TIETS11 Data Mining 5 ECTS
Organised by
Degree Programme in Computer Sciences
Person in charge
Professor Martti Juhola
Preceding studies
Recommended:
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 2015 – 2017
TIETS11 Data Mining 5 ECTS

Learning outcomes

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

Contents

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

English

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.

Evaluation

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

Faculty of Natural Sciences
Faculty of Natural Sciences
Faculty of Natural Sciences
Faculty of Natural Sciences
2017–2018
Teaching
Faculty of Natural Sciences