Principles of Data Science

lt;p>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 emphasis on vehicle network security, Software Engineering, and Computer Science and Software Engineering Education. Two of his publications received the Best Paper Award from two international conferences. He has been a member of the International Conference on Data Mining (DMIN) since 2004, and a member of the Program Committee for the 2018 International Conference on Data Science (ICDATA'18). He participated in a number of Data Science workshops. Kevin is a Senior Member of the Association for Computing Machinery (ACM), a Senior Member of the Institute of Electrical and Electronic Engineers (IEEE), and a Fellow of the British Computer Society (BCS). He served as a Program Committee member for many international conferences and chaired some of them. In 2103, he received the Faculty Excellence Award from the University of Detroit Mercy.

Robert Stahlbock is a lecturer and researcher at the Institute of Information Systems, University of Hamburg. He is also lecturer at the FOM University of Applied Sciences since 2003. He holds a diploma in Business Administration and a PhD from the UHH. His research interests are focused on managerial decision support and issues related to Maritime Logistics and other industries as well as Operations Research, Information Systems, Business Intelligence and Data Science. He is author of research studies published in international prestigious journals, conference proceedings and book chapters. He serves as guest editor of data science related books, as reviewer for international leading journals as well as a member of conference program committees. He is General Chair of the annual International Conference on Data Science since 2006. He also consults companies in various sectors and projects.

Kai Brüssau is a lecturer and researcher at the Institute of Information Systems, University of Hamburg. He holds a diploma in Business Mathematics and a PhD from the UHH. In his research as well as in his courses he cooperates with Bachelor and Master students in several projects belonging to the fields of Operations Research, Data Science, and Business Analytics. There

Introduction.- Data Acquisition, Extraction, and Cleaning.- Data Summarization and Modeling.- Data Analysis and Communication Techniques.- Data Science Tools.- Deep Learning in Data Science.- Data Science Applications.- Conclusion.