Data Analysis
Course code
KOR6138.FK
old course code
Course title in Estonian
Andmeanalüüs
Course title in English
Data Analysis
ECTS credits
4.0
Assessment form
assessment
lecturer of 2025/2026 Autumn semester
Esta Kaal (language of instruction:Estonian)
Riin Aljas (language of instruction:Estonian)
Riin Aljas (language of instruction:Estonian)
lecturer of 2025/2026 Spring semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
Course aims
To create the prerequisites for the analysis of the qualitative and quantitative data of an empirical study of a successful graduation thesis and presentation of results.
To introduce the principles, possibilities and limitations of web-data collection and analysis.
To introduce the principles, possibilities and limitations of web-data collection and analysis.
Brief description of the course
Key concepts for qualitative data analysis. Types of qualitative content analysis. Practical work with visual and textual data: analysis of data, interpretation of results and visualization.
Basic definitions of descriptive statistics: mean vs. median, dispersion, standard deviation, frequency distribution, crosstables, confidence limits, evaluation of statistically significant difference (chi-square, t-student test).
Data scraping using the free usage API platforms, capabilities and limitations.
Compilation of quantitative data for analysis. Practical work, data analysis, interpretation of results and visualization.
Teaching staff provides tasks and data. The reflection of the work experience and the feedforward of the lecturer are carried out in the seminars.
Basic definitions of descriptive statistics: mean vs. median, dispersion, standard deviation, frequency distribution, crosstables, confidence limits, evaluation of statistically significant difference (chi-square, t-student test).
Data scraping using the free usage API platforms, capabilities and limitations.
Compilation of quantitative data for analysis. Practical work, data analysis, interpretation of results and visualization.
Teaching staff provides tasks and data. The reflection of the work experience and the feedforward of the lecturer are carried out in the seminars.
Learning outcomes in the course
Upon completing the course the student:
- knows the basic concepts of qualitative textual analysis, analysis techniques and ways of visualizing the results, and has implemented them in practice;
- understands the basic concepts of descriptive statistics (mean vs. median, dispersion, standard deviation, frequency distribution, crosstables, confidence limits, assessment of statistically significant differences);
- has practiced statistical analysis in Excel and visualization of the results;
- knows the possibilities and limitations of internet-based data scraping and has implemented it in practice.
- knows the basic concepts of qualitative textual analysis, analysis techniques and ways of visualizing the results, and has implemented them in practice;
- understands the basic concepts of descriptive statistics (mean vs. median, dispersion, standard deviation, frequency distribution, crosstables, confidence limits, assessment of statistically significant differences);
- has practiced statistical analysis in Excel and visualization of the results;
- knows the possibilities and limitations of internet-based data scraping and has implemented it in practice.
Teacher
Esta Kaal, MA
Study programmes containing that course