Predictive Analytics Using Oracle Data Miner: Develop & Use Data Mining Models in Oracle Data Miner, SQL & PL/SQL
Language: English
Published by McGraw Hill, 2014
- Softcover
- Used

Condition: Used - Good
US$ 82.44
Quantity: 1 available
Add to basketItem description from seller
Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.
Seller Inventory # S_411156455
- Title
- Predictive Analytics Using Oracle Data Miner: Develop & Use Data Mining Models in Oracle Data Miner, SQL & PL/SQL
- Author
- Tierney, Brendan
- Publisher
- McGraw Hill
- Publication year
- 2014
- Condition
- Good
- Binding
- paperback
- Language
- English
- ISBN 10
- 0071821678
- ISBN 13
- 9780071821674
Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.
Build Next-Generation In-Database Predictive Analytics Applications with Oracle Data Miner
“If you have an Oracle Database and want to leverage that data to discover new insights, make predictions, and generate actionable insights, this book is a must read for you! In Predictive Analytics Using Oracle Data Miner: Develop & Use Oracle Data Mining Models in Oracle Data Miner, SQL & PL/SQL, Brendan Tierney, Oracle ACE Director and data mining expert, guides you through the basic concepts of data mining and offers step-by-step instructions for solving data-driven problems using SQL Developer’s Oracle Data Mining extension. Brendan takes it full circle by showing you how to deploy advanced analytical methodologies and predictive models immediately into enterprise-wide production environments using the in-database SQL and PL/SQL functionality. Definitely a must read for any Oracle data professional!” --Charlie Berger, Senior Director Product Management, Oracle Data Mining and Advanced Analytics
Perform in-database data mining to unlock hidden insights in data. Written by an Oracle ACE Director, Predictive Analytics Using Oracle Data Miner shows you how to use this powerful tool to create and deploy advanced data mining models. Covering topics for the data scientist, Oracle developer, and Oracle database administrator, this Oracle Press guide shows you how to get started with Oracle Data Miner and build Oracle Data Miner models using SQL and PL/SQL packages. You'll get best practices for integrating your Oracle Data Miner models into applications to automate the discovery and distribution of business intelligence predictions throughout the enterprise.
- Install and configure Oracle Data Miner for Oracle Database 11g Release 11.2 and Oracle Database 12c
- Create Oracle Data Miner projects and workflows
- Prepare data for data mining
- Develop data mining models using association rule analysis, classification, clustering, regression, and anomaly detection
- Use data dictionary views and prepare your data using in-database transformations
- Build and use data mining models using SQL and PL/SQL packages
- Migrate your Oracle Data Miner models, integrate them into dashboards and applications, and run them in parallel
- Build transient data mining models with the Predictive Queries feature in Oracle Database 12c
"Synopsis" may belong to another edition of this title.
About the Author
Brendan Tierney, Oracle ACE Director (Dublin, Ireland), an independent consultant, lectures on data mining and advanced databases at the Dublin Institute of Technology.
"About the title" may belong to another edition of this title.
Shipping rates within U.S.A.
| Item | 4 to 14 business days | 2 to 6 business days |
|---|---|---|
| First item | US$ 3.75 | US$ 6.99 |
Payment methods
Store description
Half Price Books has been connecting readers to great books since 1972. Customer service is our top priority.
Specialty
AllSeller's business information
Half Price Books, Records, Magazines, Inc.
5803 E. Northwest Hwy.
Dallas, TX U.S.A. 75231