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Big Data in Finance: Transforming the Financial Landscape: Volume 1 2024 ed. [Hardback]

  • Formāts: Hardback, 756 pages, height x width: 235x155 mm, 105 Illustrations, color; 26 Illustrations, black and white; XIII, 756 p. 131 illus., 105 illus. in color., 1 Hardback
  • Sērija : Studies in Big Data 164
  • Izdošanas datums: 28-Mar-2025
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031750942
  • ISBN-13: 9783031750946
Citas grāmatas par šo tēmu:
  • Hardback
  • Cena: 180,78 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Standarta cena: 212,69 €
  • Ietaupiet 15%
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  • Formāts: Hardback, 756 pages, height x width: 235x155 mm, 105 Illustrations, color; 26 Illustrations, black and white; XIII, 756 p. 131 illus., 105 illus. in color., 1 Hardback
  • Sērija : Studies in Big Data 164
  • Izdošanas datums: 28-Mar-2025
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3031750942
  • ISBN-13: 9783031750946
Citas grāmatas par šo tēmu:

This book offers a comprehensive exploration of how Big Data analytics is reshaping the financial world, providing crucial insights for industry professionals, scholars, and enthusiasts alike. This book delves into the expansive potential of Big Data in revolutionizing financial decision-making, risk management, and operational efficiency. It explores how advanced analytics, machine learning, and artificial intelligence are disrupting traditional financial models, empowering institutions with unparalleled insights and a competitive edge. While highlighting technological advancements, the book also addresses the challenges and ethical considerations inherent in data-driven finance. With contributions from leading experts and thought leaders, this book serves as an indispensable resource for anyone eager to understand and harness the transformative power of Big Data in finance. Embark on a journey through the dynamic convergence of finance and technology, and discover how Big Data is shaping the future of the financial landscape, one data point at a time.

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