Oracle 11g - Data Mining

• Dauer:
  3 Tage
• Zeit:
  10:00 - 16:00
• Lieferart:
  Online
• Zielgruppe:
  Business Intelligence Developer
• Vorkenntnisse:
  Oracle SQL, PL / SQL
• Methode:
  Lecture with examples and exercises.
• Erfahren Sie in der Demo mehr darüber, wie Online-Kurse durchgeführt werden.
• Kursnummer:
  2023667
• 
  Themen:
  Oracle Data Mining (ODM) provides powerful data mining functionality as native SQL functions within the Oracle Database. Oracle Data Mining enables users to discover new insights hidden in data and to leverage investments in Oracle Database technology. With Oracle Data Mining, you can build and apply predictive models that help you target your best customers, develop detailed customer profiles, and find and prevent fraud. This training provides you with an overview of the Oracle Data Mining architecture and shows you what kind of Data Mining algorithms you can use for your data analysis. You will get to know each algorithm´s principle and statistical-mathematical background before you see the algorithm being applied to DB data.

Statistics, multivariate statistics and Data Mining - Data Mining cycle - Data preprocessing: Descriptive data aggregation, data cleansing, data integration and transformation - Data Reduction - Discretization and concept hierarchies - Data Mining and Business Intelligence: Databases, Data Warehouses and OLAP as the basis for Data Mining - Oracle architecture for Data Mining: database, Data Mining module and MS Excel add-in

Factor Analysis and Principal Component Analysis - Outlier Analysis

Finding frequent patterns (Frequent Itemset Mining) - Apriori algorithm - association rules and association analysis - shopping basket analysis

Decision Trees: selection of attributes, tree pruning, deduction of rules, quality measures and comparison of models - Support Vector Machines: algorithms, building and using a model

Classification using logistic regression - Probability and Bayes´s Theorem - Naïve Bayes: algorithms, building and using a model

Introduction to Cluster Analysis - Similarity and distance measurement - Variants and basic techniques - Partitioning methods: k-Means Method - Hierarchical methods: agglomerative and divisive methods

Marco Skulschus (born in Germany in 1978) studied economics in Wuppertal (Germany) and Paris (France) and wrote his master´s thesis about semantic data modeling. He started working as a lecturer and consultant in 2002.

Veröffentlichungen

• "Grundlagen empirische Sozialforschung" (Comelio Medien, ISBN 978-3-939701-23-1)
• "System und Systematik von Fragebögen" (Comelio Medien, ISBN 978-3-939701-26-2)
• "Oracle PL/SQL" (Comelio Medien, ISBN 978-3-939701-40-8)
• "MS SQL Server - T-SQL Programmierung und Abfragen" (Comelio Medien, ISBN 978-3-939701-69-9)

Erfahrung: - He works as an IT-consultant and project manager. He developed various Business Intelligence systems for industry clients and the public sector. For several years now, he is responsible for a BI-team in India which is mainly involved in BI and OLAP projects, reporting systems as well as statistical analysis and Data Mining. He led several research projects and was leading scientist and project manager of a publicly funded project about interactive questionnaires and online surveys.

Projekte: - He works as an IT-consultant and project manager. He developed various Business Intelligence systems for industry clients and the public sector. For several years now, he is responsible for a BI-team in India which is mainly involved in BI and OLAP projects, reporting systems as well as statistical analysis and Data Mining.

Forschung: He led several research projects and was leading scientist and project manager of a publicly funded project about interactive questionnaires and online surveys.

Zertifizierung: Marco Skulschus is "Microsoft Certified Trainer", “Oracle Associate” and passed the ComptiaCTT+ examination.

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