Research Practitioner's Handbook on Big Data Analytics
1st Edition
Published on 4. May 2023
310 pages
978-1-000-57836-2 (ISBN)
Description
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.
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.
More details
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Illustrations
3 Tables, black and white; 10 Illustrations, color; 136 Illustrations, black and white
ISBN-13
978-1-000-57836-2 (9781000578362)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
S. Sasikala | D. Renuka Devi | Raghvendra Kumar
Research Practitioner's Handbook on Big Data Analytics
Book
05/2023
1st Edition
Apple Academic Press Inc.
€184.21
Shipment within 3-4 weeks
Persons
S. Sasikala, PhD, is Associate Professor and Research Supervisor in the Department of Computer Science, IDE, and Director of Network Operation and Edusat Programs at the University of Madras, Chennai, India. With 23 years of teaching experience, she has held various posts at the university, including Head-in-charge of the Centre for Web-based Learning, Nodal Officer for the UGC Student Redressal Committee, Coordinator for Online Course Development at IDE, and President of the Alumni Association at IDE. Her research interests include imaging, data mining, machine learning, networks, big data, and AI. She has published two books on computer science and published over 27 research articles in leading journals and conference proceedings as well as four book chapters. She has also received best paper awards and women's achievement awards. She is an active reviewer and editorial member for international journals and conferences.
D. Renuka Devi, PhD, is Assistant Professor in the Department of Computer Science, Stella Maris College (Autonomous), Chennai, India. She has 12 years of teaching experience. Her research interests include data mining, machine learning, big data, and AI. She actively participates in continued learning through conferences and professional research. She has published eight research papers and a book chapter in publications from IEEE, Scopus, and Web of Science. She has also presented papers at international conferences and received best paper awards.
Raghvendra Kumar, PhD, is Associate Professor in the Computer Science and Engineering Department at GIET University, India. Dr. Kumar serves as Editor of the book series Internet of Everything: Security and Privacy and the book series Biomedical Engineering: Techniques and Applications (Apple Academic Press). He has published several research papers in international journals and conferences. He has served in many roles for international and national conferences and has authored and edited over 20 computer science books in the field of Internet of Things, data mining, biomedical engineering, big data, robotics, graph theory, and Turing machines.
D. Renuka Devi, PhD, is Assistant Professor in the Department of Computer Science, Stella Maris College (Autonomous), Chennai, India. She has 12 years of teaching experience. Her research interests include data mining, machine learning, big data, and AI. She actively participates in continued learning through conferences and professional research. She has published eight research papers and a book chapter in publications from IEEE, Scopus, and Web of Science. She has also presented papers at international conferences and received best paper awards.
Raghvendra Kumar, PhD, is Associate Professor in the Computer Science and Engineering Department at GIET University, India. Dr. Kumar serves as Editor of the book series Internet of Everything: Security and Privacy and the book series Biomedical Engineering: Techniques and Applications (Apple Academic Press). He has published several research papers in international journals and conferences. He has served in many roles for international and national conferences and has authored and edited over 20 computer science books in the field of Internet of Things, data mining, biomedical engineering, big data, robotics, graph theory, and Turing machines.
Content
1. Introduction to Big Data Analytics 2. Pre-Processing Methods 3. Feature Selection Methods and Algorithms 4. Big Data Streams 5. Big Data Classification 6. Case Studies
