
Computational Business Analytics by Subrata Das
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:
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- 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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Data Mining with R
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Biological Data Mining
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Exploratory Data Analysis Using R
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Advanced Data Science and Analytics with Python
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Automated Data Analysis Using Excel
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Privacy-Aware Knowledge Discovery
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Computational Intelligent Data Analysis for Sustainable Development
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Data Mining for Design and Marketing
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Data Classification
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Statistical Data Mining Using SAS Applications
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Mining Software Specifications
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Contrast Data Mining
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Introduction to Computational Health Informatics
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Social Networks with Rich Edge Semantics
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Graph-Based Social Media Analysis
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Industrial Applications of Machine Learning
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Event Mining
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Advances in Machine Learning and Data Mining for Astronomy
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Human Capital Systems, Analytics, and Data Mining
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Practical Graph Mining with R
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Large-Scale Machine Learning in the Earth Sciences
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Support Vector Machines
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Geographic Data Mining and Knowledge Discovery
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Demystifying AI
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Data Science and Machine Learning for Non-Programmers
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Knowledge Discovery from Data Streams
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Data Science and Analytics with Python
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RapidMiner, Second Edition
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Feature Engineering for Machine Learning and Data Analytics
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Text Mining and Visualization
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Knowledge Guided Machine Learning
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Healthcare Data Analytics
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Text Mining
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.
| SKU | Unavailable |
| ISBN 13 | 9781439890707 |
| ISBN 10 | 1439890706 |
| Title | Computational Business Analytics |
| Author | Subrata Das |
| Series | Chapman And Hall Crc Data Mining And Knowledge Discovery Series |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Taylor & Francis Inc |
| Year published | 2013-12-14 |
| Number of pages | 516 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |
































