This edited volume is a Data Science text, where multiple aspects of Statistics, Machine Learning, Artificial Intelligence, Big Data methodology, High-dimensional techniques and algorithms, and applications and case studies are presented together. Owing to its very broad scope, the chapters of this book will be collected under thematically coherent groups. The planned groups of chapters are on (i) Regularization and high-dimensional machine learning, (ii) Bayesian high-dimensional modeling and computation, (iii) Spatio-temporal and dependent data models, and (iv) Deep learning and artificial intelligence. Case studies and applications, as well as high-dimensional probability theory may be two other groups of chapters, if a number of authors write with primary focus on these topics. This book will be useful for graduate students who want to specialize eventually on some aspect of Data Science, to beginners as well as advanced researchers in the field of Data Science, and mayas well serve as an encyclopedic text on Data Science.
