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The course aims to introduce students to the fundamentals of processing small to medium-scale experimental data using automated tools. Emphasis is placed on cleaning raw data, transforming it, visualizing it, and exporting the results. Students will primarily work in the R language, with extensive use of the Tidyverse ecosystem. They will also learn the basics of using the shell (bash) and GitHub for version control and code sharing.
Last update: Strejček Michal (28.05.2025)
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Attendance at lectures is recommended but not required. Last update: Strejček Michal (28.05.2025)
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The course is completed by submitting an individual project. Last update: Strejček Michal (28.05.2025)
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1. Introduction to working with data 2–3. RStudio IDE, basics of R 4–6. Tidyverse – data transformation 7–9. Tidyverse – data visualization 10. Working with strings (Stringr, regular expressions) 11–12. Basics of Linux system administration 13. Remote access and installation of software tools (Anaconda, Pip, GitHub) 14. Project consultation Last update: Strejček Michal (28.05.2025)
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Students will acquire foundational skills in working within a Linux environment and in data processing and visualization in R, with emphasis on the concept of “tidy data” and the Tidyverse ecosystem. Last update: Strejček Michal (28.05.2025)
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