This book presents a comprehensive and up-to-date treatise of a range of methodological and algorithmic issues. It also discusses
implementations and case studies, identifies the best design practices, and assesses data analytics business models and practices
in industry, health care, administration and business.Data science and big data go hand in hand and constitute a rapidly growing
area of research and have attracted the attention of industry and business alike. The area itself has opened up promising
new directions of fundamental and applied research and has led to interesting applications, especially those addressing the
immediate need to deal with large repositories of data and building tangible, user-centric models of relationships in data.
Data is the lifeblood of today's knowledge-driven economy.Numerous data science models are oriented towards end users and
along with the regular requirements for accuracy (which are present in any modeling), come the requirements for ability to
process huge and varying data sets as well as robustness, interpretability, and simplicity (transparency). Computational intelligence
with its underlying methodologies and tools helps address data analytics needs.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.