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Archived Curricula Guide 2011–2012
Curricula Guide is archieved. Please refer to current Curricula Guides
TKOPS126 Data Mining 10 ECTS
Organised by
Computer Science
Preceding studies
It is recommended that students have completed the obligatory basic courses in Mathematics before taking this course.

Learning outcomes

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


The relevance and definitions of data and data measurement, visualisation and analysis of data, unstructured and incomplete data, data mining algorithms, models and patterns, scoring functions of data mining algorithms, searching and optimisation methods, descriptive modelling, predictive modelling of classification, data management and databases in conjunction with data mining, searching for patterns and rules, and searching on the basis of contents.

Teaching methods

Teaching method Contact Online
Lectures 48 h 0 h
Exercises 20 h 0 h

Teaching language


Modes of study

Written examination and completed weekly exercises.


Numeric 1-5.

Recommended year of study

Advanced studies, 2nd year, spring semester or later.
The course is not lectured every year.

Study materials

  1. Hand D., Mannila H. & Smyth P., Principles of Data Mining. MIT Press 2001.

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