Computational Business Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Das, Subrata

ISBN 10: 1439890706 / ISBN 13: 9781439890707

Published by Chapman and Hall/CRC, 2013

About this Item

The book is in good condition with all pages and cover intact, including the dust jacket if originally issued. The spine may show light wear. Pages may contain some notes or highlighting, and there might be a "From the library of" label. Boxed set packaging, shrink wrap, or included media like CDs may be missing.

Seller Inventory # BSM.15XDG

Bibliographic Details

• Title: Computational Business Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
• Publisher: Chapman and Hall/CRC
• Publication Date: 2013
• Language: English
• ISBN 10: 1439890706
• ISBN 13: 9781439890707
• Binding: Hardcover
• Condition: good

About this title

Synopsis

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.

About the Author

Subrata Das is the founder and president of Machine Analytics and also serves as a consulting scientist to other companies. He has many years of experience in industrial, government, and academic research and development. He earned his Ph.D. in computer science and master's in mathematics.