Learning outcomes

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

Contents

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

Finnish

Evaluation

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