InfoSphere Warehouse Components Overview
- Describe the InfoSphere Warehouse architecture
- List the components of InfoSphere Warehouse
- List the different InfoSphere Warehouse editions
Design Studio
- Describe the Eclipse platform
- Explain how to use perspectives in the Design Studio
- List the components of the Business Intelligence perspective
- Explain how to customize Design Studio perspectives
Physical Data Modeling
- Explain how to create physical data models from scratch or by reverse engineering
- Describe how to validate the model using Analyze Model
- Generate a DDL script to deploy your model to a database
- Compare objects in the project to the database to generate a delta DDL script
SQL Warehousing Tool
- Describe the use of the SQL Warehousing Tool
- List the SQL Warehousing Tool components
- Explain the use of data flow: Operators, Ports, and Connectors
- List the components of a data warehouse project
- Describe the use of variables in the SQL Warehousing Tool
- Define a data station operator
- Describe an Execution Plan Graph
Control Flows
- Define a control flow
- List the components of a control flow
- Describe control flow ports
- Explain how to use the control flow iterator
- Describe the integration between SQW and DataStage
- Explain how to import a DataStage parallel job to run in SQW
Administration Console
- Describe the SQL Warehousing Tool deployment process
- Explain the use of application profiles
- List the functions of the Administration Console
- Explain the role based security used by the Administration Console
Introduction to Cubing Services
- Define OLAP and its position with Business Intelligence
- Contrast different types of OLAP
- State the benefits of OLAP
- List the components of Cubing Services
- Define Summary Tables as used by Cubing Services
- Describe how Cubing Services exploits Summary Tables
- State the benefits of using Cubing Services
Cubing Services Metadata
- Explain how OLAP metadata is:
- Imported into the Design Studio
- Deployed to the InfoSphere Warehouse repository
- List the main tasks when defining a cube model
- Describe dimensions, levels and hierarchies
- List the different hierarchy types
- Define advanced measures for a cube model
Cubing Services Security and Virtual Cubes
- Describe Cubing Services concept of virtual cubes
- Explain how to create a virtual cube
- Give examples of where a virtual cube is appropriate
- Explain how to merge members in a virtual cube
- List the objects that make up the Cubing Services security model
- Describe the Cubing Services security model lifecycle
- Explain the resolution when there are conflicting access and deny policies for a user
Cubing Services Administration
- Describe the Cubing Services architecture
- List some examples of cube server configuration parameters
- List the cube deployment criteria
- Describe the action that takes place when queries are made against a cube server
- Describe some tools used to query a cube via a Cubing Services cube server
Cubing Services Optimization Advisor
- State the benefits of using Summary Tables
- Compare SQL used to create different Summary Tables
- Use Optimization Advisor to generate Summary Table scripts
A Data Mining Foundation
- Define data mining
- Distinguish between verification-driven and discovery-driven analysis
- Discuss where data mining can be applied
- Describe the key elements for a successful data mining project
- Describe the purposes and uses of a data mining process
- State six steps in a data mining process
An Introduction to InfoSphere Intelligent Miner
- Describe the components of InfoSphere Intelligent Miner
- List the different model types supported by InfoSphere Intelligent Miner Modeling
- Describe how InfoSphere Intelligent Miner Scoring is used
- Explain how to inspect your data using different distributions: Univariate, Bivariate, and Multivariate
- Describe how to execute a mining flow
- Discuss how to generate a Java class from a mining flow
InfoSphere Intelligent Miner Supported Mining Techniques
- Describe the Cluster function used in InfoSphere Intelligent Miner Modeling
- Describe the Classification function used in InfoSphere Intelligent Miner Modeling
- Describe the Regression function used in InfoSphere Intelligent Miner Modeling
- Describe the Associations function used in InfoSphere Intelligent Miner Modeling
- Describe the Sequential Rule function used in InfoSphere Intelligent Miner modeling
- Describe the Time Series Analyisi function used in InfoSphere Intelligent Miner modeling
Unstructured Text Analytics
- Describe the regular expression extraction capabilities of InfoSphere Warehouse
- Describe how the frequent terms analysis capabilities of the Design Studio can aid in creating a dictionary
- Describe how list base information extraction can be used to enhance a data mining run
Agenda
Day 1
- Welcome
- Unit 1: InfoSphere Warehouse Components Overview
- Unit 2: Design Studio
- Exercise for Design Studio
- Unit 3: Data Modeling
- Exercise for Data Modeling
- Unit 4: SQL Warehouse Tool
Day 2
- Exercise for SQL Warehouse Tool
- Unit 5: Control Flows
- Exercise for Control Flows
- Unit 6: Administration Console
- Exercise for Administration Console
Day 3
- Unit 7: Introduction to Cubing Services
- Unit 8: Cubing Services Metadata
- Exercise for Cubing Services Metadata
- Unit 9: Cubing Services Administration
- Exercise for Cubing Services Administration
Day 4
- Unit 10: Cubing Services Optimization Advisor
- Exercise for Optimization Advisor
- Unit 11: A Data Mining Foundation
- Unit 12: An Introduction to InfoSphere Intelligent Miner
- Unit 13: InfoSphere Intelligent Miner Supported Mining Techniques
- Topic 1: Clustering Functions
- Exercise for Clustering
- Topic 2:Predictive Models
Day 5
- Unit 13: InfoSphere Intelligent Miner Supported Mining Techniques (Continued)
- Exercise for Prediction
- Topic 3: Associations and Sequential Rule
- Exercise for Associations and Sequential Rule
- Unit 14: Unstructured Text Analysis
