04572nam a22005775i 4500 978-981-97-0448-4 DE-He213 20251030111730.0 cr nn 008mamaa 240319s2024 si | s |||| 0|eng d 9789819704484 978-981-97-0448-4 10.1007/978-981-97-0448-4 doi QA76.9.Q36 UN bicssc COM021000 bisacsh UN thema Data Analytics and Machine Learning [electronic resource] : Navigating the Big Data Landscape / edited by Pushpa Singh, Asha Rani Mishra, Payal Garg. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XIII, 353 p. 157 illus., 125 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Studies in Big Data, 2197-6511 ; 145 Chapter 1. Introduction to Data Analytics, Big Data, and Machine Learning -- Chapter 2. Fundamentals of Data Analytics and Lifecycle -- Chapter 3. Building Predictive Models with Machine Learning -- Chapter 4. Stream data model and architecture -- Chapter 5. Leveraging Big Data for Data Analytics -- Chapter 6. Advanced Techniques in Data Analytics -- Chapter 7. Scalable Machine Learning with Big Data -- Chapter 8. Big Data Analytics Framework using Machine Learning on Massive Datasets -- Chapter 9. Deep-learning Techniques in Big-Data analytics -- Chapter 10. Data Privacy and Ethics in Data Analytics -- Chapter 11. Practical Implementation of Machine Learning Techniques & data analytics using R -- Chapter 12. Real-World Applications of Data Analytics, Big Data, and Machine Learning -- Chapter 13. Implementing Data-Driven Innovation in Organizations -- Chapter 14. Business Transformation using Big Data Analytics and Machine Learning -- Chapter 15. Future Trends and Emerging Opportunities in HealthAnalytics -- Chapter 16. Future Trends in Data Analytics and Machine Learning. 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. Quantitative research. Machine learning. 55059 Natural language processing (Computer science). 65131 Data Analysis and Big Data. Machine Learning. 55059 Natural Language Processing (NLP). 66164 Singh, Pushpa. editor. edt http://id.loc.gov/vocabulary/relators/edt 73403 Mishra, Asha Rani. editor. edt http://id.loc.gov/vocabulary/relators/edt 73404 Garg, Payal. editor. edt http://id.loc.gov/vocabulary/relators/edt 73405 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819704477 Printed edition: 9789819704491 Printed edition: 9789819704507 Studies in Big Data, 2197-6511 ; 145 68724 https://doi.org/10.1007/978-981-97-0448-4 ZDB-2-SMA ZDB-2-SXMS 58467 58467