Computational Business Analytics

Name: Computational Business Analytics
Brand: Taylor & Francis
SKU: 9781439890707
Price: 14551.0 INR
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by Subrata Das

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ISBN13: 9781439890707
Binding: Hardcover
Subject: N/A
Publisher: Taylor & Francis
Publisher Imprint: CRC Press
Publication Date: December 24, 2013
Pages: 516
Original Price: GBP 171.99
Language: English
Edition: N/A
Item Weight: 522 grams
BISAC Subject(s): Data Science / Data Analytics, Leadership, and Management Science

About The Book

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.

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