Learning outcomes
After completing the course, the participants
- know the phases of the process of knowledge discovery (data prepocessing, data mining and postprocessing)
- know basic data mining tasks and methods
- are aware of possibilities of utilising data mining in different research fields

Description

In data mining, large quantities of data are explored and analysed by automatic and semi-automatic means to discover novel, interesting information. Data mining is an interdisciplinary field combining e.g. methods from computer sciences and statistics. It has wide, diverse application areas from education, social, business and administrative sciences to medical and life sciences.

Course contents
-    Lectures 10 h
-    Hands-on exercises with data mining tools 10 h
-    Reading research articles related to applications of data mining methods in participant’s own field and writing a short report
-    Giving a presentation on applications of data mining in participant’s own field (presentation session 3 h)

Teachers: Kati Iltanen, Martti Juhola, Henry Joutsijoki

Target group

The course is intended for post-graduate students who are interested in data mining. No computer sciences or statistics background is required.

Enrolment: At the maximum 15 students, minimum 10 students. Selection method is draw.

Teaching:

Lectures:

4.4. at 10-12 Pinni A2089 (Juhola)

11.4.  at 10-12 Pinni A2089 (Juhola)

24.4. at 10-12 Pinni A2088 (Iltanen)

3.5. at 10-12 Pinni A2089 (Joutsijoki)

10.5. at 10-12 Pinni A2089 (Joutsijoki)

Practices:

4.4. at 12-14 Pinni B1084 (Joutsijoki)

11.4. at 12-14 Virta computer classroom 53 (Joutsijoki)

24.4. at 12-14 Pinni B1084 (Iltanen)

3.5. at 12-14 Pinni B1084 (Joutsijoki)

10.5. at 12-14 Pinni B1084 (Joutsijoki)

Presentation session

17.5. at 10-13 Pinni A2089

Evaluation: Pass/fail