- English
- ePUB (mobile friendly)
- Available on iOS & Android
eBook - ePub
Mathematical Modeling for Big Data Analytics
About this book
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
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Yes, you can access Mathematical Modeling for Big Data Analytics by Passent El-Kafrawy,Mohamed F. El-Amin in PDF and/or ePUB format, as well as other popular books in Computer Science & Data Processing. We have over one million books available in our catalogue for you to explore.
Information
Table of contents
- Title of Book
- Part I: Theoretical foundation
- Part II: Data specific applications
- Index