Mathematical Modelling for Big Data Analytics is a comprehensive guidebook that explores the use of mathematical models and algorithms for analyzing large and complex datasets. The book covers a range of topics, including statistical modeling, machine learning, optimization techniques, and data visualization, and provides practical examples and case studies to demonstrate their applications in real-world scenarios. Users will find a clear and accessible resource to enhance their skills in mathematical modeling and data analysis for big data analytics. Real-world examples and case studies demonstrate how to approach and solve complex data analysis problems using mathematical modeling techniques.
This book will help readers understand how to translate mathematical models and algorithms into practical solutions for real-world problems. Coverage of the theoretical foundations of big data analytics, including qualitative and quantitative analytics techniques, digital twins, machine learning, deep learning, optimization, and visualization techniques make this a must have resource.
1. Chapter 5: An introduction to digital twins and big data analytics - p. 91 2. Chapter 6: Exploration of machine learning techniques - p. 107 3. Chapter 7: On deep leaning techniques - p. 121 4. Chapter 8: Optimization techniques for big data - p. 143 5. Chapter 9: Visualization in big data analytics - p. 157 6. Chapter 10: Ethical considerations for big data analytics - p. 171 7. Part II: Data specific applications - p. P2 8. Chapter 11: Text analytics techniques - p. 193 9. Chapter 12: Network analytics techniques - p. 205 10. Chapter 13: Spatial analytics techniques - p. 219 11. Chapter 14: Lifetime testing and sound analytical techniques - p. 241 12. Chapter 15: IoT based data analytics - p. 257 13. Index - p. 273
Mathematical Modelling for Big Data Analytics is a comprehensive guidebook that explores the use of mathematical models and algorithms for analyzing large and complex datasets. The book covers a range of topics, including statistical modeling, machine learning, optimization techniques, and data visualization, and provides practical examples and case studies to demonstrate their applications in real-world scenarios. Users will find a clear and accessible resource to enhance their skills in mathematical modeling and data analysis for big data analytics. Real-world examples and case studies demonstrate how to approach and solve complex data analysis problems using mathematical modeling techniques.
This book will help readers understand how to translate mathematical models and algorithms into practical solutions for real-world problems. Coverage of the theoretical foundations of big data analytics, including qualitative and quantitative analytics techniques, digital twins, machine learning, deep learning, optimization, and visualization techniques make this a must have resource.
1. Chapter 5: An introduction to digital twins and big data analytics - p. 91 2. Chapter 6: Exploration of machine learning techniques - p. 107 3. Chapter 7: On deep leaning techniques - p. 121 4. Chapter 8: Optimization techniques for big data - p. 143 5. Chapter 9: Visualization in big data analytics - p. 157 6. Chapter 10: Ethical considerations for big data analytics - p. 171 7. Part II: Data specific applications - p. P2 8. Chapter 11: Text analytics techniques - p. 193 9. Chapter 12: Network analytics techniques - p. 205 10. Chapter 13: Spatial analytics techniques - p. 219 11. Chapter 14: Lifetime testing and sound analytical techniques - p. 241 12. Chapter 15: IoT based data analytics - p. 257 13. Index - p. 273
