Mathematical Modeling for Big Data Analytics
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Product details:
- Publisher Morgan Kaufmann
- Date of Publication 1 November 2025
- ISBN 9780443267352
- Binding Paperback
- No. of pages302 pages
- Size 276x216 mm
- Language English 700
Categories
Long description:
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.
- Provides comprehensive coverage of mathematical and statistical techniques for big data analytics
- Gives 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 results
- Includes 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
Table of Contents:
Part I: Theoretical Foundation
1. An Overview of Big Data Analytics
2. Mathematical and Statistical Concepts Underlying Big Data Analytics
3. Qualitative Analytics Techniques
4. Quantitative Analytics Techniques
5. An Introduction to Digital Twins and their Use in Big Data Analytics
6. Exploration of Machine Learning Techniques
7. On Deep Learning Techniques
8. Optimization Techniques for Big Data Analytics
9. Visualization in Big Data Analytics
10. Ethical Considerations for Big Data Analytics
Part II: Data-Specific Application
11. Text Analytics Techniques
12. Network Analytics Techniques
13. Spatial Analytics Techniques
14. Timeseries and Sound Analytics Techniques
15. IoT based data Analytics
