x !
Archived Curricula Guide 2015–2017
Curricula Guide is archieved. Please refer to current Curricula Guides
TIETS11 Data Mining 5 ECTS
Organised by
Degree Programme in Computer Sciences
Person in charge
Professor Martti Juhola
Preceding studies
Data Structures or equivalent required. It is recommended that students have completed the basic courses in Mathematics and Statistics before taking this course.
Corresponding course units in the curriculum
School of Information Sciences
Curricula 2012 – 2015
TIETS11 Data mining 10 ECTS

Learning outcomes

The student learns the premises, objectives and relevance as well as the basic methods of data mining.


Properties of data and measurements are considered. Preprocessing methods of data are described to select and prepare data for data mining algorithms. Some data mining algorithms are presented as well as their applications, for instance, for classification and prediction of data.

Teaching methods

Teaching method Contact Online
Lectures 24 h 0 h
Exercises 10 h 0 h

Offered every second or third year.

Teaching language


Modes of study

Option 1
Available for:
  • Degree Programme Students
  • Other Students
  • Open University Students
  • Doctoral Students
  • Exchange Students
Lectures, weekly excercises and exam  Participation in course work 
In English

Written examination and completed weekly exercises.


Numeric 1-5.

Study materials

Dorian Pyle: Data Preparation for Data Mining, Morgan Kaufmann Publishers, 1999

Ian H. Witten, Eibe Frank, Mark A. Hall: Data Mining, Practical Machine Learning Tools and Techniques, third edition, Morgan Kaufmann Publishers, 2011

Belongs to following study modules

School of Information Sciences
School of Information Sciences
School of Information Sciences
Archived Teaching Schedule. Please refer to current Teaching Shedule.
School of Information Sciences