Printed Copy): US $94
Printed Copy: US $65
Library License: US $236
ISBN: 978-981-5124-85-9 (Print)
ISBN: 978-981-5124-84-2 (Online)
Year of Publication: 2023
DOI: 10.2174/97898151248421230101
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Introduction

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.


eBook: US $59 Special Offer: US $94 Printed Copy: US $65 Library License: US $236

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