Lecturer(s)


Schindler Martin, Mgr. Ph.D.

Course content

Overview of basic technics of data mining. Lectures: 1. Basic notions, overview of the software for data mining. 2. Data organization and preparation. 3. Data types, transformation, data manipulation. 4. Praphical display of data. 5. Detection of extremes. 6. Dimensional reduction, principal component analysis. 7. Decesion trees. 8. association rule mining. 9. Linear regression, prediction. 10. Logistic regression, classification. 11. Cluster analysis, partitioning and hierarchical methods. 12. Text mining. 13. Neural networks. 14. Big data. Practicals: Practical examples of the topics from lectures are dealt with at the computers. The students are taught how to apply the theory on the real data examples.

Learning activities and teaching methods

Monological explanation (lecture, presentation,briefing)
 Class attendance
 42 hours per semester
 Preparation for exam
 106 hours per semester

Learning outcomes

Overview of basic technics of data mining and their application to real data examples.
Basic overview of data mining technics and their application.

Prerequisites

Elements of probability theory, data analysis and statistics, basics of R.

Assessment methods and criteria

Oral exam, Written exam
work at the practicals, semestral work/written and oral exam

Recommended literature


Dalgaard, P. Introductory Statistics with R. 2008. ISBN 9780387790534.

HAN, J., KAMBER, M., PEI, J. Data mining: concepts and techniques. Boston: Elsevier, 2011. ISBN 780123814791.

STÉPHANE, Tuff Éry. Data mining and statistics for decision making. Wiley, 2011. ISBN 9780470688298.

YANCHANG Zhao. R and Data Mining: Examples and Case Studies. Elsevier, 2012. ISBN 9780123969637.
