Data Analytics and Machine Learning : Navigating the Big Data Landscape / edited by Pushpa Singh, Asha Rani Mishra, Payal Garg.

Format:

Book

Author/Creator:

Singh, Pushpa.

Contributor:

Mishra, Asha Rani.

Garg, Payal.

Series:

Studies in Big Data, 2197-6511 ; 145

Language:

English

Subjects (All):

Quantitative research.

Machine learning.

Natural language processing (Computer science).

Data Analysis and Big Data.

Machine Learning.

Natural Language Processing (NLP).

Local Subjects:

Data Analysis and Big Data.

Machine Learning.

Natural Language Processing (NLP).

Physical Description:

1 online resource (357 pages)

Edition:

1st ed. 2024.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2024.

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.

Contents:

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.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9789819704484

9819704480

OCLC:

1427666886