The course starts from the fundamentals of statistical programming through the description of standard programming elements - data types, packages and data structures, designing user-defined functions and objects. After that, we describe how to import data from different sources and prepare them for analysis - transformation and tidying of data, managing missing values, deriving new variables from existing ones, managing date / time and textual type of data. The basics of statistical and exploratory analysis of data sets are learned. The concept of grammar of graphics and ways of designing professional visualizations are discussed. Knowledge of managing different types of distributions is acquired as well as basic ways of creating simulations. Knowledge is gained how to implement chosen machine learning methods. The programmatic approach to data mining is mastered - sampling, separation into training and test sets, creation and evaluation of predictive and descriptive models.
- Teacher: Damir Pintar
- Teacher: Ana Šarčević
- Teacher: Mihaela Vranić