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data mining for business analytics concepts, techniques and applications in python galit shmueli | peter c. bruce peter gedeck | nitin r. patel
18 Απρ 2016 · Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner. This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and ...
cated business analytics programs. A general “Business Analytics,” “Predictive Analytics,” or “Data Mining” course, common in MBA and undergraduate programs as a one-semester elec-tive, would cover Parts I–III, and choose a subset of methods from Parts IV and V. Instructors can choose to use cases as team assignmen.
Data are typically stored in a database environment and are large in scale. Data mining is also referred to as knowledge discovery in database (KDD). Business intelligence is the transformation of raw data into knowledge and insight for making better business decisions.
4 Ιουν 2020 · Business intelligence includes tools and techniques for data gathering, analysis, and visualization for helping with executive decision making in any industry. Data mining includes statistical and machine-learning techniques to build decision-making models from raw data.
This book provides an introduction to key concepts in data mining and business intelligence. It covers topics such as data exploration, preprocessing, classification, clustering, outlier analysis, frequent pattern mining, and applications of data mining and business intelligence.
Download book PDF. Overview. Authors: Wilfried Grossmann, Stefanie Rinderle-Ma. Comprehensive and systematic introduction to BI with special emphasis on process data and business process management. Illustrates all concepts through selected examples and by using open source tools like R and ProM.
