Computer Statistics 510-IS1-3SK-23
Profile of studies: general academic
Mode of studies: full‑time
Course type: elective
Field: natural sciences; discipline: computer science
Year / semester: 3 / —
Prerequisites (sequential system of courses and examinations): —
Introductory courses: Probabilistic Methods and Statistics
Course Hours:
Lecture: 15 hours
Laboratory: 30 hours
Teaching methods:
Lecture, laboratory
ECTS credits: 4
Student Workload
Participation in classes:
- lecture: 15 hours
- laboratory: 30 hours
Preparation for classes: 8 hours
Reading assigned literature: 4 hours
Reports, assignments, homework: 18 hours
Preparation for laboratory assessment: 8 hours
Preparation for the exam: 8 hours
Exam duration: 2 hours
Consultations: 6 hours
Quantitative indicators
Student workload requiring direct contact with the instructor: 53 hours, 2.1 ECTS
Student workload not requiring direct contact with the instructor: 46 hours, 1.9 ECTS
Type of course
Prerequisites
Prerequisites (description)
Course coordinators
Learning outcomes
Learning Outcomes
(Knowledge) The student knows fundamental:
- concepts, definitions, and theorems in statistics and understands their analytical and algebraic foundations — KP6_WG2.
- applications of the R environment in statistical data processing and analysis — KP6_WG4.
(Skills) The student is able to:
- analyze statistical problems and identify appropriate solutions — KP6_UW3.
- select suitable statistical methods for analyzing different types of data — KP6_UW3.
- correctly perform data analysis in the R environment — KP6_UW3, KP6_UW7, KP6_UW21.
- systematically expand their knowledge in statistical data analysis using the R environment — KP6_UU1.
(Social competences) The student:
- approaches problems related to statistical data processing and analysis in the R environment creatively — KP6_KO1.
Assessment criteria
General form of assessment: exam.
Laboratory: quizzes or project/reports based on laboratory work.
Bibliography
1. Wickham H., Advanced R, Chapman & Hall’s R Series, 2019 (book available at https://adv-r.hadley.nz/) [accessed 2026-04-08].
2. Grosser M., Bumann H., Wickham H., Advanced R Solutions (book available at https://advanced-r-solutions.rbind.io/) [accessed 2026-04-08].
3. Gillespie C., Lovelace R., Efficient R Programming, O’Reilly (book available at https://csgillespie.github.io/efficientR/index.html) [accessed 2026-04-08].
4. Grolemund G., Hands-On Programming with R, O’Reilly (book available at https://rstudio-education.github.io/hopr/index.html) [accessed 2026-04-08].
5. Black K., R Tutorial, https://www.cyclismo.org/tutorial/R/index.html [accessed 2026-04-08].
6. The Use R! series published by Springer, available to students through the Main Library of the University of Białystok.
7. Technical documentation of R environment library packages, available for example at: https://ftp.gwdg.de/pub/misc/cran/
9. Nowosad J.(in Polish), Elementarz programisty Wstęp do programowania używając R, Poznań: Space A., 2020 Online: https://nowosad.github.io/elp/index.html [accessed 2026-04-08].
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: