FSpS:CORE149 DATA-A: Data analysis for ever

CORE149 DATA-A: Data analysis for everyone

Faculty of Sports Studies
spring 2025

Extent and Intensity

0/2/0. 3 credit(s). Type of Completion: k (colloquium).

Teacher(s)

Mgr. Michal Bozděch, Ph.D. (seminar tutor)
Mgr. Martin Sebera, Ph.D. (seminar tutor)
Mgr. et Mgr. Filip Zlámal, Ph.D. (seminar tutor)

Guaranteed by

Mgr. Michal Bozděch, Ph.D.
Department of Physical Education and Social Sciences – Faculty of Sports Studies
Supplier department: Department of Physical Education and Social Sciences – Faculty of Sports Studies

Timetable of Seminar Groups

CORE149/01: Thu 15:00–16:40 E34/203 - seminární místnost, M. Bozděch

Prerequisites

TYP_STUDIA(BM) && FORMA(P)
This course does not require any strict formal prerequisites. However, to make the most of the course content, it is advisable that students:
- have basic computer skills (operating system navigation, file management, navigating the university information system),
- be familiar with spreadsheet software (e.g., MS Excel, Google Sheets) at a basic level,
- possess at least a high-school level of mathematical knowledge (basic arithmetic, working with percentages and fractions),
- be willing to actively participate in discussions and practical assignments during seminars, - install the recommended statistical software (e.g., JASP, SPSS, MATLAB) and bring their own laptop to each seminar.

The above-mentioned skills and knowledge will make it easier to follow the course material and complete practical tasks, but they are not strictly required for enrollment.

Course Enrolment Limitations

The course is offered to students of any study field.
The capacity limit for the course is 30 student(s).
Current registration and enrolment status: enrolled: 8/30, only registered: 1/30, only registered with preference (fields directly associated with the programme): 0/30

Course objectives

The goal of the “CORE149 DATA-A: Data Analysis for Everyone” course is to introduce students to the key principles and procedures of data analysis so that they can effectively apply them in their field of study and in everyday life. The course is designed primarily for students in non-mathematical degree programs, focusing on practical demonstrations and clear explanations. Students will learn to understand different types of data, select and apply suitable analytical procedures, interpret the results, and communicate them in a comprehensible manner. The course includes interactive demonstrations of more than 30 different types of analyses, covering both general and field-specific scenarios. The course builds on general knowledge of data processing and presentation, expanding it with a critical approach to data and the ability to assess data quality in relation to research objectives. Its interdisciplinary scope helps students develop competences that foster better collaboration with experts across various fields. The skills acquired can be applied in subsequent courses focusing on statistical methods, research projects, and advanced data analysis, as well as to better understand the world around them and support creative and scientific endeavors.

Learning outcomes

Upon successful completion of the “CORE149 DATA-A: Data Analysis for Everyone” course, students will be able to:
- explain and apply the fundamental principles of data analysis, including the lifecycle of research data,
- select and utilize appropriate analytical procedures and statistical tests to address specific research problems,
- interpret analytical results and draw substantiated conclusions with regard to ethics and research objectives,
- create comprehensible data visualizations (e.g., graphs, tables) and present them in a way that supports effective communication,
- critically evaluate the quality of processed data and discuss findings with colleagues from various fields as well as the general public.

