Computational Business Analytics
1st Edition
Published on 14. December 2013
516 pages
978-1-4398-9073-8 (ISBN)
Description
This book presents tools and techniques for descriptive, predictive, and prescriptive analytics applicable across multiple domains. The author first covers core descriptive and inferential statistics for analytics and then enhances numerical statistical techniques with symbolic artificial intelligence and machine learning techniques for richer predictive and prescriptive analytics. 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.
More details
Series
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Illustrations
290 s/w Abbildungen, 67 s/w Tabellen
290 b/w images and 67 tables
File size
100,62 MB
ISBN-13
978-1-4398-9073-8 (9781439890738)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Subrata Das
Computational Business Analytics
Book
12/2013
1st Edition
Chapman & Hall/CRC
€197.50
Shipment within 15-20 days
Person
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.
Content
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.
