Springer Principles of Data Science (Transactions on Computational Science and Computational Intelligence)

This book provides readers with a thorough understanding of various research areas within the field of data science. The book introduces readers to various techniques for data acquisition, extraction, and cleaning, data summarizing and modeling, data analysis and communication techniques, data science tools, deep learning, and various data science applications. Researchers can extract and conclude various future ideas and topics that could result in potential publications or thesis. Furthermore, this book contributes to Data Scientists’ preparation and to enhancing their knowledge of the field. The book provides a rich collection of manuscripts in highly regarded data science topics, edited by professors with long experience in the field of data science. Introduces various techniques, methods, and algorithms adopted by Data Science experts Provides a detailed explanation of data science perceptions, reinforced by practical examples Presents a road map of future trends suitable for innovative data science research and practice From the Back Cover This book provides readers with a thorough understanding of various research areas within the field of data science. The book introduces readers to various techniques for data acquisition, extraction, and cleaning, data summarizing and modeling, data analysis and communication techniques, data science tools, deep learning, and various data science applications. Researchers can extract and conclude various future ideas and topics that could result in potential publications or thesis. Furthermore, this book contributes to Data Scientists’ preparation and to enhancing their knowledge of the field. The book provides a rich collection of manuscripts in highly regarded data science topics, edited by professors with long experience in the field of data science. Introduces various techniques, methods, and algorithms adopted by Data Science experts Provides a detailed explanation of data science perceptions, reinforced by practical examples Presents a road map of future trends suitable for innovative data science research and practice About the Author Hamid R. Arabnia received a Ph.D. degree in Computer Science from the University of Kent (England) in 1987. He is currently a Professor (Emeritus) of Computer Science at University of Georgia (Georgia, USA), where he has been since October 1987. His research interests include parallel and distributed processing techniques and algorithms, supercomputing, Data Science (in the context of scalable HPC), imaging science, and other compute intensive problems. His most recent activities include: Studying ways to promote legislation that would prevent cyber-stalking, cyber-harassment, and cyber-bullying. As a victim of cyber-harassment and cyber-bullying, in 2017 and 2018 he won a lawsuit with damages awarded for a total of $3 Million (includes $650K awarded for attorney’s costs). Since this court case was one of the few cases of its kind in the United States, this ruling is considered to be important. Prof. Arabnia is Editor-in-Chief of The Journal of Supercomputing (Springer). He is the book series editor-in-chief of "Transactions of Computational Science and Computational Intelligence" (Springer). He is the editor of Computational Science and Computational Intelligence (IEEE CPS). He is a Senior Adviser to a number of corporations and is a Fellow and Adviser of Center of Excellence in Terrorism, Resilience, Intelligence & Organized Crime Research (CENTRIC). Dr. Kevin Daimi received his Ph.D. from the University of Cranfield, England. He has a long mixture of academia and industry experience. He has worked as Senior Programmer/Systems Analyst, Computer Specialist, and Computer Consultant. He is currently Professor of Computer Science and Software Engineering Programs at the University of Detroit Mercy. His research interests include Data Science, Computer and Network Security with em