Traditional business analytics have so far focused mostly on descriptive analyses of historical data using a myriad of sound statistical techniques. This book describes how numerical statistical techniques can be augmented and enriched with techniques from symbolic artificial intelligence (AI), machine learning (ML)/data mining, and control theory for enhanced descriptive, predictive, and prescriptive analytics. The book is unique in its coverage of both traditional probabilistic/statistical and cutting-edge AI/ML-based approaches to descriptive and predictive analytics and associated decision support. It provides analytics practitioners with problem modeling guidance and appropriate modeling techniques and algorithms suitable for solving practical problems. The book offers a detailed account of various types of uncertainties and techniques for handling them. Special emphasis is given to modeling problems that are time-dependent. The book also covers text analytics with useful applications, such as information structuring and sentiment analysis.