Fundamentals in statistical data preparation with special focus on available tools. Students will gain selected knowledge in statistics and will be able to apply it in modern data analysis. Application of the models to real data is achieved by using a selected programming environment.

Mathematics for IT 1+2, Statistics Probability theory

The following topics will be covered in the course:

• Special statistical methods (multivariate regression + regularization, classification, clustering, contingency tables, ...)
• Statistical analysis models (regression model, exploratory data analysis, ...)
• Statistical analysis of network data

The following basic literature will be used in the course:

• James G. et al: "An Introduction to Statistical Learning: with Applications in R", Springer, 6th edition, 2016;
• Hastie J. et al: "The Elements of Statistical Learning: Data Mining, Inference, and Prediction", Springer, 2nd edition, 2017;

Lecture and exercises

Final grade comprised of

• Class participation,
• Final exam