Predictive Analytics Using Oracle Data Miner - Tierney, Brendan
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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,…mehr

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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
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Autorenporträt
Brendan Tierney, Oracle ACE Director, is an independent consultant and lectures on Data Mining and Advanced Databases in the Dublin Institute of Technology in Ireland. He is an Oracle ACE Director and has extensive experience working in the areas of Data Mining, Data Warehousing, Data Architecture and Database Design. Brendan has worked on projects in Ireland, UK, Belgium and USA and has been working with the Oracle database and tools since 1992. Brendan is the editor of the UKOUG Oracle Scene magazine and deputy chair of the OUG Ireland BI SIG. Brendan is a regular speaker at conferences across Europe and the USA, including Oracle Open World. In addition to his presentations Brendan has written technical articles for OTN, Oracle Scene, IOUG SELECT Journal and ODTUG Technical Journal. He is author of the Oracle Press book titled, Predictive Analytics using Oracle Data Miner.