COURSE PRESENTATION FORM - DATA WAREHOUSING AND DATA MINING- 2009/2010
COURSE NAME: Data Warehousing and Data Mining
COURSE CODE: 70102
(MSc 509) / 72002 (MSc 270)
LECTURER: Mouna Kacimi and
Johann Gamper
TEACHING ASSISTANT: Mouna Kacimi
TEACHING LANGUAGE: English
CREDIT POINTS: 8
LECTURE HOURS: 48
EXERCISE HOURS: 24
TIMESPAN: 28.09.2009 - 23.01.2010
TIMETABLE: see
Timetable Page
OFFICE HOURS LECTURER: Mouna Kacimi: During the lecture time span: Faculty of CS, Piazza Università 1, Block A, 416
OFFICE HOURS TEACHING ASSISTANT:
PREREQUISITES
Programming, Basic Probability Theory and Statistics
OBJECTIVES
Enable students to understand and implement classical algorithms in data mining and data warehousing; students will be able to assess the strengths and weaknesses of the algorithms, identify the application area of algorithms, and apply them.
SYLLABUS
- Data Analysis and Uncertainty
- Classification & Prediction
- Cluster Analysis
- Association rules
- Data warehousing
- SQL OLAP extensions
- Multi-dimensional Join
- Data warehouse performance
TEACHING FORMAT
Classroom lectures and project
ASSESSMENT
The assessment of the course consists of its two parts:
- project (60%)
- theory (40%)
The project work is assessed through presentation, demo and final report. The theory is assessed with an oral exam. Each part is graded up to 30 points and both parts must be passed (at least 18 points). final_grade = project_grade*0.6 + oral_exam_grade*0.4.
READING LIST
Textbook:
- Jiawei Han and Micheline Kamber, “Data Mining: Concepts and Techniques”, Second Edition, 2006
- Pang-Ning Tan, Michael Steinbach, and Vipin Kumar, "Introduction to Data Mining", Pearson Addison Wesley, 2008, ISBN: 0-32-134136-7
- Margaret H. Dunham, “Data Mining: Introductory and Advanced Topics”, Prentice Hall, 2003
SOFTWARE USED
Weka: Data Mining Software in Java.
LEARNING OUTCOME
In-depth understanding of the main algorithms in Data Warehousing and Data Mining
COURSE PAGE
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