This book presents a comprehensive and up-to-date treatise of the area covering a spectrum of methodological and algorithmic issues.It also discusses implementations and case studies, identifies the best design practices, and assesses business models and practices of Data Analytics in industry, health care, administration, and business.
Data Science and Big Data, coming hand in hand, constitute one of the rapidly growing areas of research and have attracted attention of industry and business. The area itself has opened up new promising directions of fundamental and applied research as well as led to interesting applications, especially those being drawn by the immediate needs to deal with large repositories of data and building some tangible, user- centric models of relationships in data. Data are lifeblood of today's knowledge-driven economy.
Numerous models of Data Science are oriented towards end-users and along with the regular requirements of accuracy (which are present in any modeling), come the requirements of abilities to process huge and varying data sets and robustness, interpretability, and simplicity (transparency). Computational Intelligence with its underlying methodologies and tools helps address the needs of Data Analytics.
The book is of interest to those researchers and practitioners involved in Data Science, Internet engineering, Computational Intelligence, management, operations research, and knowledge-based systems.
