lecturer of 2024/2025 Spring semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
Brief description of the course
Review of data analysis methods using case studies. R language areas of use, potentials and restrictions. Using R with other development tools and technologies. Examples of mathematical modelling. Encoding characteristics of real-world phenomenons into modellable form. Finding and checking relationships and dependencies. Interpreting calculation results in real environment while taking into account possible restrictions. Models in ecosystems, meteorology, traffic. Multidimentional scaling, usable equations. Syntax of R. Expressions, vectors, matrixes, standard statistical functions. Graphical presentation of results. Using add-on modules. File input and output. Iterations. Shaping data from web into suitable form for analysis. Using databases and XML-sources.
Learning outcomes in the course
Upon completing the course the student:
- knows potentials and restrictions of automated statistical analysis;
- can plan and implement model describing real-world phenomenon and interpret its results.