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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.