Mathematical Modeling for Big Data Analytics

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. It 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. This book provides a clear and accessible resource for readers who are looking to enhance their skills in mathematical modeling and data analysis for big data analytics. Through real-world examples and case studies, readers will gain a deeper understanding of how to approach and solve complex data analysis problems using mathematical modeling techniques. The authors emphasize the importance of effective data visualization and provide guidance on how to present and communicate the results of data analysis effectively to stakeholders. Researchers and analysts face a variety of challenges due to rapidly changing technologies and keeping up with the latest mathematical and statistical techniques for big data analytics. Mathematical Modelling for Big Data Analytics helps readers understand how to translate mathematical models and algorithms into practical solutions for real-world problems. The book begins with 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. The second part of the book concludes with data-specific applications such as text analytics, network analytics, spatial analytics, timeseries analytics, sound analytics, and IoT-based analytics techniques. Provides comprehensive coverage of mathematical and statistical techniques for big data analyticsGives readers practical guidance on how to approach and solve complex data analysis problems using mathematical modeling techniques, with an emphasis on effective communication and presentation of resultsIncludes leading-edge information on current trends and emerging technologies and tools in the field of big data analytics, with discussions on ethical considerations and data privacy INDICE: Part I: Theoretical Foundation1. An Overview of Big Data Analytics2. Mathematical and Statistical Concepts Underlying Big Data Analytics3. Qualitative Analytics Techniques4. Quantitative Analytics Techniques5. An Introduction to Digital Twins and their Use in Big Data Analytics6. Exploration of Machine Learning Techniques7. On Deep Learning Techniques8. Optimization Techniques for Big Data Analytics9. Visualization in Big Data Analytics10. Ethical Considerations for Big Data AnalyticsPart II: Data-Specific Application11. Text Analytics Techniques12. Network Analytics Techniques13. Spatial Analytics Techniques14. Timeseries and Sound Analytics Techniques15. IoT based data Analytics

• ISBN: 978-0-443-26735-2
• Editorial: Morgan Kaufmann
• Encuadernacion: Rústica
• Páginas: 250
• Fecha Publicación: 01/01/2025
• Nº Volúmenes: 1
• Idioma: Inglés