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.

• 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

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.

• 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

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.

• 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