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10 Αυγ 2011 · Covers all the hot topics such as data warehousing, data mining and its applications, machine learning, classification, supply optimization models, decision support systems, and analytical methods for performance evaluation.
data mining for business analytics concepts, techniques and applications in python galit shmueli | peter c. bruce peter gedeck | nitin r. patel
Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® is an excellent textbook for advanced undergraduate and graduate-level courses on data mining, predictive analytics, and business analytics.
Chapter 5 introduces data mining, discussing the phases of a data mining process and their objectives. Chapter 6 describes the activities of data preparation for business intelligence and data mining; these include data validation, anomaly detection, data transformation and reduction.
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.
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.
