Data Sciences
Optional VIRTUAL real-time attendance at lectures and tutorials
The master’s study programme Data Sciences equips graduates with in-depth knowledge of modern data analysis and specific knowledge related to the specifics of data within individual disciplines or special types of data. This includes a wide range of advanced methods of data analysis and other concepts of modern data sciences. It also includes independent development of algorithmic and software solutions for various types of problems related to data analysis for scientific, commercial and other purposes. By connecting mathematical, informational and social science topics, students will obtain a comprehensive set of complementary knowledge that will enable problem-solving of processing and analysis of large databases in real environment, and the use of artificial intelligence.
The study programme is carried out as a full-time and part-time mode of study.
The full-time study is free for employed students as well.*
*For EU citizens and citizens of the countries with signed bilateral agreement with Slovenia.
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Enrolment conditions
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Conditions for advancing
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Conditions for the completion
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Part-time study
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Competences
The following individuals can be enrolled in the first year of the master’s study programme Data Sciences:
- Who has completed a bachelor’s study programme in the fields of mathematics, statistics or computer sciences. The competent faculty authority does not prescribe additional study obligations based on the application for enrolment;
- Who has completed a bachelor’s study programme in the field of Informatics or other natural sciences. Based on the application for enrolment, the competent faculty authority prescribes additional study obligations to the candidate in the range of 12 ECTS;
- Who has completed a bachelor’s study programme in the fields that belong to the study fields of social sciences and business and administrative studies. Based on the application for enrolment, the competent faculty authority prescribes additional study obligations to the candidate in the range of 18 ECTS;
- Who has completed a bachelor’s study programme in other fields of study. Based on the application for enrolment, the competent faculty authority prescribes additional study obligations to the candidate in the range of maximum 31 ECTS.
For graduates of higher professional study programmes adopted before 11. 06. 2004, the provisions for graduates of bachelor’s study programmes shall apply mutatis mutandis.
Anyone who has completed an equivalent education abroad can also enrol in the first or subsequent years of the master’s study programme Data Sciences. At the request of the applicant, FIS determines the equivalence of education acquired abroad within the procedure of education recognition.
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 (graduate traineeship) 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.
Students of the master’s programme Data Sciences obtain the following competences:
- 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.
Students of the master’s programme Data Sciences will also obtain course-specific competences, which are listed in the individual curricula of the programme’s curriculum.
Curriculum - Data Sciences (MA)
The programme lasts 2 years and is divided into 4 semesters. It ends with the preparation of a master’s thesis. Consequently, in the third semester, the student selects a mentor for the master’s thesis, with whom they prepare a draft thesis through consultations. In the fourth semester, the student prepares and defends their master’s thesis.
Explanation of the table: The table includes all obligatory courses specified upon enrolment in the stated academic year. Each listed course:
- serves as a link to the course syllabus,
- the letter L next to a course represents a link to the lecturer delivering the lectures for the selected course,
- the letter T next to a course represents a link to the lecturer conducting the tutorials for the selected course.
Obligatory Courses
1st year | 2nd year |
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Linear Algebra and Optimization | Data Warehouses and Data Analytics |
Selected Topics in Probability and Statistics | Big Data Analysis |
Introduction to Data Science | Machine Learning 2 |
Data Science Programming | Statistical Learning and Modeling |
Data Visualization | Thesis Seminar |
Data Mining | High Performance Computing |
Categorical Data Analysis | Creativity and Critical Thinking |
Machine Learning 1 | Master's Thesis |
Elective Course 1 | |
Elective Course 2 | |
Elective Course 3 |
The lecturers for the courses in the academic year 2025/2026 will be announced at a later date.
1st year | 2nd year |
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Linear Algebra and Optimization - L, T | Data Warehouses and Data Analytics - L, T |
Selected Topics in Probability and Statistics - L, T | Big Data Analysis - L, T |
Introduction to Data Science - L, T | Machine Learning 2 - L, T |
Data Science Programming - L, T | Statistical Learning and Modeling - L, T |
Data Visualization - L, T | Thesis Seminar - L, T |
Data Mining - L, T | High Performance Computing - L, T |
Categorical Data Analysis - L, T | Creativity and Critical Thinking - L, T |
Machine Learning 1 - L, T / L, T | Master's Thesis |
Elective Course 1 | |
Elective Course 2 | |
Elective Course 3 |
1st year | 2nd year |
---|---|
Linear Algebra and Optimization | Data Warehouses and Data Analytics |
Selected Topics in Probability and Statistics | Big Data Analysis |
Introduction to Data Science | Machine Learning 2 |
Data Science Programming | Statistical Learning and Modeling |
Data Visualization | Thesis Seminar |
Data Mining | High Performance Computing |
Categorical Data Analysis | Creativity and Critical Thinking |
Machine Learning 1 | Master's Thesis |
Elective Course 1 | |
Elective Course 2 | |
Elective Course 3 |