Oracle - Data Mining
Course Overview
| ID | 2102006 |
| Duration | 3.0 days |
| Methods | Lecture with examples and exercises. |
| Prerequisites | Oracle SQL, PL / SQL |
| Target group | Business Intelligence Developer |
| Vorgängerkurs | 2102001 |
Overview
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.
Dates
Zurzeit stehen keine offenen Termine zur Verfügung. Nutzen Sie alternativ die Inhouse‑Option.
Learn with customized examples and content—precisely tailored to your requirements.
Your benefits at a glance
- Flexible preferred date
- Customized content
- Intensive exchange
- High practical relevance
Description
Use Oracle PL/SQL and data mining to identify patterns in data, such as groups, important variables, or relationships, that can be used for classification and prediction. Learn how to transform and prepare your data with Oracle SQL so that you can then use Oracle's built-in data mining algorithms for analysis, prediction, and classification with Oracle PL/SQL.
Services
- Lunch / catering
- Help with hotel / travel
- Comelio certificate
- Flexible: free cancellation up to one day before
Comelio Media
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Content
Data Mining and Oracle
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
Factors and influences
Factor Analysis and Principal Component Analysis - Outlier Analysis
Data Mining using Association analysis
Finding frequent patterns (Frequent Itemset Mining) - Apriori algorithm - association rules and association analysis - shopping basket analysis
Data Mining and Classification
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
Data Mining and Probability Theory
Classification using logistic regression - Probability and Bayes´s Theorem - Naïve Bayes: algorithms, building and using a model
Cluster Analysis
Introduction to Cluster Analysis - Similarity and distance measurement - Variants and basic techniques - Partitioning methods: k-Means Method - Hierarchical methods: agglomerative and divisive methods
Instructor
Our Oracle trainer, Marco Skulschus, studied economics with a focus on business informatics in Wuppertal and Paris and has been working as an Oracle trainer and Oracle DB developer for data warehousing and reporting solutions for over 10 years. He has published several books on Oracle SQL and Oracle PL/SQL.
Publications
- Oracle SQL (Comelio Medien )
978-3-939701-41-5 - Oracle PL/SQL (Comelio Medien )
978-3-939701-40-8 - Oracle PL/SQL - Objektrelationale Techniken (Comelio Medien )
978-3-939701-42-2 - Oracle, PL/SQL und XML (Comelio Medien )
978-3-939701-49-1 - Oracle 10g: Programmierung mit PL/SQL, Java, PHP und C++ (Galileo Computing )
978-3898423144