MDA304 Data Curation and Security

Faculty of Science
Autumn 2025
Extent and Intensity
0/0/0. 8 credit(s). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Michal Veselý, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Michal Veselý, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Prerequisites
PROGRAM(B-DAE) && MDA101 Mathematics I && MDA102 Mathematics II && MDA201 Mathematics III
Basics of statistics
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.
Course objectives
Introductory course to data science. The course aims at establishing the best practices in data curation, data security, etc.
Learning outcomes
Practices in data curation;
data security
Syllabus
  • Introducing to Data Mining
  • Data Objects
  • Statistical Descriptions of Data
  • Data Visualization
  • Data Preprocessing
  • Data Warehousing
  • Data Cube
Literature
    recommended literature
  • HAN, Jiawei; Micheline KAMBER and Jian PEI. Data mining : concepts and techniques. 3rd ed. Boston: Elsevier, 2012, xxxv, 703. ISBN 9780123814791. info
  • HASTIE, Trevor; Robert TIBSHIRANI and J. H. FRIEDMAN. The elements of statistical learning : data mining, inference, and prediction. 2nd ed. New York, N.Y.: Springer, 2009, xxii, 745. ISBN 9780387848570. info
Teaching methods
Essentially asynchronous approach; complemented by synchronous communication with the tutor upon agreement
Assessment methods
Written and oral exam. The written exam is for max 40 points. For successful examination (the grade at least E), the student needs in total 20 points or more.
Language of instruction
English
Follow-Up Courses
Study support
https://is.muni.cz/auth/el/sci/podzim2025/MDA304/index.qwarp
Teacher's information
https://is.muni.cz/auth/el/sci/podzim2025/MDA304/index.qwarp
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