Datawarehousing Training Overview
This course provides the students with skills necessary to understand and evaluate various available technologies in the area of Database Mining and Warehousing. It compares and contrasts various available techniques available for database mining, the algorithms behind each one, and which applications each is best suited for.
Datawarehousing Training Prerequisites
At least some experience with any relational database management systems.
Datawarehousing Training Course duration
2 Days
Datawarehousing Training Objectives
More specifically, this course will enable the students to:
- Understand the concepts in Data Mining and Data Warehousing.
- Understand Industry specific terms and abbreviations in use.
- Understand the various techniques/algorithms used in Data Mining.
- Compare and contrast the available products and tools.
- Assess the requirements for building a data mart/warehouse.
- Understand OLAP, multidimensional databases, and data organization for analytical processing.
- Learn from the success and failure examples from prior applications of this technology in the industry.
- Learn about the research directions in this field, and the resources to keep their knowledge up to date.
Datawarehousing Training Course outline
- Introduction and Overview
- Current Trends in marketplace
- The Need for Data Mining
- Terminology: Data Marts, Warehouses, and Information Warehouses
OLTP, OLAP, and Data Mining.
- Types of Warehouses
- Multi-dimensional databases
- Success Stories
- Horror Stories
- Developing the Warehouse
- Different Organizations/Schemas
- Star Schema
- Snowflake Schema
- Modeling (ER and EER) and Normalization
- Data Transformation/Cleansing
- Size Considerations
- Hardware Estimation
- Challenges and Issues
- Factors to Consider
- Correctness
- Scalability
- Optimization
- Usability
- Storage Management
- Using Read Only Tablespaces
- Hardware Optimization
- Query Optimization
- BitMapped Indexes
- Database Snapshots
- Partition Views
- Meta Data Management
- Invalidation/Refresh of generated aggregates.
- Data Mining: Perspectives
- The AI view
- The Statistics View
- The Database View
- OLAP and Data Mining: Comparison
- The Role of OLAP
- (MD) OLAP and ROLAP: ViewPoints
- Differences between OLAP and DataMining
- Techniques for Data Mining
- Neural Nets
- Classification Trees
- Regression Analysis
- Rule Induction
- Rule Induction: In depth Overview
- Market Basket Analysis
- Generalized Association Rules
- Post Processing + Applications
- Typicality/Atypicality
- Distinguishing Segments
- Predicting Missing Values
- What's Available:
- Data Warehousing Tools
- Data Mining Tools
- Middleware Tools
- Magazines, Webzines and Journals
- Conferences and Papers Published
- Other Resources on the Web
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