Abstract
<p><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black"><i>Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python</i> presents an applied approach to data mining concepts and methods, using Python software for illustration</span></span></span></p><p> </p><p style="margin-bottom:16px"><span style="font-size:12pt"><span style="background:white"><span style="line-height:18.0pt"><span style="font-family:Calibri,sans-serif"><span style="color:black"><span lang="EN-US" style="font-size:10.5pt"><span style="font-family:"Arial","sans-serif"">Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities.</span></span></span></span></span></span></span></p><p style="margin-bottom:16px"> </p><p style="margin-bottom:16px"><span style="font-size:12pt"><span style="background:white"><span style="line-height:18.0pt"><span style="font-family:Calibri,sans-serif"><span style="color:black"><span lang="EN-US" style="font-size:10.5pt"><span style="font-family:"Arial","sans-serif"">This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes:</span></span></span></span></span></span></span></p><p><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">- A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process</span></span></span></p><p><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">- A new section on ethical issues in data mining</span></span></span></p><p><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">- Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students</span></span></span></p><p><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">- More than a dozen case studies demonstrating applications for the data mining techniques described</span></span></span></p><p><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">- End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented</span></span></span></p><p><span style="font-size:12pt"><span style="font-family:Calibri,sans-serif"><span style="color:black">- A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions</span></span></span></p><p style="margin-bottom:16px"> </p><p><i><span lang="EN-US" style="font-size:12.0pt"><span style="font-family:"Calibri","sans-serif""><span style="color:black">Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python</span></span></span></i><span lang="EN-US" style="font-size:12.0pt"><span style="font-family:"Calibri","sans-serif""><span style="color:black"> is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.</span></span></span></p>