Computational business analytics
Author(s)
Bibliographic Information
Computational business analytics
（Chapman & Hall/CRC data mining and knowledge discovery series）
Chapman & Hall/CRC, 2014
- : hbk.
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Note
Includes bibliographical references(p. 463-477) and index
Description and Table of Contents
Description
Learn How to Properly Use the Latest Analytics Approaches in Your Organization
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.
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:
Enriches principal component and factor analyses with subspace methods, such as latent semantic analyses
Combines regression analyses with probabilistic graphical modeling, such as Bayesian networks
Extends autoregression and survival analysis techniques with the Kalman filter, hidden Markov models, and dynamic Bayesian networks
Embeds decision trees within influence diagrams
Augments nearest-neighbor and k-means clustering techniques with support vector machines and neural networks
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.
Table of Contents
Analytics Background and Architectures. Mathematical and Statistical Preliminaries. Statistics for Descriptive Analytics. Bayesian Probability and Inference. Inferential Statistics and Predictive Analytics. Artificial Intelligence for Symbolic Analytics. Probabilistic Graphical Modeling. Decision Support and Prescriptive Analytics. Time Series Modeling and Forecasting. Monte Carlo Simulation. Cluster Analysis and Segmentation. Machine Learning for Analytics Models. Unstructured Data and Text Analytics. Semantic Web. Analytics Tools. Appendices. Index.
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