Knowledge to be aquired on completion:
Methodologies for knowledge discovery in databases. Understanding of the main methodologies for classification, clustering, and association rules. Deeper study of database technologies and familiarization with data warehouses.
Acquisition of skills in applying data warehouse and data mining techniques, and in using existing tools. Acquisition of skills in extractging knowledge from data and evaluating the results.
Course Content (Syllabus)
Introduction, data issues and data preprocessing, data warehouses, classification, clustering, association rules, outlier detection, applications and case studies, practical exercises in R/Spark/RapidMiner.
Data Warehouses, Data Processing, Classification, Clustering, Association Rules
Additional bibliography for study
- Margaret Dunham, Data Mining Introductory and Advanced Topics, ISBN: 0130888923, Prentice Hall, 2003
- Jiawei Han, Micheline Kamber, Data Mining : Concepts and Techniques, 3rd edition, Morgan Kaufmann, ISBN 978-0123814791, 2011
- Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining, Pearson Addison Wesley, 2006
- Mehmed Kantardzic, Data Mining: Concepts, Models, Methods, and Algorithms, ISBN: 0471228524, Wiley-IEEE Press, 2002.