Authors: Dr. Rahul Nandkumar Khadke, Dr. Pankaj Suresh Kadu, Prof. Shital Pankaj Kadu, and Prof. Pooja Dwarkadas Kherdekar

ISBN: 978-81-69297-87-5

DOI: https://doi.org/10.59646/687

Date of Publication: May 13, 2026

Cite this book: Rahul NK, Pankaj SK, Shital PK, and Pooja DK, (2026), Modern Data Science: Big Data, AI, and Real-World Applications, San International Scientific Publications, ISBN: 978-81-69297-87-5, DOI: https://doi.org/10.59646/687

Preface

The twenty-first century has witnessed an unprecedented explosion in the generation, collection, and utilization of data. From social media interactions and online transactions to scientific experiments, healthcare systems, industrial automation, and smart cities, data has become one of the most valuable resources driving innovation and decision-making across the globe. Organizations today are no longer guided solely by intuition or traditional business practices; instead, they increasingly rely on data-driven insights, predictive intelligence, and automated systems powered by artificial intelligence and machine learning. In this rapidly evolving digital landscape, data science has emerged as a multidisciplinary field that combines statistics, computing, mathematics, domain expertise, and artificial intelligence to transform raw data into meaningful knowledge and actionable solutions. Modern Data Science: Big Data, AI, and Real-World Applications has been carefully designed to provide students, researchers, educators, and industry professionals with a comprehensive understanding of the foundational principles, modern technologies, analytical methods, and practical applications that define contemporary data science. The book systematically explores the complete data science lifecycle, beginning with data acquisition, preprocessing, visualization, and exploratory analysis, and progressing toward advanced machine learning, deep learning, big data architectures, distributed systems, cloud platforms, MLOps, predictive analytics, and intelligent decision systems.

Special emphasis has been placed on integrating theoretical concepts with practical relevance, enabling readers to understand not only how modern data-driven systems function, but also why they are essential in solving complex real-world problems across domains such as healthcare, finance, manufacturing, cybersecurity, transportation, natural language processing, and smart infrastructure. The text further highlights the importance of ethical AI, responsible data governance, scalability, automation, and security in modern analytical ecosystems, ensuring that readers appreciate the social, technical, and organizational implications of data-driven technologies. Each unit has been structured in a progressive and learner-friendly manner, covering foundational topics before advancing toward sophisticated analytical and AI methodologies, thereby making the book suitable for undergraduate and postgraduate curricula as well as professional self-learning. By bridging the gap between academic foundations and industrial practices, this book aims to equip readers with the conceptual clarity, technical knowledge, and practical perspective necessary to thrive in the era of big data and intelligent systems. It is our sincere hope that this book will inspire curiosity, analytical thinking, innovation, and responsible use of technology among learners and practitioners who aspire to contribute meaningfully to the future of data science and artificial intelligence.