Course for international guest/part time students
- Faculty
- Faculty of Science
- Organization
- TTK Department of Probability Theory and Statistics
- Code
- statszamu0um20gm
- Title
- Modern computational methods of statistics
- Usual semester
- Autumn
- Published semester
- 2026/27/1
- ECTS
- 3
- Language
- en
- Learning outcomes
- Knowledge: getting familiar with the main tools and methods of computational statistics Ability: compentent usage of the methods of computational statistics Attitude: the need to extend the mathematical knowledge, to gain new analytic and applied programming skills Autonomy and Responsibility: based on the gained knowledge in the mathematics of computational statistics, the students are able to decide which tools are the most suitable to solve applied problems
- Course content
- To learn and overview the multivariate statistical methods and its computational tools. Dimension reduction. Principal components, factor models, canonical correlation. Data analysis methods for discrete data, especially for binary data, logistic regression. Methods based on multivariate scaling. Correspondence-analysis. Grouping, clustering and classification. Methods analyzing survival data. Probit, logit and nonlinear regression. Cox-regression. The class is a computer-lab based practice. The used tools: mainly the R project, but possibly EXCEL, Python, Statistica, SPSS as well.
- Assessment method
- term grade
- Bibliography
- lecture notes
- Recommended bibliography
- Recommended literature: 1. http://www.statsoft.com/textbook/stathome.html 2. http://www.spss.com/stores/1/Training_Guides_C10.cfm 3. http://www.r-project.org/doc/bib/R-books.html 4. http://www.mathworks.com/access/helpdesk/help/pdf_doc/stats/stats.pdf