Course description

In the course, students will gain a comprehensive knowledge of reproducible data analysis starting from the creation of the data table to the publication of the results by learning about the theory and practice of the methods used. The analysis of a data table containing repeated measurements is performed according to the requirements of reproducible research, from the creating and cleaning of the data table to the publication of the results in a publishable form. Students learn how to explore the distribution and relationship of the variables with advanced exploratory analyses and machine learning techniques. They learn how to deal with the problem of missing data and the methods of discovering their structure. They learn also how to formulae scientific and then statistical questions, perform analyses, check, interpret the results, and then bring them into a form suitable for publication.

Mandatory resources:

• Fehérvári, P.: Computational Statistics, lecture and practical slides handouts (for internal use)
• Wickham, H., & Grolemund, G. (2016). R for data science: import, tidy, transform, visualize, and model data. O’Reilly Media, Inc.

Recommended resources:

• Wickham et al., (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4(43), 1686,
• Little R, Rubin D (2014). Statistical Analysis with Missing Data. John Wiley & Sons.
• Xie, Yihui. Dynamic Documents with R and knitr. Chapman and Hall/CRC, 2016.
• Lantz B, Machine Learning with R, 2ed, Packt, 2015.