Course: Data Mining

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Course title Data Mining
Course code KAP/DMI
Organizational form of instruction Lecture + Lesson
Level of course Master
Year of study not specified
Semester Summer
Number of ECTS credits 5
Language of instruction Czech
Status of course Compulsory-optional
Form of instruction Face-to-face
Work placements Course does not contain work placement
Recommended optional programme components None
Course availability The course is available to visiting students
  • 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.
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 978-0-387-79053-4.
  • 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 978-0-470-68829-8.
  • YANCHANG Zhao. R and Data Mining: Examples and Case Studies. Elsevier, 2012. ISBN 978-0123969637.

Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester