Admission requirements
Knowledge of databases
Description
The course Data Mining consists of a series of lectures in which advanced data mining techniques and statistical analysis will be discussed.
Course objectives
At the end of the course, students:
Should have a clear understanding of the current challenges and state of the art of databases and data mining.
Will have an understanding of the basic algorithms for preparing data and databases for data warehousing and data mining.
Will understand the basic data structures and organization that enable data analysis and data mining huge data sets.
Have an understanding of the important algorithms and challenges in several important emerging applications of data mining: mining biosequence databases, social networks, and graph mining.
Timetable
The schedule is tailor-made and will be defined by mutual agreement.
Mode of instruction
The course combines lectures, case studies, interactive discussions, assignments, research and a final paper. Students are required to fill in expected study efforts (SBUs) by co-operating, self-study and to explore literature on available resouces such as libraries, internet, etc.
There is a preparatory assignment before the first meeting.
Assessment method
Active Participation (10%)
Presentations/Assigments (30%)
Paper (50%)
ELO
DABD
Reading list
Data Mining. Practical Machine Learning Tools and Techniques (Third Edition), Morgan Kaufmann, January 2011, ISBN 978-0-12-374856-0
Various articles.
Software to use at home.
Target Audience
Marketeers, management, researchers trying to discover trends and patterns in Big Data or trying to understand data mining results. Some mathematics (algorithms of mining) is involved but no high level mathematics or proving is involved (level College). Every student can do research within the environment of their employer. The resulting paper can be focust on organization level or technical level.
Contact information
For more information, please contact Programme Co-ordinator ms. Judith Havelaar LL.M