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Data Analytics and Machine Learning Navigating the Big Data Landscape /

This book presents an in-depth analysis of successful data-driven initiatives, highlighting how organizations have leveraged data to drive decision-making processes, optimize operations, and achieve remarkable outcomes. Through case studies, readers gain valuable insights and learn practical strateg...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Singh, Pushpa (Editor), Mishra, Asha Rani (Editor), Garg, Payal (Editor)
Format: Electronic eBook
Language:English
Published: Singapore : Springer Nature Singapore : Imprint: Springer, 2024.
Edition:1st ed. 2024.
Series:Studies in Big Data, 145
Subjects:
Quantitative research.
Machine learning.
Natural language processing (Computer science).
Data Analysis and Big Data.
Machine Learning.
Natural Language Processing (NLP).
Online Access:https://doi.org/10.1007/978-981-97-0448-4
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Summary:This book presents an in-depth analysis of successful data-driven initiatives, highlighting how organizations have leveraged data to drive decision-making processes, optimize operations, and achieve remarkable outcomes. Through case studies, readers gain valuable insights and learn practical strategies for implementing data analytics, big data, and machine learning solutions in their own organizations. The book discusses the transformative power of data analytics and big data in various industries and sectors and how machine learning applications have revolutionized exploration by enabling advanced data analysis techniques for mapping, geospatial analysis, and environmental monitoring, enhancing our understanding of the world and its dynamic processes. This book explores how big data explosion, the power of analytics and machine learning revolution can bring new prospects and opportunities in the dynamic and data-rich landscape. It highlights the future research directions in data analytics, big data, and machine learning that explores the emerging trends, challenges, and opportunities in these fields by covering interdisciplinary approaches such as handling and analyzing real-time and streaming data.
Physical Description:1 online resource (XIII, 353 p. 157 illus.). 125 illus. in color.
ISBN:9789819704484
ISSN:2197-6511 ;
DOI:10.1007/978-981-97-0448-4

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