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): 156 mm x 234 mm

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.

Zielgruppe

Academic and Postgraduate

Autoren/Hrsg.

Bhadouria, Aashi Singh

Ahirwar, Anamika

Fachgebiete

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

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 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, is currently a Professor and Head of the BCA Program in the Computer Science and Engineering Department at Compucom Institute of Technology & Management, Jaipur, Rajasthan, India. With 21 years of experience in teaching and research, she has published over 60 research papers in prestigious national and international journals and conferences. She has contributed chapters to several internationally edited books. Additionally, she has authored and reviewed numerous books with both national and international publishers and holds five patents. Her research interests include medical imaging, data mining, celestial sound, Internet of Things (IoT), artificial intelligence, and machine learning. Dr. Ahirwar has delivered numerous expert and guest lectures, participated in seminars, and chaired sessions at various IEEE and other international conferences. She serves as a reviewer for leading publishers, including IEEE, Springer, Scrivener Wiley, and IGI Global, and is an editorial board member for IEEE conferences and other reputed international journals. She is also a member of the International Association of Engineers (IAENG), Hong Kong. She has supervised many research projects and has been recognized with several awards, including the National Eminent Researcher Award, National Eminent Academic Influencer Award, Academic ICON Award Shikshak, and Best Research Publication Award. Dr. Ahirwar earned her PhD in Computer Applications from Rajiv Gandhi Proudyogiki Vishwavidyalaya (RGPV), Bhopal, India. She holds a Master of Computer Applications (MCA) from Government Gitanjali Girls PG College, Bhopal, and a Bachelor of Science (BSc) in Mathematics from Government K.R.G. PG Autonomous College, Gwalior, Madhya Pradesh, India.