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Mathematical Modeling for Big Data Analytics

Name: Mathematical Modeling for Big Data Analytics
Brand: Elsevier Science
SKU: 9780443267352
Price: 1087.11 RON
Availability: InStock

Autor: Passent El-Kafrawy, Mohamed F. El-Amin

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Editura: Elsevier Science

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Mathematical Modelling for Big Data Analytics is a comprehensive guidebook that explores the use of...

Informații despre carte

Autor

Passent El-Kafrawy, Mohamed F. El-Amin

Limbă

engleză

Legare

Carte - Carte broșată

Publicat

2025

Pagini

250

EAN

9780443267352

ISBN

0443267359

Enbook ID

46011391

Editura

Elsevier Science

Greutate

450

Dimensiuni

191 x 235

Descriere completă

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

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