E0410 Fundamentals of Statistics for Scientific Data Using R

Faculty of Science
Spring 2025
Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).
In-person direct teaching
Teacher(s)
Mgr. et Mgr. Jiří Kalina, Ph.D. (seminar tutor)
Daria Sapunova (seminar tutor)
Guaranteed by
Mgr. et Mgr. Jiří Kalina, Ph.D.
RECETOX – Faculty of Science
Contact Person: Mgr. et Mgr. Jiří Kalina, Ph.D.
Supplier department: RECETOX – Faculty of Science
Timetable
Mon 17. 2. to Sat 24. 5. Fri 10:00–11:50 D29/347-RCX2
Prerequisites
Basic math (high-school level). No need of statistical knowledge.
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
there are 8 fields of study the course is directly associated with, display
Course objectives
The aim of the course is to provide students with the essentials of common scientific statistical background and fundamental R programming skills.
Learning outcomes
After completing the course, students will be able to use R for solving common scientific data analytical tasks (designing data collection, data handling, elementary statistical tests and graphs, linear modeling) and further develop using tools provided in the course.
Syllabus
  • 1. Course description (content, assesment etc.). Basic statistical definitions, descriptive statistics. Practical in R, familiarization with RStudio, uploading data, basic calculations. 2. Descriptive statistics. Outliers, missing values. Practical in R. 3. Visualization - common graphs,aesthetics. Practical in R. 4. Data cleaning, Tidyverse package in R. Practical in R. Inspection and visualization of a dataset, results communication. 5. Hypothesis testing, p-values, distributions, data normality testing, data transformation. Practical in R. 6. Parametric and non-parametric statistics, parametric tests (t-test, ANOVA). Practical in R performing the tests, interpretation of the results. 7.Non-parametric tests (Kruskal-Wallis, Mann-Whitney). Practical in R performing the tests, interpretation of the results. 8. Pearson and Spearman correlation. Practical in R. 9. Linear regression (OLS). Practical in R. 10. Linear regression (OLS). Assumptions, model evaluation. Practical in R. 11. Multiple regression. Variables selection for a model, preparation of the model – good practice steps. Practical in R.
Literature
    recommended literature
  • WICKHAM, Hadley, Mine ÇETINKAYA-RUNDEL and Garrett GROLEMUND. R for data science : import, tidy, transform, visualize, and model data. 2nd edition. Tokyo: O'Reilly, 2023, xxiii, 548. ISBN 9781492097402. 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
  • BRUCE, Peter C. and Andrew BRUCE. Practical statistics for data scientists : 50 essential concepts. First edition. Beijing: O'Reilly, 2017, xvi, 298. ISBN 9781491952962. info
  • FIELD, Andy P. An adventure in statistics : the reality enigma. Illustrated by James Iles. First published. Los Angeles: Sage, 2016, xvi, 746. ISBN 9781446210451. info
  • FIELD, Andy P., Jeremy MILES and Zoë FIELD. Discovering statistics using R. First published. Los Angeles: Sage, 2012, xxxiv, 957. ISBN 9781446200452. info
Teaching methods
Each lesson is divided into a theoretical part with a following practical part.
Assessment methods
Final at the end of the course - working on a dataset provided by a student or the teacher. The results should be presented as a pdf file consisting of a small introduction regarding the topic, statistical method description, data visualization and interpretation of the results.
Language of instruction
English
Follow-Up Courses
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
Teacher's information
Practical parts are held in R statistical environment (freely available).
The course is also listed under the following terms Spring 2024.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/sci/spring2025/E0410