BKM_VTAB Selected topics of business data analysis

Faculty of Economics and Administration
Spring 2025
Extent and Intensity
0/0/0. 6 credit(s). Type of Completion: z (credit).
Teacher(s)
Mgr. Bc. Martin Chvátal, Ph.D. (lecturer)
Ing. Mgr. Markéta Matulová, Ph.D. (lecturer)
Ing. Mgr. Michal Rychnovský, Ph.D. MSc (lecturer)
Guaranteed by
Mgr. Bc. Martin Chvátal, 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 (in Czech)
FORMA ( K )
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 aim of the subject is for students to get acquainted with the problems they encounter in commercial practice and their possible solutions. Furthermore, the goal is to develop teamwork skills in processing the chosen topic and presenting the findings achieved.
Learning outcomes
After completing the subject, the student will be able to: - Conduct research (online) of sources to quickly orient themselves in a new topic. -Utilize acquired knowledge in the field of data analysis to solve practical problems and present the results achieved. - Collaborate in a project team in accordance with established rules of cooperation and specified deadlines.
Syllabus
  • Students will work in teams of two to three to participate in solving selected topics from the provided list: • Examples of using mathematics and statistics in analytical practice • Examples of misinterpretation of statistical results • Integration of R with existing company data sources • Example of an R script for solving a real-world work problem • Programs and procedures for analyzing financial company data • GDPR and data legal protection • How to secure data on a computer, flash drive, and online • Common and painful mistakes in working with SQL • Indexing and optimization of SQL scripts • Data analysis from Google Analytics in digital marketing • Export and formatting - how to handle situations where data is exported in various formats and needs to be converted into a common format for processing • "Cleaning" data - specific examples of dealing with databases that contain incomplete or blanket entries • Demonstration of the use of interesting public databases (e.g., ČSÚ, ARAD from ČNB) for individual projects - e.g., www.datapaq.cz • How to efficiently automatically update publicly available data in a worksheet (e.g., in Excel) or on your own website • Presentation of work in Power BI • Online educational courses for improving data handling skills - examples, tips, evaluations • Introduction to machine learning and working with "bots" - e.g., using Azure to create a custom chatbot for a website • The phenomenon of "low" coding - creating applications for data processing by "laymen" in the MS Power Apps environment • Data scraping in R, Python, or Power Automate along with presenting the acquired data • Practical demonstration of connecting to selected APIs using R or Python and applying statistical methods to the obtained data • Creating a simple personal website on WordPress and analyzing visitor data • Connecting a personal website with Shiny with interesting statistics • Interesting R packages • How to present and how not to present data • How to create a questionnaire, possible pitfalls, how to distribute it, where, and analysis of data obtained from it • Application of data science in the financial sector • Data prediction modeling • Automation of routine tasks in Excel or R using scripts
Literature
  • BAUMER, Benjamin, Daniel KAPLAN and Nicholas J. HORTON. Modern data science with R. 2nd edition. Boca Raton: CRC Press, Taylor & Francis Group, 2021, xvii, 631. ISBN 9780367745448. info
  • GEMIGNANI, Zach, Chris GEMIGNANI, Richard GALENTINO and Patrick Jude SCHUERMANN. Efektivní analýza a využití dat. Translated by Jiří Huf. 1. vydání. Brno: Computer Press, 2015, 240 stran. ISBN 9788025145715. info
  • 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
Teaching methods
Peer-to-peer learning, teamwork in solving practical problems, presentation, and feedback.
Assessment methods
The subject is completed with a credit based on the presentation of the solution to the chosen topic and active participation in the discussion.
Language of instruction
Czech
Further comments (probably available only in Czech)
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 (Spring 2025, recent)
  • Permalink: https://is.muni.cz/course/econ/spring2025/BKM_VTAB