Paperback. In today's data-driven world, the convergence of Artificial Intelligence (AI), Data Science, and Big Data has become a pivotal force driving innovation, efficiency, and competitive advantage across industries. The exponential growth of data, fueled by advancements in technology and the proliferation of connected devices, presents both unprecedented opportunities and complex challenges. This book, "Artificial Intelligence in Data Science and Big Data: Techniques, Tools, and Applications," aims to bridge the gap between theory and practice, providing a comprehensive guide to harnessing the power of AI within the realms of data science and big data analytics.Motivation and PurposeThe inspiration for this book stems from the recognition that while data is abundant, the ability to extract meaningful insights and actionable intelligence is not uniformly distributed. Many organizations and professionals grapple with understanding how to effectively integrate AI technologies into their data science workflows to address big data challenges. This book seeks to demystify AI concepts, techniques, and tools, offering practical guidance to leverage them in processing, analyzing, and interpreting large-scale data.The intersection of AI, data science, and big data is at the forefront of the digital transformation era. Organizations that effectively harness these technologies are poised to make significant strides in innovation, operational efficiency, and customer engagement. However, the rapid evolution of these fields can be daunting. This book addresses this challenge by providing: Comprehensive Coverage: Combining foundational knowledge with advanced topics to cater to both novices and experienced professionals.Practical Insights: Offering actionable guidance, code examples, and real-world applications to facilitate immediate implementation.Ethical Framework: Emphasizing the importance of responsible AI, data privacy, and ethical considerations in developing and deploying AI solutions.Future Orientation: Preparing readers for emerging trends and technologies that will shape the future landscape of AI and big data. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

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Detalles bibliogr�ficos

T�tulo: Artificial Intelligence in Data Science and Big Data (Paperback)
Editor: Eldonusa
Fecha de publicaci�n: 2024
Idioma: Ingl�s
ISBN 13: 9798330453351
Encuadernaci�n: Paperback
Estado: new

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Sinopsis

In today's data-driven world, the convergence of Artificial Intelligence (AI), Data Science, and Big Data has become a pivotal force driving innovation, efficiency, and competitive advantage across industries. The exponential growth of data, fueled by advancements in technology and the proliferation of connected devices, presents both unprecedented opportunities and complex challenges. This book, "Artificial Intelligence in Data Science and Big Data: Techniques, Tools, and Applications," aims to bridge the gap between theory and practice, providing a comprehensive guide to harnessing the power of AI within the realms of data science and big data analytics.

Motivation and Purpose

The inspiration for this book stems from the recognition that while data is abundant, the ability to extract meaningful insights and actionable intelligence is not uniformly distributed. Many organizations and professionals grapple with understanding how to effectively integrate AI technologies into their data science workflows to address big data challenges. This book seeks to demystify AI concepts, techniques, and tools, offering practical guidance to leverage them in processing, analyzing, and interpreting large-scale data.

The intersection of AI, data science, and big data is at the forefront of the digital transformation era. Organizations that effectively harness these technologies are poised to make significant strides in innovation, operational efficiency, and customer engagement. However, the rapid evolution of these fields can be daunting. This book addresses this challenge by providing:

Comprehensive Coverage: Combining foundational knowledge with advanced topics to cater to both novices and experienced professionals.

Practical Insights: Offering actionable guidance, code examples, and real-world applications to facilitate immediate implementation.

Ethical Framework: Emphasizing the importance of responsible AI, data privacy, and ethical considerations in developing and deploying AI solutions.

Future Orientation: Preparing readers for emerging trends and technologies that will shape the future landscape of AI and big data.

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