Research Practitioner's Handbook on Big Data Analytics

With the growing interest in and use of big data analytics in many industries and in many research fields around the globe, this new volume addresses the need for a comprehensive resource on the core concepts of big data analytics along with 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.

Research Practitioner's Handbook on Big Data Analytics will be a valuable addition to the libraries of practitioners in data collection in many industries along with research scholars and faculty in the domain of big data analytics. The book can also serve as a handy textbook for courses in data collection, data mining, and big data analytics.

隨著全球各行業和研究領域對大數據分析的興趣和使用日益增長，這本新書解決了對大數據分析核心概念以及工具、技術和方法的全面資源的需求。該書以有組織且直接的方式介紹大數據分析的原因和方法，並結合理論和實踐方法。

書中的作者以系統的方式組織內容，從大數據分析的介紹和概述開始，然後深入探討預處理方法、特徵選擇方法和算法、大數據流和大數據分類。書中討論了諸如群體智能、數據挖掘、蝙蝠算法和遺傳算法、大數據流等術語和方法。作者解釋了深度學習和機器學習以及其他方法和工具如何應用於大數據分析。

該書的最後一部分展示了一系列實例案例，展示了在醫療保健、商業、教育和社交媒體等行業中使用數據分析的例子。

《大數據分析研究實踐手冊》將成為許多行業數據收集從業人員以及大數據分析領域的研究學者和教師的寶貴資源。該書還可以作為數據收集、數據挖掘和大數據分析課程的便捷教材。

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.

S. Sasikala, 博士，是印度金奈馬德拉斯大學計算機科學系的副教授和研究導師，也是網絡運營和Edusat計劃的主任。她擁有23年的教學經驗，在大學擔任過多個職位，包括網絡學習中心的負責人、UGC學生申訴委員會的節點官員、IDE在線課程開發協調員以及IDE校友會主席。她的研究興趣包括影像、數據挖掘、機器學習、網絡、大數據和人工智能。她出版了兩本計算機科學書籍，並在領先期刊和會議論文集上發表了27篇研究文章和四篇書籍章節。她還獲得了最佳論文獎和婦女成就獎。她是國際期刊和會議的活躍評審和編輯成員。

D. Renuka Devi, 博士，是印度金奈的Stella Maris學院（自治學院）計算機科學系的助理教授。她擁有12年的教學經驗。她的研究興趣包括數據挖掘、機器學習、大數據和人工智能。她通過參加會議和專業研究積極參與持續學習。她在IEEE、Scopus和Web of Science等出版物上發表了八篇研究論文和一篇書籍章節。她還在國際會議上發表論文並獲得了最佳論文獎。

Raghvendra Kumar, 博士，是印度GIET大學計算機科學與工程系的副教授。Kumar博士擔任《萬物互聯：安全與隱私》和《生物醫學工程：技術與應用》（Apple Academic Press）書系的編輯。他在國際期刊和會議上發表了多篇研究論文。他在國際和國內會議中擔任多個職位，並在物聯網、數據挖掘、生物醫學工程、大數據、機器人技術、圖論和圖靈機等計算機科學領域撰寫和編輯了20多本書籍。