Course: Experimental data processing and analysis

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Course title Experimental data processing and analysis
Course code KALCH/C735
Organizational form of instruction Lecture + Seminary
Level of course Master
Year of study not specified
Semester Winter
Number of ECTS credits 4
Language of instruction Czech
Status of course Compulsory
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Husáková Lenka, doc. Ing. Ph.D.
Course content
1. Types of data structures, data visualization tools and software, probability distributions, descriptive statistical characteristics, data transformations, centering and scaling 2. Efficient moment estimation with extremely small sample size, statistical hypothesis testing 3. Analysis of variance (ANOVA) 4. Building linear regression models, correlation analysis 5. Precision limits and interval estimation in the calibration, method validation 6. Nonlinear regression models 7. The principles of multivariate exploratory data analysis 8. Principal component analysis (PCA) 9. Factor analysis (FA) 10. Canonical correlation analysis (CCA) 11. Discriminant analysis (DA) 12. Logistic regression (LR) 13. Cluster analysis (CLU)

Learning activities and teaching methods
Monologic (reading, lecture, briefing), Skills training
Learning outcomes
The aim of this course is to provide basic knowledge of the common statistical methods in a practical way and without extensive mathematical derivations. The course participants receive a systematic introduction to the various possibilities of the data analysis software. The training consists of an explanation of the data analysis procedures and possible fields of application. Examples in the course are generally understandable data sets from different application areas.
Students will be able to understand basic theoretical and applied principles of statistics and use these for data management, analysis and problem solving.
Prerequisites
To take full advantage of the training, students should be familiar with the graphical user interface of the Windows operating system. Basic mathematical and statistical knowledge would be advantageous but is not necessary.

Assessment methods and criteria
Written examination, Home assignment evaluation

Students will apply various data science skills, techniques, and tools to complete a project and publish a report.
Recommended literature
  • MELOUN, M.; MILITKÝ, J. Interaktivní statistická analýza dat. 3. vyd. Praha: Karolinum, 2012.
  • MELOUN M, MILITKÝ J. Kompendium statistického zpracování dat. 3. vyd. Praha: Karolinum, 2012.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester