BKM_DAMI Datamining

Faculty of Economics and Administration
Spring 2025
Extent and Intensity
0/0/0. 6 credit(s). Type of Completion: zk (examination).
In-person direct teaching
Teacher(s)
Ing. Mgr. Jakub Buček (lecturer)
doc. Mgr. Maria Králová, Ph.D. (lecturer)
Ing. Jakub Weiner (lecturer)
Guaranteed by
doc. Mgr. Maria Králová, Ph.D.
Division of Applied Mathematics and Computer Science – Faculty of Economics and Administration
Contact Person: Lenka Hráčková
Supplier department: Division of Applied Mathematics and Computer Science – Faculty of Economics and Administration
Prerequisites
FORMA(K)
The basic terms in calculus of probability and mathematical statistics.
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The course aim is to provide students with advanced tools for solving tasks they may encounter in commercial practice (e.g. customer segmentation, marketing strategy evaluation, market basket analysis e.t.c.). Students will also become familiar with the practical procedures in the preparation, processing and analysis of data and attention will also be paid to the interpretation and presentation of results.
Analyses will be performed via the R environment, which is one of the most frequently used statistical tools in both academic and commercial areas. Moreover, it is free to download to any standard operating systems at https://cran.r-project.org/.
Learning outcomes
After graduation of the course, students will acquire the following skills:
- They will be able to define their project case (business case), choose a suitable method to meet it and correctly interpret and present the results to a wider audience (typically in the form accessible to a lower and middle management).
- They will be able to prepare and process data for further analysis.
- They will be able to detect errors in the data files systematically and will be able to apply appropriate methods to fix these errors.
Syllabus
  • At each of the three tutorials a separate project case (business case) will be presented in order to address the following topics:

    • Defining a project plan
    • Data preparation and processing (especially handling of missing data and discretization)
    • Data Exploration
    • Dimensionality reduction (eg PCA)
    • Association Analysis (market basket analysis)
    • Classification tasks (eg trees, logistic regression)
    • Cluster analysis (hierarchical and non-hierarchical clustering)
    • Interpretation and presentation of results
Literature
    recommended literature
  • HENDL, Jan. Big data : věda o datech - základy a aplikace. První vydání. Praha: Grada Publishing, 2021, 224 stran. ISBN 9788027130313. URL info
  • TORGO, Luís. Data mining with R : learning with case studies. Second edition. Boca Raton: CRC Press/Taylor & Francis Group, 2017, xix, 405. ISBN 9781482234893. info
  • WICKHAM, Hadley and Garrett GROLEMUND. R for data science : import, tidy, transform, visualize, and model data. First edition. Sebastopol, CA: O'Reilly, 2016, xxv, 492. ISBN 9781491910399. info
Teaching methods
Theoretically and practically oriented tutorials with an emphasis on the active students' approach. Study of recommended literature, individual work on project plans.
Assessment methods
To complete the course, the elaboration and defence of a semestral project are needed. In evaluation, the emphasis is placed on the given project plan specification (business case), appropriate methods selection and performance, correct interpretation and presentation of results. For Erazmus students: online participation at seminars via MS Teams, active participation at seminar work project, presence at final exam.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
The course is taught: in blocks.
Note related to how often the course is taught: 12 hodin.
Information on the extent and intensity of the course: tutorial 12 hodin.
The course is also listed under the following terms Spring 2023, Spring 2024.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/econ/spring2025/BKM_DAMI