Data Warehouse Technologies. Data Marts. Metadata. Data warehouse Development. Architecture. Infrastructure. Platforms. Software Tools. STAR Schema. Snowflake Schema. ETL Functions. Data Quality. User Interface Features. Web-Enabled Data Warehouses. Data Mining. Physical Design. Deployment Issues. Maintenance.
·Provide a solid introduction to the topic of Data Warehousing.
·Show the difference between database and data warehousing.
·Introduce the ETL Model.
·Use the Star Schema to design a Data Warehouse.
After completing this course, the student should demonstrate the knowledge and ability to:
·Design a data warehouse or data mart to present information needed by management in a form that is usable for management clients.
·Implement a high quality data warehouse or data mart.
·Effectively administer a corporate data resource in such a way that it will truly meet management’s needs.
·Evaluate standards and new technologies to determine their potential impact on your information resource.
Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, 2nd Ed., Morgan Kaufmann, 2006.
Book Web site: http://www-faculty.cs.uiuc.edu/~hanj/bk2/index.html .
Note : From this Website, students can download the Original Book Slides prepared by the Authors of the Book.
·H. Liu and H. Motoda,Feature Selection for Knowledge Discovery and Data Mining, Kluwer, 1998.
Students will be evaluated in this course using a combination of assessment methods, including
Written Exams: First Exam: 20 Marks, Second Exam : 20 , Final Exam: 40 Marks
Assignments and Project : 20 Marks