Mit Oracle 12c

Kurslevel:
Advanced

Zielgruppe:
Business Intelligence Developer

Voraussetzungen:
Oracle SQL, PL / SQL

Methode:
Lecture with examples and exercises.

Seminarziele:
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.

Themen:
A. Data Mining and Oracle
Dauer:0.5 Tage
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

B. Factors and influences
Dauer:0.5 Tage
Factor Analysis and Principal Component Analysis - Outlier Analysis

C. Data Mining using Association analysis
Dauer:0.25 Tage
Finding frequent patterns (Frequent Itemset Mining) - Apriori algorithm - association rules and association analysis - shopping basket analysis

D. Data Mining and Classification
Dauer:0.75 Tage
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

E. Data Mining and Probability Theory
Dauer:0.5 Tage
Classification using logistic regression - Probability and Bayes´s Theorem - Naïve Bayes: algorithms, building and using a model

F. Cluster Analysis
Dauer:0.5 Tage
Introduction to Cluster Analysis - Similarity and distance measurement - Variants and basic techniques - Partitioning methods: k-Means Method - Hierarchical methods: agglomerative and divisive methods

Unsere dozenten

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:

• "Oracle PL/SQL" (Comelio Medien, ISBN 978-3-939701-40-8)


• "Oracle SQL" (Comelio Medien, ISBN 978-3-939701-41-5)


• "Oracle PL/SQL - Objektrelationale Techniken" (Comelio Medien, ISBN 978-3-939701-42-2)


• "Oracle, PL/SQL und XML" (Comelio Medien, ISBN 978-3-939701-49-1)

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

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 "Oracle Certified Associate" and passed the ComptiaCTT+ examination.

Webseite:

• http://www.marco-skulschus.de


• http://de.wikipedia.org/wiki/Marco_Skulschus

Referenzkurse:

• Oracle - Statistics using SQL
• Oracle 10g - SQL
• Relational Database Systems