Large corporations and government entities need a data
warehouse to store the key information that is used to make
data-driven decisions. Nearly every Fortune 500 corporation
is either planning to build a data warehouse or is currently
in the process of building one. However, many data warehouse
projects fail because building a data warehouse is a difficult
task that requires a combination of business sponsorship
and solid database systems expertise.
This course will provide a foundation for that expertise.
Once a warehouse is built, data mining techniques are frequently
employed to identify trends in the warehouse that may not
be readily apparent. Although many of these techniques have
been known for 30 to 40 years, they have only recently been
used as a business tool for applications (like identifying
credit card fraud or tracking customer preferences).
This course provides a thorough practical coverage of the
techniques used to build a warehouse including requirements
definitions, extract-transformation-loads of data, query
applications and executive information systems. Additionally,
data mining algorithms and techniques that identify expected
and unexpected trends in data stored in a warehouse will
be covered. This course provides hands-on experience with
data migration tools, data design tools, and data mining
tools.
In this course, you will:
- Design, implement and use a data warehouse
- Use data mining tools analyze and identify patterns
in data
- Perform extract-transformation-loads of data
- Query a data warehouse to retrieve useful information
- Migrate data from existing databases to a data warehouse
- Use OLAP services to analyze data
