Course Description:
The Course will cover the following materials:
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.
Course Objectives:
· 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.
Learning Outcomes:
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.
Teaching Resources:
Main Text Book:
Paulraj Ponniah, Data Warehousing Fundamentals, John Wiley & Sons, Inc. 2001. Book Web site: http://www3.interscience.wiley.com/cgi-bin/bookhome/93516216 (Note: The book is available Online)
Supplementary Books:
Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, 2nd Ed., Morgan Kaufmann, 2006. ISBN 1-55860-901-6 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.
Evaluation Plan:
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
Course Plan: Get the PDF version of the Course Syllabus Week 1 : Introduction : The Compelling Need for Data Warehousing (Get Slides)
· Course Description · Introduction to Data Warehouse and Data Warehousing · Date Warehousing and Data Mining · The Heart and aim of Data Warehousing · A short history of Date Warehousing
Week 2: Data Warehouse: The Building Blocks (Get Slides)
Week 3:
Data Mining Basics (Chapter 17) (Get Slides)
Trends in Data Warehousing (Chapter 2) (Get Slides)
Week 4: Planning and Project Management (Get Slides)
Week 5: Defining the Business Requirements (Get Slides)
First Exam
Week 6: Requirements as the Driving Force for Data Warehousing
Week 7: The Architectural Components
Week 8: Infrastructure as the Foundation for Data Warehousing
Week 9: The Significant Role of Metadata
Week 10: Dimensional Modeling
Week 11: Data Extraction, Transformation, and Loading
Second Exam
Week 12: Data Quality and Data Warehousing and the Web
Week 13: More on Data Mining
Week 14: The Physical Design Process
Week 15: Data Warehouse Deployment and Maintenance
