- Knowing and understanding of the basic problems, terms and measures of statistics and data science (k1,k2)
- Understanding the role of the subject in society and the ethical handling of data (k3,k4,k5)
- Applying statistical methods with the freeware LibreOffice (k3)
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- History of statistics and data science
- Data ethics
- Basic statistical concepts such as population and sample, characteristic, scale level, etc.
- Descriptive statistics: frequency distribution, quantiles, measures of location, measures of dispersion, measures of correlation
- Univariate and multivariate graphical representations of data
- Simple linear and non-linear regression including residual analysis
- Time series methods and index calculation
- Concentration measurement, dealing with missing data
- Probability calculation including Bayes' theorem
- Important probability distributions and bootstrapping
- Statistical testing
- Decision trees
- Artificial neural networks
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