Mastering Data Science

Unraveling Patterns and Predictive Analytics for Building Intelligent Systems
1. Auflage 2026
ISBN: 978-1-77964-050-5
Verlag: Apple Academic Press Inc.

Buch, Englisch, 536 Seiten, Format (B × H): 178 mm x 254 mm

In an era defined by explosive data growth, understanding data science is more essential than ever. This book offers a clear and practical introduction to the field, equipping readers with the tools to collect, clean, analyze, and interpret data for informed decision-making.

Covering core concepts and modern techniques, the book guides readers through data analysis, statistics, machine learning, and big data technologies, supported by real-world examples, case studies, and hands-on exercises. Complex topics are presented in an accessible way, enabling a strong grasp of data patterns, algorithms, and analytical thinking.

The volume also addresses emerging trends and ethical considerations, including data privacy and responsible AI use. Designed for students, early learners, and professionals across disciplines such as business, healthcare, finance, and marketing, this book serves as both a foundational guide and a practical resource for applying data science in real-world contexts.

Zielgruppe

Academic and Postgraduate

Autoren/Hrsg.

Bhadouria, Aashi Singh

Ahirwar, Anamika

Fachgebiete

• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse

Inhaltsverzeichnis

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

Über Autor(innen)

Aashi Singh Bhadouria is an Assistant Professor of Computer Science and Engineering at the Madhav Institute of Technology and Science, Gwalior, India. She has published numerous research papers in national and international journals and has participated in several conferences. Her research interests include digital image processing, computer vision, machine learning, natural image processing, big data computing, and artificial intelligence. She has supervised undergraduate and postgraduate students on major, minor, and internship projects and is a member of IEEE.

Anamika Ahirwar, PhD, is Professor and Head of the Department of Computer Science and Engineering at the Compucom Institute of Technology and Management, Rajasthan, India. With over 21 years of academic and research experience, she has published more than 80 papers in indexed journals and conference proceedings. She has authored and edited several books with leading international publishers and has received multiple honors, including the I2OR National Eminent Researcher Award, Academic Influencer Award, and Faculty Excellence Award. Dr. Ahirwar holds five published patents and has supervised numerous postgraduate and doctoral scholars. She also serves as a reviewer and editorial board member for reputed journals and conferences, contributing actively to advancements in emerging technologies.