Computer Science & Information Management

Mastering Data Science
Unraveling Patterns and Predictive Analytics for Building Intelligent Systems

Aashi Singh Bhadouria
Anamika Ahirwar, PhD

Mastering Data Science

Now on Press
Pub Date: August 2026
Hardback Price: see ordering info
Hard ISBN: 9781779640505
E-Book ISBN: 978-1-77964-051-2
Pages: 536pp w/index
Binding Type: Hardback / ebook
Notes: 15 color and 139 b/w illustrations

The importance of data science has never been greater than it is now, a time marked by the exponential increase of data and the transformational potential of information. This new book provides thorough and useful introduction to data science, covering the core ideas and methods needed to efficiently gather, clean, examine, analyze, and understand data. The book serves as the key to learning to take advantage of the enormous potential that data science provides and will prepare readers to handle real-world data difficulties and make data-informed choices.

The book first provides a comprehensive introduction to the field of data science, covering essential concepts, techniques, and technologies, from foundational principles to advanced topics. Through practical examples, case studies, and hands-on exercises, the book helps readers to develop practical skills in data analysis, data mining, big data technologies, statistical analysis, machine learning, and ethical considerations. By breaking down complex topics into accessible chapters, the book fosters a deep understanding of core concepts such as data patterns, statistical foundations, machine learning algorithms, and ethical considerations in data science.

In addition to covering fundamental principles, the book also explores emerging trends and challenges in the field, including the ethical implications of data-driven decision-making and future trends shaping the landscape of data science.

Topics covers the fundamentals of data science, analyzing data through patterns to make decisions with big data techniques, probability and statistics, machine learning techniques for data science, and ethics, privacy, and the future of data science. With sample questions and case studies, the book assists students in preparing for university exams and real-world applications, equipping them with the knowledge and skills needed for academic success and professional growth in the field of data science.

Key Features
• Provides a comprehensive overview of data science, covering essential concepts, data analysis techniques, big data technologies, statistical foundations, machine learning essentials, ethics, privacy considerations, and future trends.
• Includes brief introductions on the topics, practical examples, exercises, and real-world applications to help readers understand and apply data science concepts effectively in various domains.
• Organized in a progressive manner, starting from foundational concepts and gradually progressing to more advanced topics in data science, making it suitable for both beginners and intermediate learners.
• Puts a significant emphasis on big data techniques and tools, enabling readers to work with large and complex datasets using modern technologies and platforms.
• Addresses ethical issues and privacy concerns in data science, encouraging responsible data handling practices and discussing the ethical implications of data-driven decision-making.
• Includes sample question papers designed to help students prepare for university exams, reinforcing their understanding of key concepts and providing valuable exam practice.

This volume will be a valuable resource for beginners of data science as well as a refresher for data analysts, data scientists, as well as for professionals in other fields such as business, finance, healthcare, and marketing, who often need to understand data science to assess market targets, see existing and emerging trends, use data for decision-making purposes, and more.

CONTENTS:
Preface

PART 1: The Fundamentals of Data Science

1. Data Science Essentials
2. Decoding Data Patterns

PART 2: Analyzing Data: From Patterns to Decisions with Big Data Techniques

3. Data Analysis and Analytics for Uncovering Patterns
4. Data Mining Essentials for Decision-Making
5. Big Data Technologies and Tools

PART 3: Introduction to Probability and Statistics

6. Statistical Foundations of Data Science
7. Probability Distribution for Data Science

PART 4: Machine Learning Essentials: From Fundamentals to Advanced Techniques

8. Machine Learning Fundamentals
9. Supervised Learning Techniques
10. Ensemble Learning Techniques
11. Unsupervised Learning Techniques

PART 5: Ethics, Privacy, and the Future of Data Science

12. Data Science Ethics and Privacy
13. Future Trends in Data Science

PART 6: Case Studies: Data Analytics Case Studies

PART 7: Sample Questions for University Exams


Index


About the Authors / Editors:
Aashi Singh Bhadouria
Assistant Professor, Department of Computer Science and Engineering, Madhav Institute of Technology and Science in Gwalior, India

Aashi Singh Bhadouria is an Assistant Professor in the Department of Computer Science and Engineering at the Madhav Institute of Technology and Science in Gwalior, India. has published many research papers in international and national journals and attended several conferences. Her current research interests include digital image processing, computer vision, machine learning, natural image processing, big data computing, and artificial intelligence. She has supervised many students at postgraduate and graduate levels for their major, minor, and internship projects. She is also a member of the IEEE (Institute of Electrical and Electronics Engineers). She holds a bachelor degree in Computer Science and Engineering from Rajiv Gandhi Prodyogki Vishvavidhlaya (RGPV), Bhopal, India, and a master degree in Computer Science and Engineering from the Madhav Institute of Technology and Science, Gwalior, India.

Anamika Ahirwar, PhD
Professor and Head, Department of Computer Science and Engineering, Compucom Institute of Technology & Management, Jaipur, Rajasthan, India

Anamika Ahirwar, PhD, is a Professor and Head of the Department of Computer Science & Engineering at the Compucom Institute of Technology & Management, Jaipur, Rajasthan, India. She has more than 21 years of academic and research experience. She earned her PhD in Computer Applications from Rajiv Gandhi Technical University, Bhopal, where her doctoral research focused on data mining schemes for medical imaging. Her areas of expertise include medical imaging, data science, machine learning, sentiment analysis, and celestial sound. Dr. Ahirwar is a prolific researcher with over 80 publications in SCI-indexed, Scopus-indexed, and other international and national journals and conference proceedings. She has authored and edited several books with leading international publishers, including Wiley, CRC Press, IGI Global, Cambridge Scholars, and Nova Science. She is the recipient of several awards, such as the I2OR National Eminent Researcher Award, the Academic Influencer Award, and the Faculty Excellence Award. Dr. Ahirwar holds five published patents and has supervised numerous postgraduate and doctoral research scholars in computer science. As a reviewer and editorial board member for reputed journals and conferences, she actively promotes research in emerging technologies. She is a life member of professional bodies such as IAENG and I2OR and has contributed significantly as a session chair, expert speaker, and convener at many international academic forums.




Follow us for the latest from Apple Academic Press:
Copyright © 2026 Apple Academic Press Inc. All Rights Reserved.