MDA403 Applied Data Analytics

Faculty of Science
Spring 2026
Extent and Intensity
0/0/0. 10 credit(s). Type of Completion: z (credit).
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)
MDA304 Data Curation and Security
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The key technical course of the program, aiming to build a quantitative skillset to address applied problems with data. Methods overview given and illustrated on practical cases. The course serves as a key pre-requisite for the bachelor's practice/capstone project and more in-depth courses of the ML/AI specialization, as well as for the ML and Algorithms on Graphs
Learning outcomes
A quantitative skillset (understanding and being able to implement most common analytic methods) to address applied problems with data
Syllabus
  • Basic Concepts
  • Pattern Mining
  • Classification of Methods
  • Cluster Analysis
  • Outlier Detection
Literature
    required literature
  • HAN, Jiawei; Micheline KAMBER and Jian PEI. Data mining : concepts and techniques. 3rd ed. Boston: Elsevier, 2012, xxxv, 703. ISBN 9780123814791. info
    recommended literature
  • PROVOST, Foster and Tom FAWCETT. Data science for business : what you need to know about data mining and data-analytic thinking. 1st ed. Beijing: O'Reilly, 2013, xxi, 386. ISBN 9781449361327. info
  • MURPHY, Kevin P. Machine learning : a probabilistic perspective. Cambridge, Mass.: MIT Press, 2012, xxix, 1067. ISBN 9780262018029. info
Teaching methods
Essentially asynchronous approach; complemented by synchronous communication with the tutor upon agreement
Assessment methods
Final project for max 40 points. For successful examination, 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/jaro2026/MDA403/index.qwarp
Teacher's information
https://is.muni.cz/auth/el/sci/jaro2026/MDA403/index.qwarp
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