Description
Taylor & Francis Ltd Computational Business Analytics by Subrata Das
Learn How to Properly Use the Latest Analytics Approaches in Your Organization_x000D__x000D_Computational Business Analytics presents tools and techniques for descriptive, predictive, and prescriptive analytics applicable across multiple domains. Through many examples and challenging case studies from a variety of fields, practitioners easily see the connections to their own problems and can then formulate their own solution strategies._x000D__x000D_The book first covers core descriptive and inferential statistics for analytics. The author then enhances numerical statistical techniques with symbolic artificial intelligence (AI) and machine learning (ML) techniques for richer predictive and prescriptive analytics. With a special emphasis on methods that handle time and textual data, the text:_x000D__x000D__x000D__x000D__x000D__x000D__x000D_Enriches principal component and factor analyses with subspace methods, such as latent semantic analyses _x000D_Combines regression analyses with probabilistic graphical modeling, such as Bayesian networks _x000D_Extends autoregression and survival analysis techniques with the Kalman filter, hidden Markov models, and dynamic Bayesian networks _x000D_Embeds decision trees within influence diagrams_x000D_Augments nearest-neighbor and k-means clustering techniques with support vector machines and neural networks _x000D__x000D_These approaches are not replacements of traditional statistics-based analytics; rather, in most cases, a generalized technique can be reduced to the underlying traditional base technique under very restrictive conditions. The book shows how these enriched techniques offer efficient solutions in areas, including customer segmentation, churn prediction, credit risk assessment, fraud detection, and advertising campaigns._x000D_show more