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Learning Opportunity: Statistics

Statistics

Course Information

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.
Learning outcomes:

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..
Framework:
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.
Specialisations:

No.
Optional courses:

No.
Distinctive features of a study programme:

No.

Reference Data

Location:
Education Level:
Thematic area:
Language:
Teaching Language:
Study Type:
Duration:
Access requirements: 

Minimum education - Higher education

Credits: 

90

Provider Information

Provider Name: 
Vilnius University
Provider Type: 
University
Provider Contact Info: 

Provider phone number: +370-5-2687000 Provider email: rastine@cr.vu.lt Provider URL: http://www.vu.lt

Qualifications Awarded

Reference Data

Qualification Awarded:
Master of Statistics
Awarding body:

Vilnius University

Awarding body contact info:

Provider phone number: +370-5-2687000 Provider email: rastine@cr.vu.lt Provider URL: http://www.vu.lt

Course Locations

Reference Data

Course address:

Vilniaus m. sav. Vilniaus m. Universiteto g. 3