Transcript CIT 365: Data Mining and Data Warehousing

CIT 365: Data Mining and
Data Warehousing
Course Instructor: Bajuna Salehe
Email: [email protected]
Web:
www.ifm.ac.tz/staff/bajuna/courses/
Preliminary
Course Objective
• To enable student to understand how to
analyse a large pool of data to find
patterns and rules that can be used to
guide decision making and predict future
behavior.
Data Warehousing And Mining
• These are two major concepts to be
discussed in this course.
• We will start with data warehousing and
then data mining
Assessment
• Assessment will involve assignments,
tests, and Final Examination
• Assignment will be provided 2 weeks after
the commencements of lecture
– It will carry 10% of your course work
• There will be 2 tests
– Each test will carry 15% of your course work
• Final Examination will carry 60% of the
overall grade.
Recommended Readings
• Fayyad Piatesky – Shapiro, Smyth, and
Uthurusamy editors (1996), Advances In
Knowledge Discovery Data Mining, AAAI
Press/MIT Press.
• Hand Manila, and Smyth (2001), Principles Of
Data Mining MIT Press.
• Reeves Kimball and Tomthwaite Ross (1998),
Data Warehouse Life Cycle Toolkit, John Wiley
and Sons
• Han and Kamber (2001), Data Mining: Concepts
And Techniques, Morgan Kaufman.