To advance from the first to the second year, the student must acquire at least 45 ECTS from the first year. The faculty may allow the student to advance to a higher year, even if the required conditions are not met, in the following circumstances: maternity, prolonged illness, exceptional family or social circumstances, participation in top cultural, sports or professional events. A student who does not meet the conditions for enrolling in a higher year may repeat a year once during their studies or change their study programme or course due to non-fulfilment of obligations in the previous study programme or course. It is not possible to repeat the second year because an additional year (absolventski staž) is intended for fulfilling the missing obligations.

The condition for the completion of studies is the successful completion of all study obligations prescribed by the programme in the amount of 120 ECTS. A student who enrols directly in the second year, after completing a university education or specialization according to a programme adopted before 11.06.2004, must pass all prescribed differential exams and full-time study obligations of the second year. The study ends with the preparation and oral defence of the master’s thesis.

The  study programme is implemented in the form of full-time and part-time study.

Part-time study takes place in Novo mesto. The syllabus, exam periods, lecturers and conditions for the advancement of students to a higher year are the same as in full-time study.

• Striving for quality of professional work through autonomy, (self-) criticism, (self-) reflection and (self-) evaluation of the professional work;
• General understanding of the meaning of data;
• Ability to interpret the results of data analysis;
• Ability to use various software solutions for data analysis;
• Use of appropriate methodological approaches for conducting, coordinating and organizing research;
• Ability to search for sources and obtain data to perform the analysis in accordance with the given requirements;
• Ability to work in groups at all stages of data analysis;
• Ability to manage problems and transform them into easier-to-imagine models;
• Ability to think analytically and algorithmically;
• Mastering modern high-performance tools and specific data processing software;
• Ability to articulate a research problem and on this basis the ability to obtain, select, evaluate and place new information;
• Ability of flexible application of knowledge in practice;
• Ability to independently and autonomously process and maintain data;
• In-depth understanding and critical thinking about the limitations of data or data quality and its ethical use.