Computational Business Analytics

Subrata Das

December 14, 2013 by Chapman and Hall/CRC
Reference - 516 Pages - 290 B/W Illustrations
ISBN 9781439890707 - CAT# K14110
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series


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  • Incorporates descriptive, predictive, and prescriptive analytics processes
  • Presents case studies that illustrate statistics-, AI-, and ML-based analytics systems in numerous domains, including loan processing, credit risk assessment, life status estimation, fraud detection, and customer sentiment analysis
  • Provides a refresher on the basics of analytics, along with the necessary mathematical and statistical preliminaries
  • Covers computational technologies for information extraction and text classification in text analyses
  • Contains an abundance of references to the theory of statistics and probability, AI, ML, control theory, and other relevant areas


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.


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