Data Science Using Oracle Data Miner and Oracle R Enterprise (eBook)
XXII, 289 Seiten
Apress (Verlag)
978-1-4842-2614-8 (ISBN)
Automate the predictive analytics process using Oracle Data Miner and Oracle R Enterprise. This book talks about how both these technologies can provide a framework for in-database predictive analytics. You'll see a unified architecture and embedded workflow to automate various analytics steps such as data preprocessing, model creation, and storing final model output to tables.
You'll take a deep dive into various statistical models commonly used in businesses and how they can be automated for predictive analytics using various SQL, PLSQL, ORE, ODM, and native R packages. You'll get to know various options available in the ODM workflow for driving automation. Also, you'll get an understanding of various ways to integrate ODM packages, ORE, and native R packages using PLSQL for automating the processes.Data Science Automation Using Oracle Data Miner and Oracle R Enterprise starts with an introduction to business analytics, covering why automation is necessary and the level of complexity in automation at each analytic stage. Then, it focuses on how predictive analytics can be automated by using Oracle Data Miner and Oracle R Enterprise. Also, it explains when and why ODM and ORE are to be used together for automation.
The subsequent chapters detail various statistical processes used for predictive analytics such as calculating attribute importance, clustering methods, regression analysis, classification techniques, ensemble models, and neural networks. In these chapters you will also get to understand the automation processes for each of these statistical processes using ODM and ORE along with their application in a real-life business use case.What you'll learn
- Discover the functionality of Oracle Data Miner and Oracle R Enterprise
- Gain methods to perform in-database predictive analytics
- Use Oracle's SQL and PLSQL APIs for building analytical solutions
- Acquire knowledge of common and widely-used business statistical analysis techniques
Who this book is for
- IT executives, BI architects, Oracle architects and developers, R users and statisticians.
Automate the predictive analytics process using Oracle Data Miner and Oracle R Enterprise. This book talks about how both these technologies can provide a framework for in-database predictive analytics. You'll see a unified architecture and embedded workflow to automate various analytics steps such as data preprocessing, model creation, and storing final model output to tables.You'll take a deep dive into various statistical models commonly used in businesses and how they can be automated for predictive analytics using various SQL, PLSQL, ORE, ODM, and native R packages. You'll get to know various options available in the ODM workflow for driving automation. Also, you'll get an understanding of various ways to integrate ODM packages, ORE, and native R packages using PLSQL for automating the processes.Data Science Automation Using Oracle Data Miner and Oracle R Enterprise starts with an introduction to business analytics, covering why automation is necessary and the level of complexity in automation at each analytic stage. Then, it focuses on how predictive analytics can be automated by using Oracle Data Miner and Oracle R Enterprise. Also, it explains when and why ODM and ORE are to be used together for automation.The subsequent chapters detail various statistical processes used for predictive analytics such as calculating attribute importance, clustering methods, regression analysis, classification techniques, ensemble models, and neural networks. In these chapters you will also get to understand the automation processes for each of these statistical processes using ODM and ORE along with their application in a real-life business use case.What you'll learnDiscover the functionality of Oracle Data Miner and Oracle R EnterpriseGain methods to perform in-database predictive analyticsUse Oracle's SQL and PLSQL APIs for building analytical solutionsAcquire knowledge ofcommon and widely-used business statistical analysis techniquesWho this book is forIT executives, BI architects, Oracle architects and developers, R users and statisticians.
Sibanjan is a Sr Analyst for Business Intelligence and Data Science evangelist. He has a strong consulting experience on Business Systems and Data Analytics. As a highly empowered consultant offering around 7 yrs of cross functional experience in the industry, he has helped several organizations to improve, automate and operationalize analytics for their business processes. He comes with a background of implementing business processes using Oracle ERP systems and predictive analytics solutions using Oracle Data Miner and Oracle R Enterprise. He is a Master of Business Analytics from Singapore Management University and holds several certification credentials such as OCA, OCP, ITIL V3, CSCMS and Six Sigma Green belt.
IntroductionChapter 1 : Getting Started with Oracle Advanced AnalyticsOverview of Data Science and CRISP-DM MethodologyOverview of machine learning and its application in industriesGetting started with Oracle Advanced Analytics- Oracle Data Miner and Oracle R EnterpriseAnalytical SQL and PL/SQL functionsSummaryChapter 2 : Installation and Hello WorldOracle Data Miner InstallationSample Hello World Oracle Data Miner workflowOracle Data Miner components for SQL Developer GUIOracle R Enterprise InstallationSample Hello World program using Oracle RSummaryChapter 3: Clustering MethodsApproaches for cluster analysisK-means algorithm fundamentalsK-means algorithm in Oracle Advanced AnalyticsMetrics for evaluating clustering algorithmsCreate clusters using Oracle SQL and PLSQL API'sCreate clusters using Oracle R EnterpriseCreate clusters using Oracle SQL Developer GUICase Study - Customer SegmentationSummaryChapter 4: Association RulesIntroduction to association rulesTerminologies associated with association rulesApriori algorithm fundamentalsIdentify interesting rulesAssociation rules using Oracle SQL and PLSQL API'sAssociation rules using Oracle R EnterpriseAssociation rules using Oracle SQL Developer GUICase Study - Market Basket AnalysisSummaryChapter 5: Regression AnalysisUnderstanding RelationshipsIntroduction to Regression AnalysisOLS Regression fundamentalsOLS Regression using Oracle Advanced AnalyticsGLM and Ridge Regression OverviewGLM Regression using Oracle SQL and PLSQL API'sGLM Regression using Oracle R EnterpriseGLM Regression using Oracle SQL Developer GUICase Study - Sales ForecastSummaryChapter 6: Classification TechniquesOverview of classification techniquesLogistics Regression fundamentalsDecision Tree fundamentalsSVM fundamentalsNaïve Bayes fundamentalsClassification using Oracle Advanced AnalyticsClassification using Oracle SQL and PLSQL API'sClassification using Oracle R EnterpriseClassification using Oracle SQL Developer GUICase Study - Customer Churn PredictionSummaryChapter 7: Advanced TopicsOverview of Neural NetworksNeural Network using Oracle Advanced AnalyticsOverview of Anomaly detectionAnomaly detection using Oracle Advanced AnalyticsOverview of Random ForestRandom Forest using Oracle Advanced AnalyticsOverview of Predictive QueriesPredictive Queries using Oracle Advanced AnalyticsOverview of Product Recommendation EngineProduct Recommendation engine using Oracle Advanced AnalyticsSummaryChapter 8: Solution DeploymentOracle Data Miner Import and Export functionalityIntroduction to PMMLGenerating PMML from Oracle Advanced Analytics models
| Erscheint lt. Verlag | 22.12.2016 |
|---|---|
| Zusatzinfo | XXII, 289 p. 318 illus., 289 illus. in color. |
| Verlagsort | Berkeley |
| Sprache | englisch |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Informatik ► Datenbanken ► Oracle | |
| Mathematik / Informatik ► Informatik ► Netzwerke | |
| Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge | |
| Informatik ► Theorie / Studium ► Compilerbau | |
| Schlagworte | Advance Analytics • Association Rules • Clustering Methods • Naive Bayes Fundamentals • Oracle • Oracle Data Miner • PL/SQL • R • Regression Analysis • Solution Deployment • SVM Fundamentals |
| ISBN-10 | 1-4842-2614-3 / 1484226143 |
| ISBN-13 | 978-1-4842-2614-8 / 9781484226148 |
| Haben Sie eine Frage zum Produkt? |
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