N_DZD Data Mining

University of Finance and Administration
Summer 2027
Extent and Intensity
0/2/0. 3 credit(s). Type of Completion: z (credit).
Teacher(s)
Ing. Renata Janošcová, Ph.D. (seminar tutor)
Guaranteed by
Ing. Renata Janošcová, Ph.D.
Department of Computer Science and Mathematics – Departments – University of Finance and Administration
Contact Person: Ivana Plačková
Prerequisites
Tento předmět nemá žádné předpoklady.
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The aim of the course is to introduce students to methods of automated knowledge extraction from databases. The lecture will focus on individual algorithms from the field of machine learning. Students will become familiar with one of the used KDD (Knowledge Discovery in Databases) systems. In the practical part, students will work with a selected KDD system (usually Weka).
Learning outcomes
At the end of the course students should be able to:
- understand the role of KDD for data analysis;
- understand different data mining tasks;
- understand the basic principles of various data mining algorithms;
- understand the basic methods of evaluation of created models;
- understand data preprocessing problems.
Syllabus
  • 1. The process of knowledge extraction from databases: types of tasks, sub-steps, methodologies.
  • 2. Knowledge extraction bases: database techniques, statistical methods of data analysis
  • 3. Machine learning, Weka system.
  • 4. Decision trees.
  • 5. Association rules.
  • 6. Decision rules.
  • 7. Neural networks, genetic algorithms.
  • 8. Bayesian classification, case inference.
  • 9. Interpretation of results and found knowledge; KDD project assignment.
  • 10. Data preprocessing; KDD project solution.
  • 11. New trends; KDD project solution.
  • 12. KDD project presentation.
Literature
    required literature
  • BORKOVCOVÁ, Monika, Jan MERTA et all. Enviromentální analýza dat. Žilina: EDIS, 2025. ISBN: 978-80-554-2186-5. Dostupná po registraci z: https://edis.uniza.sk/produkt/7421/Environmentalni-analyza-dat/.
  • BERKA, P. a J. GÓRECKI. [pdf] Dolování dat. Skripta SU OPF, Karviná, 2017. Dostupná z: https://is.slu.cz/el/opf/zima2021/INMNPDOD/um/Dolovani_dat.pdf
  • Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J.Pal.2017.Data Mining:Practical Machine Learning Tools and Techniques. Vol.Fourth edition. Cambridge,MA, US: Morgan Kaufmann. ISBN 9780128042915.EBSCO (e-kniha přes IS VŠFS)
  • MUKTHAR, K. P. J., HUERTA-SOTO, R. M., JAIN, V. a RAMIREZ-ASIS, E. H., eds. AI and Fintech: Improving the Financial Landscape. Boca Raton: CRC Press, 2025. ISBN 978-1-032-82517-5. DOI: 10.1201/9781003645849.
    recommended literature
  • BERKA,P. Dobývání znalostí z databází. Academia, Praha 2003. ISBN 80-200-1062-9
  • WITTEN, Ian H., Frank, EIBE et all. Data Mining : Practical Machine Learning Tools and Techniques. Edition 5. Cambridge,MA, US: Morgan Kaufmann, 2025. ISBN 9780443158889.
  • JAMSA, K. Introduction to Data Mining and Analytics. Burlington : Jones & Bartlett Learning, 2021. ISBN: 9781284180909. EBSCO (e-kniha, dostupná přes IS VŠFS)
  • NAQVI, Al. Artificial Intelligence for Audit, Forensic Accounting, and Valuation : A Strategic Perspective, John Wiley & Sons, Incorporated, 2020. ProQuest Ebook Central.ISBN:9781119601883.ProQuest Ebook (e-kniha přes IS VŠFS)
Teaching methods
Teaching takes place in the form of exercises in the full-time form of study; concentration in the combined form of study.

The following methods will be used in the exercises: lecture, discussion, team project, project presentation. In the KS form, an important study method is self-study of available materials.

The minimum mandatory attendance in classes is 50% in both forms (PS and KS).
Assessment methods
The course is concluded with a credit.

CREDIT is awarded in both forms (PS and KS) for the development of practical tasks assigned during the seminars and a team project (4-6 students), which involves the analysis of real data in the selected KDD system in accordance with the assignment. The project assignment can be found in the study materials and in MS Teams.

Attendance is required (min. 50 %), submission of the seminar and project in the submission room and presentation of the team project at the seminars.

ISP students - the same conditions as full-time students.
ISP and REPEATING students - agree with the teacher as soon as possible on the method of assessment.
Language of instruction
Czech
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
Teacher's information
The course website (above) is a LINK to the team (subject) in MS Teams (then select your teacher's channel, or the form of study - KS or PS).
It is necessary to install MS Teams in the form of an application on your laptop.
CONTACTS for the teacher: guarantor Ing. Renata Janošcová, Ph.D 37037@mail.vsfs.cz.
CONSULTATION: information can be found on the teacher's personal pages in IS VŠFS (Teaching). https://is.vsfs.cz/auth/osoba/37037#vyuka
ISP and REPEATING students: At the very beginning of the semester (first - second week), contact your teacher and agree on specific conditions of attendance and evaluation.
I RECOMMEND submitting a request for inclusion in a timetable (seminar) group for a specific teacher according to instructions from the study department. 3 credits: 75 - 90 hours of study load.
The course is also listed under the following terms Summer 2020, Summer 2021, Summer 2022, Summer 2023, Summer 2024, Summer 2025, Summer 2026.
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