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Lecturer(s)
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Schindler Martin, Mgr. Ph.D.
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Course content
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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.
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Learning activities and teaching methods
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Monological explanation (lecture, presentation,briefing)
- Class attendance
- 42 hours per semester
- Preparation for exam
- 106 hours per semester
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Learning outcomes
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Overview of basic technics of data mining and their application to real data examples.
Basic overview of data mining technics and their application.
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Prerequisites
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Elements of probability theory, data analysis and statistics, basics of R.
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Assessment methods and criteria
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Oral exam, Written exam
work at the practicals, semestral work/written and oral exam
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Recommended literature
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Dalgaard, P. Introductory Statistics with R. 2008. ISBN 978-0-387-79053-4.
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HAN, J., KAMBER, M., PEI, J. Data mining: concepts and techniques. Boston: Elsevier, 2011. ISBN 780123814791.
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STÉPHANE, Tuff Éry. Data mining and statistics for decision making. Wiley, 2011. ISBN 978-0-470-68829-8.
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YANCHANG Zhao. R and Data Mining: Examples and Case Studies. Elsevier, 2012. ISBN 978-0123969637.
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