Curricula Guides
2015–2016
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
Recommended:
TIETA6
Data Structures
Corresponding course units in the curriculum
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
EnglishModes 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
School of Information Sciences
School of Information Sciences
School of Information Sciences
School of Information Sciences
School of Information Sciences
2015–2016
Teaching
Archived Teaching Schedule. Please refer to current Teaching Shedule.
School of Information Sciences