Statistical Analysis Solutions in R
Course code
IFI6245.DT
old course code
Course title in Estonian
Andmeanalüüsi lahendused R-keeles
Course title in English
Statistical Analysis Solutions in R
ECTS credits
6.0
Assessment form
Examination
lecturer of 2024/2025 Autumn semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
lecturer of 2024/2025 Spring semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
Course aims
Create prerequisites for developing skills and abilities in automated data analysis, finding appropriate ways to present specific data and drawing conclusions based on them.
Brief description of the course
Areas of use, possibilities and limitations of the R language. Data summaries and presentations. Using the R language with other development tools and technologies. Statistical tests, generalization using them. T-test, test of proportions, chi-square test. Examples of mathematical modeling. Adapting the characteristics of real phenomena to the modeled form. Interpreting the results of calculations in a real environment. Dimensionality reduction of multi-attribute datasets - multi-dimensional scaling, principal component analysis. Grouping of data (k-means), estimation of group membership probability. Data analysis examples in language technology, ecological data, educational data.
Learning outcomes in the course
Upon completing the course the student:
- knows the possibilities and limitations of automated data analysis;
- can describe the data set in a versatile manner using figures, tables, figures and animations;
- can compare mean values using mathematical tests, evaluate the strength of the correlation relationship, answers found using regression, group data;
- can design and program a model used for a real phenomenon and interpret the results obtained with it.
- knows the possibilities and limitations of automated data analysis;
- can describe the data set in a versatile manner using figures, tables, figures and animations;
- can compare mean values using mathematical tests, evaluate the strength of the correlation relationship, answers found using regression, group data;
- can design and program a model used for a real phenomenon and interpret the results obtained with it.
Teacher
Jaagup Kippar
The course is a prerequisite
Study programmes containing that course