Entry requirements

120 credits with 30 credits in mathematics, including mathematical statistics, 45 credits in computer science and/or engineering, including Database Design I and a second course in computer programming. Algorithms and Data Structures I is recommended.

Learning outcomes

To pass, the student should be able to:

• explain different methods to extract processed information from large amounts of data, both in theory and in practical application
• use these methods with appropriate tools
• evaluate and compare the suitability of different methods.

Content

Introduction to data mining, its terminology and overview over various types of data and its properties, an overview of different methods to explore and visualise large amounts of data, introduction to classification methods, introduction to clustering methods, introduction to association analysis, handling of personal integrity in the area of data mining.

The subjects are treated both theoretically and practically through laboratory sessions where selected methods are implemented and tested on typical amounts of data.

Instruction

Lectures, seminars, laboratory sessions and written assignments. Guest lecture.

Assessment

Written examination (3 HE credits) and assignments that are presented orally and written (2 HE credits).