Data Sciences
Optional VIRTUAL real-time attendance at lectures and tutorials
The study programme 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.
Curriculum
1st year | 2nd year | Elective courses |
---|---|---|
Linear Algebra and Optimization | Data warehouses and data analytics | Analysis of complex networks |
Selected topics in probability and statistics | Big data analysis | Ethics and Entrepreneurial Communication |
Introduction to Data Science | Machine learning 2 | Selected Topics in Algorithms |
Data Science Programming | Statistical learning and modeling | Marketing Communication |
Data visualization | Thesis Seminar | Modeling complex systems |
Data mining | High Performance Computing | Knowledge Management |
Categorical data analysis | Creativity and Critical Thinking | Numerical mathematics |
Machine learning 1 | Master's thesis | Organizational psychology |
Elective course 1 | Internet Research | |
Elective course 2 | The introduction to the business, employment, contract and tort law | |
Elective course 3 | Fuzzy-Set Analysis | |
Discrete mathematics | ||
Marketing Strategies based on Data Sets | ||
Professional English 2 for the ICT field* |
*Professional English 2 for ICT will start in the academic year 2025/2026.
- Enrolment conditions
- Conditions for advancing
- Conditions for the completion
- Part-time study
- 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.
Linear Algebra and Optimization
LINK TO the course syllabus until the academic year 2023/2024
LINK TO the course syllabus from academic year 2024/2025
LECTURER: izr. prof. dr. Vesna Andova
ECTS: 6
Selected topics in probability and statistics
LINK TO the course syllabus until the academic year 2023/2024
LINK TO the course syllabus from academic year 2024/2025
LECTURERS: doc. dr. Nuša Erman
ECTS: 6
Introduction to Data Science
LINK TO the course syllabus
LECTURER: izr. prof. dr. Zoran Levnajić, viš. pred. dr. Albert Zorko
ECTS: 6
Data Science Programming
Data visualization
LINK TO the course syllabus until the academic year 2023/2024
LINK TO the course syllabus from academic year 2024/2025
LECTURER: doc. dr. Nuša Erman
ECTS: 5
Data mining
Categorical data analysis
LINK TO the course syllabus until the academic year 2023/2024
LINK TO the course syllabus from academic year 2024/2025
LECTURER: prof. dr. Janez Povh, asist. Lorena Mihelač
ECTS: 5
Machine learning 1
LINK TO the course syllabus
LECTURER: Prof. Ph.D. Biljana Mileva Boshkoska, Asst. prof. Ph.D. Andrej Furlan
LECTURER (par-time, in English language): Prof. Ph.D. Biljana Mileva Boshkoska, Tch. Asst. Viktor Androvikj
ECTS: 5
Elective course 1
ECTS: 5
Elective course 2
ECTS: 5
Elective course 3
ECTS: 5
Data warehouses and data analytics
Big data analysis
LINK TO the course syllabus until the academic year 2023/2024
LINK TO the course syllabus from academic year 2024/2025
LECTURERS: prof. dr. Srđan Škrbić
ECTS: 5
Machine learning 2
LINK TO the course syllabus until the academic year 2023/2024
LINK TO the course syllabus from academic year 2024/2025
LECTURER: Asist. Prof. Ph.D. Aleksandra Rashkovska Koceva, Tch. Asst. Ana Nikolikj
ECTS: 5
Statistical learning and modeling
LINK TO the course syllabus until the academic year 2023/2024
LINK TO the course syllabus from academic year 2024/2025
LECTURER: doc. dr. Nuša Erman
ECTS: 7
Thesis Seminar
LINK TO the course syllabus until the academic year 2023/2024
LINK TO the course syllabus from academic year 2024/2025
LECTURER: Prof. Ph.D. Tea Golob, Asst. Teja Štempfel
ECTS: 8
High Performance Computing
LINK TO the course syllabus until the academic year 2023/2024
LINK TO the course syllabus from academic year 2024/2025
LECTURER: prof. dr. Srđan Škrbić
ECTS: 5
Creativity and Critical Thinking
LINK TO the course syllabus until the academic year 2023/2024
LINK TO the course syllabus from academic year 2024/2025
LINK TO the course syllabus from academic year 2025/2026
LECTURER: izr. prof. dr. Katarina Rojko
ECTS: 3
Master’s Thesis
ECTS: 22
Analysis of complex networks
LINK TO the course syllabus
ECTS: 5
Ethics and Entrepreneurial Communication
LINK TO the course syllabus
ECTS: 5
Selected Topics in Algorithms
LINK TO the course syllabus
ECTS: 5
Marketing Communication
LINK TO the course syllabus
ECTS: 5
Modeling complex systems
LINK TO the course syllabus
ECTS: 5
Knowledge Management
LINK TO the course syllabus
ECTS: 5
Numerical mathematics
LINK TO the course syllabus
ECTS: 5
Organizational psychology
LINK TO the course syllabus
ECTS: 5
Internet Research
LINK TO the course syllabus
ECTS: 5
The introduction to the business, employment, contract and tort law
LINK TO the course syllabus
ECTS: 5
Fuzzy-Set Analysis
LINK TO the course syllabus
ECTS: 5
Discrete mathematics
LINK TO the course syllabus
ECTS: 5
Marketing Strategies based on Data Sets
LINK TO the course syllabus
ECTS: 5
Professional English 2 for the ICT field
LINK TO the course syllabus from academic year 2024/2025
ECTS: 5