Research Practitioner's Handbook on Big Data Analytics
eBook - ePub

Research Practitioner's Handbook on Big Data Analytics

  1. 292 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Research Practitioner's Handbook on Big Data Analytics

About this book

This new volume addresses the growing interest in and use of big data analytics in many industries and in many research fields around the globe; it is a comprehensive resource on the core concepts of big data analytics and the tools, techniques, and methodologies. The book gives the why and the how of big data analytics in an organized and straightforward manner, using both theoretical and practical approaches.

The book's authors have organized the contents in a systematic manner, starting with an introduction and overview of big data analytics and then delving into pre-processing methods, feature selection methods and algorithms, big data streams, and big data classification. Such terms and methods as swarm intelligence, data mining, the bat algorithm and genetic algorithms, big data streams, and many more are discussed. The authors explain how deep learning and machine learning along with other methods and tools are applied in big data analytics. The last section of the book presents a selection of illustrative case studies that show examples of the use of data analytics in industries such as health care, business, education, and social media.

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Yes, you can access Research Practitioner's Handbook on Big Data Analytics by S. Sasikala,D. Renuka Devi, Raghvendra Kumar in PDF and/or ePUB format, as well as other popular books in Computer Science & Data Mining. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Cover Page
  2. Half Title Page
  3. Title Page
  4. Copyright Page
  5. About the Authors
  6. About the Editor
  7. Contents
  8. Abbreviations
  9. Preface
  10. Introduction
  11. 1. Introduction to Big Data Analytics
  12. 2. Preprocessing Methods
  13. 3. Feature Selection Methods and Algorithms
  14. 4. Big Data Streams
  15. 5. Big Data Classification
  16. 6. Case Study
  17. Index