Syllabus

1. Introduction to the Course
• Course content and objectives (completion requirements)
• History of data analysis
• Good and bad practices in data analysis
• Current trends and the future of data analysis
• Freely available software for data analysis
2. Life Cycle of Data in Research
• Design, collection, cleaning, analysis, interpretation, and sharing of data
• Types of variables and data
• Descriptive statistics: role, purpose, and types
• Power analysis and sample size determination
• Data visualization based on purpose and data types
3. Statistical Hypothesis Testing
• History and purpose
• Types of hypotheses
• Significance level and relationship to confidence intervals
• Type I and Type II errors, test sensitivity and specificity
4. Measures of Association
• Purpose and classification according to research problems and data types
• Overview of suitable tests and significance of testing in a broader context
• Computation procedures for categorical data: Odds ratio test, Relative risk, Chi-square (three variants)
• Computation procedures for metric data: Pearson's and Spearman's correlation coefficients, assumptions testing
• Interpretation and visualization of results
5. Tests for Differences between Means
• Purpose and classification according to research problems and data types
• Overview of tests and significance of testing mean differences in a broader context
• Computation procedures for three different t-tests and their nonparametric alternatives
• Computation procedures for ANOVA, ANCOVA, and MANCOVA tests and their paired and nonparametric alternatives
• Interpretation and visualization of results
6. Regression Analysis
• Purpose and classification according to research problems and data types
• Overview of tests and significance of testing in a broader context
• Computation procedures: Linear, logistic/probit, and multiple linear regression, assumptions testing
• Interpretation and visualization of results
7. Mixed-Design Analysis
• Purpose and classification according to research problems and data types
• Overview of tests and significance of testing in a broader context
• Computation procedures for binomial logistic regression with categorical predictors, multinomial logistic regression, Factorial ANOVA, Mixed-Design ANOVA, Factorial MANOVA, Mixed-Design MANOVA, assumptions testing
• Interpretation and visualization of results
8. Survival Analysis
• Purpose and classification according to research problems and data types
• Parametric, semi-parametric, and nonparametric models
• Overview of tests and significance of testing in a broader context
• Computation procedures: Life tables, Kaplan-Meier analysis, Survival rate and hazard function, Cox proportional hazards regression analysis, Parametric survival analytical models, Survival trees, assumptions testing
• Interpretation and visualization of results
9. Cluster and Factor Analysis
• Purpose and classification according to research problems and data types
• Hierarchical and non-hierarchical Cluster analysis
• Exploratory and Confirmatory Factor analysis
• Overview of tests and significance of testing in a broader context
• Computation procedures for Cluster (k-means, nearest neighbor) and Factor analysis (principal components)
• Interpretation and visualization of results
10. Bayesian Statistics
• Purpose and classification according to research problems and data types
• Overview of tests and significance of testing in a broader context
• Computation procedures
• Interpretation and visualization of results
11. Neural Networks
• Types of artificial intelligence, types of machine learning and neural networks and their components
• Programming languages
• Practical examples
• Data preparation demonstration
• Model generation and tuning (fine-tuning) procedure
• Interpretation and visualization of the generated model
12. Misuse of Data Analysis
• Practical examples
• Methods for reducing misuse of data analysis
• Conclusion and summary of the course

Literature

recommended literature

Goss-Sampson, M. (2019). Statistical analysis in JASP: A guide for students (5th ed.). ).
RABUŠIC, Ladislav, Petr SOUKUP and Petr MAREŠ. Statistická analýza sociálněvědních dat (prostřednictvím SPSS) (Statistical data analysis (with SPSS)). 2., přepracované vyd. Brno: Masarykova univerzita, 2019, 573 pp. ISBN 978-80-210-9247-1. URL info

not specified

KIM, Phil. MATLAB deep learning : with machine learning, neural networks and artificial intelligence. New York: Apress, 2017, xvii, 151. ISBN 9781484228449. info

Teaching methods

The course is conducted in the form of seminars, where students are introduced to the theoretical framework of the subject and simultaneously gain hands-on experience using pre-prepared data available in the information system. Each seminar includes a discussion of theoretical foundations, a collaborative data analysis session, and a short written test (KvIS), for which achieving at least 60% of the possible points is required to receive credit. Three times during the semester, students will also complete a feedback questionnaire that serves to evaluate the effectiveness of the teaching process and, if necessary, adjust the course content or methods of working with data.

Assessment methods

During the semester, active participation in seminars is required, allowing a maximum of three unexcused absences. Any additional absences must be properly excused according to MU’s Study and Examination Regulations. Knowledge acquisition is primarily verified by short written tests (KvIS) at the end of each seminar, and students must achieve at least 60% of the total possible points for a passing grade in the continuous assessment. Students who do not meet this requirement may take a final exam (colloquium). During this exam, they will be given a randomly selected assignment requiring them to choose an appropriate test, conduct the data analysis, interpret the results, and describe their procedure. Only personal notes and course materials may be used; the use of the internet or AI-based tools is prohibited.

Náhradní absolvování

The course can be completed in a non-standard way within the framework of an individual study plan, which students agree with the course guarantor before the start or at the beginning of the semester and enter into the IS.

Language of instruction