Objective(s) of a study programme:
to prepare a high-level specialist, which could critically analyze existing statistical methods, as well as newly emerging ones, be able to apply them in non-standard situations, be ready to extend autonomously his knowledge in the selected field of statistics.
The graduates will:
1) be able to analyze complex real processes using methods of probability theory and mathematical statistics and interdisciplinary knowledge;
2) be able to present the results of a research to the specialists in statistics and other areas, following professional ethics;
3) be able to analyze the results and methods of mathematical statistics presented in the newest scientific literature;
4) be able to master new programming software for non-standard problem solving;
5) understand differences between classical and Bayes approach to statistics;
6) be able to investigate properties of sophisticated statistics;
7) be able to apply the newest achievements in the Markov processes theory;
8) be able to analyze statistics of dependent samples;
9) be able to choose and adapt appropriate quality control schemes and control charts of technological processes;
10) be able to choose and modify models of survival analysis and reliability theory used in engineering, medicine and biology;
11) know and be able to apply the newest statistical methods for analyzing big data arrays;
12) know the mathematical substantiation and limits of application of these methods.
Activities of teaching and learning:
lectures, practical and computer classes, literature studying and problem solving by himself, individual and group statistical projects, writing master thesis..
Methods of assessment of learning achievements:
written papers and exams, programming tasks, computer statistical tasks, master thesis defense..
Study subjects (modules), practical training:
The programme encompasses 90 credits in total:
1) subjects of theoretical statistics (Markov chains, Bayes statistics, Multivariate statistics, Finite population statistics, Time series, Multiparametric statistics) – 30 credits;
2) applications of statistics in various fields (Survival analysis, Stochastic reliability models, Quality control systems, Data mining, Statistical learning algorithms, Functional data analysis) – 30 credits;
3) master thesis – 30 credits.
Distinctive features of a study programme: