Big Data Quantification for Complex Decision-Making

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  • Big Data Quantification for Complex Decision-Making Book Detail

  • Author : Zhang, Chao
  • Release Date : 2024-04-16
  • Publisher : IGI Global
  • Genre : Business & Economics
  • Pages : 328
  • ISBN 13 :
  • File Size : 25,25 MB

Big Data Quantification for Complex Decision-Making by Zhang, Chao PDF Summary

Book Description: Many professionals are facing a monumental challenge: navigating the intricate landscape of information to make impactful choices. The sheer volume and complexity of big data have ushered in a shift, demanding innovative methodologies and frameworks. Big Data Quantification for Complex Decision-Making tackles this challenge head-on, offering a comprehensive exploration of the tools necessary to distill valuable insights from datasets. This book serves as a tool for professionals, researchers, and students, empowering them to not only comprehend the significance of big data in decision-making but also to translate this understanding into real-world decision making. The central objective of the book is to examine the relationship between big data and decision-making. It strives to address multiple objectives, including understanding the intricacies of big data in decision-making, navigating methodological nuances, managing uncertainty adeptly, and bridging theoretical foundations with real-world applications. The book's core aspiration is to provide readers with a comprehensive toolbox, seamlessly integrating theoretical frameworks, practical applications, and forward-thinking perspectives. This equips readers with the means to effectively navigate the data-rich landscape of modern decision-making, fostering a heightened comprehension of strategic big data utilization. Tailored for a diverse audience, this book caters to researchers and academics in data science, decision science, machine learning, artificial intelligence, and related domains.

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As business becomes increasingly complex and global, decision-makers must act more rapidly and accurately, based on the best available evidence. Modern data min