Description
Computational Business Analytics
Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
Author: Das Subrata
Language: English
Subjects for Computational Business Analytics:
Keywords
SAS Enterprise Guide; BPA; descriptive and inferential statistics for analytics; MONTE CARLO; descriptive; predictive; and prescriptive analytics; Probabilistic LSA; numerical statistical techniques with symbolic artificial intelligence and machine learning; Data Set; methods that handle time and textual data; LDA; latent semantic analyses; RDF Triple; computational technologies for information extraction and text classification; Dempster's Rule; Bayesian networks; Dempster Shafer Theory; hidden Markov models; Junction Tree; clustering techniques with support vector machines and neural networks; Cpt; autoregression and survival analysis; Focal Element; regression analyses with probabilistic graphical modeling; Junction Tree Algorithm; principal component and factor analyses with subspace methods; BN; JDL Model; Linear Regression; Join Tree; EU; Owl; JDL Data Fusion Model; Multiple Linear Regression; Chance Node; Risk Assessment; Course Of Action; COAs
Support: Print on demand
Description
/li>Contents
/li>Readership
/li>Biography
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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.
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.
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.
