Stock photo: cover may vary
Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel® with XLMiner® Hardback - 2010
by Shmueli, Galit/ Patel, Nitin R./ Bruce, Peter C
- New
- Hardback
A$58.85
A$29.24
Delivery to USA
Standard delivery: 7 to 14 days
More delivery options
Standard delivery: 7 to 14 days
Ships from Revaluation Books (Devon, United Kingdom)
Details
- Title Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel® with XLMiner®
- Author Shmueli, Galit/ Patel, Nitin R./ Bruce, Peter C
- Binding Hardback
- Edition [ Edition: secon
- Condition New
- Pages 404
- Volumes 1
- Language ENG
- Publisher John Wiley & Sons Inc, Mississauga, ON, Canada
- Publication date 2010
- Illustrated Yes
- Bookseller's Inventory # 1-0470526823
- ISBN 9780470526828 / 0470526823
- Weight 1.98 lbs (0.90 kg)
- Dimensions 10.1 x 7.2 x 1.1 in (25.65 x 18.29 x 2.79 cm)
- Category Mathematics
- Library of Congress subjects Data mining, Business - Data processing
- Library of Congress Catalogue Number 2010005152
- Dewey Decimal Code 005.54
- Quantity available 1
About Revaluation Books Devon, United Kingdom
Biblio member since 2020
General bookseller of both fiction and non-fiction.
Reader reviews for Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel® with XLMiner®
Write a review for this book
Important Terms and Guidelines
- Please focus on the book’s content and context. Also, add any personal comments as to how you enjoyed the book. Substantiate your likes and dislikes. You may make comparisons to other books.
- Reviews must be at least 140 characters in length.
- Please do not reveal critical plot elements.
- This is not a help line. Contact customer support if you need help.
Your review must not include:
- Obscenities, discriminatory language, or other insulting language not suitable for public domain
- Advertisements, “spam” content, or references to other products, offers or websites.
- Email addresses, URLs, phone numbers, physical addresses or other contact information.
- Overly critical comments about other reviews or reviewers
- Time-sensitive material (i.e. promotional tours, seminars, lectures, etc.)
- Availability, price, or alternative ordering/shipping information
