Data Analysis in R - JEB157
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The course introduces data analysis and programming in R and provides a practical foundation for later work in statistics, econometrics, finance, and empirical research. It combines core programming concepts with modern workflows for data manipulation, visualization, statistical computation, and reproducible analysis.
The course is taught in person. If the course (or rather the classroom) reaches its maximum capacity, the course lecturers may deregister students for whom the course is not mandatory during the first two weeks of the semester. If you are on the WAITING LIST, don't worry about it for now, it will be handled according to the classroom capacity during the first two weeks of the semester. Last update: Kurka Josef, Mgr., Ph.D. (17.09.2026)
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After completing the course, students should be able to read, write, and debug basic R code; work with the principal R data structures; import, inspect, clean, transform, reshape, and combine data; produce informative statistical graphics; implement basic simulation, inference, and linear-model analysis; and organize a reproducible empirical analysis. The course is primarily computational. Deeper statistical and econometric theory is covered in the corresponding statistics and econometrics courses.
Last update: Kurka Josef, Mgr., Ph.D. (10.09.2026)
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• Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G.: R for Data Science (2e). • Kabacoff, R. I.: R in Action (3rd edition). • Grolemund, G.: Hands-On Programming with R. Last update: Kurka Josef, Mgr., Ph.D. (10.09.2026)
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Graded assignments:
Students are required to obtain at least 50% of available points from both the empirical project and project consultation. Individual tasks are described in more detail in the PDF syllabus. Grading scale follows the faculty regulations:
Last update: Kurka Josef, Mgr., Ph.D. (10.09.2026)
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See the attached PDF file.
Last update: Kurka Josef, Mgr., Ph.D. (10.09.2026)
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