A Practical Guide to Data Mining for Business and Industry
Andrea Ahlemeyer-Stubbe
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Description for A Practical Guide to Data Mining for Business and Industry
Hardcover. * Presents data mining processes, methods and commonly used methods for descriptive and exploratory statistics using SAS and JMP. Num Pages: 324 pages, illustrations. BIC Classification: KJQ; UNF. Category: (P) Professional & Vocational. Dimension: 157 x 230 x 21. Weight in Grams: 538.
Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. The book is formatted to allow statisticians, computer scientists, and economists to cross-reference from a particular application or method to sectors of interest.
Product Details
Publisher
John Wiley & Sons Inc United States
Number of pages
312
Format
Hardback
Publication date
2014
Condition
New
Weight
538g
Number of Pages
328
Place of Publication
New York, United States
ISBN
9781119977131
SKU
V9781119977131
Shipping Time
Usually ships in 7 to 11 working days
Ref
99-50
About Andrea Ahlemeyer-Stubbe
Andrea Ahlemeyer-Stubbe, Director Strategic Analytics, DRAFTFCB München GmbH, Germany Shirley Coleman, Principal Statistician, Industrial Statistics Research Unit, School of Maths and Statistics, Newcastle University, UK
Reviews for A Practical Guide to Data Mining for Business and Industry
“A Practical Guide to Data Mining for Business and Industrygives practical tools on how information can be extracted from masses of data. The book is very well written, in a conversational tone that makes it enjoyable to read. The authors are excellent communicators. If you are interested in learning about data mining, learning to do a particular task in data ... Read more