Univerza na Primorskem Fakulteta za matematiko, naravoslovje in informacijske tehnologije
Presentation of the study
The master’s study programme Data Science is conducted in Slovenian and English language.
Data science is an interdisciplinary field that combines computer science, mathematics, and domain knowledge with the aim of extracting useful insights from large volumes of data for industry, research, and public policy.
Students acquire in-depth theoretical knowledge and practical experience in the collection, management, and analysis of structured and unstructured data, such as text, images, audio, and video. The programme places emphasis on statistical and algorithmic methods, analytical thinking, reasoning, teamwork, and the use of modern programming tools.
The study programme prepares students for independent data analysis, the development of efficient solutions, and the use of modern tools in solving real-world problems. It emphasises teamwork, critical thinking, and ethical handling of data, and enables collaboration with industry and research institutions, providing students with practical experience and preparation for a career in data science both domestically and internationally.
Programme information
Programme name: Data Science
Type of programme: master’s study programme, 2nd cycle
Degree awarded: “magister podatkovnih znanosti” equiv. to Master’s degree in Data Science
Duration: 2 years (4 semesters)
ECTS-credits: 120 ECTS
Mode of study: full-time
Language of instruction: Slovene, English
Place of study: Koper
Accreditation: the programme is accredited in accordance with the Higher Education Act and is officially recognised.
From the academic year 2026/2027, students of the master’s study programme Data Science may, based on the agreement between the University of Primorska and the University of Trieste, graduate with two degrees: Data Science at UP FAMNIT and Laurea Magistrale in Data Science and Artificial Intelligence at the University of Trieste.
Participating master’s students must complete part of their study obligations (at least one semester) at the University of Trieste, in accordance with their individual learning agreement, which will be prepared and signed after their acceptance into the programme. The partner institutions in the double degree programme fully recognize the ECTS credits and learning outcomes achieved at the partner institution.
Students who successfully defend their thesis and obtain the required ECTS credits will be awarded diplomas from both partner institutions.
Both universities will announce a Call for Applications for the double degree programme every academic year. The call will be open to students already enrolled in the relevant study programme and will outline the selection process, application deadline, list of courses offered by both universities, and other important information.
Admission requirements for 1st year
a) completed a first-cycle study programme in the field of mathematics, computer science or bioinformatics; or
b) completed a first-cycle study programme in other professional fields that are not included in paragraph 1. The candidate has to pass study obligations that are fundamental for enrolment, in an amount of from 10 do 60 ECTS-credits (from the first-cycle study programmes Mathematics and Computer Science). The applicants may fulfil these obligations during their first-cycle studies, under training programmes, or by taking examinations prior to enrolment in the study programme Data Science. Upon examination of the candidate’s previous study programme, the competent academic committee of UP FAMNIT shall define each candidate’s additional academic requirements on a case-by-case basis.
Admission may also be gained by an applicant having completed a comparable study abroad and who has been, in the process of recognition of their qualification and in line with the Recognition and Evaluation of Education Act, granted the right to continue their studies in the Master’s degree programme in Data Science.
In the case of enrolment limitations, applicants shall be selected on the basis of the average grade obtained in their undergraduate studies.
Candidates who are required to complete additional requirements (bridging exams) for admission to the master’s study programme attend courses within the first-cycle study programmes Mathematics and Computer Science at UP FAMNIT. The course schedule is tailored to first-cycle students; therefore, in case of scheduling conflicts, these candidates must arrange individual agreements with the respective course instructors.
“Transfer between study programmes” refers to a situation in which a student enrolled in a particular study programme does not complete it (i.e. discontinues education in the enrolled programme) and directly enrols into a higher year of a new study programme, whereby both the previous and the new programme must belong to the same Bologna cycle (level). When considering the possibility of transferring to a new study programme, the comparability of the programmes and the student’s completed study requirements in the previous programme are taken into account.
Admission to the 2nd year of the master’s study programme in Data Science under the transfer criteria is possible if the following conditions are met:
- the candidate meets the admission requirements for the master’s study programme in Data Science,
- the completion of the initial study programme which the candidate is transferring from ensures the acquisition of comparable competences to those of the master’s study programme in Data Science, and
- other criteria in accordance with the Criteria for Transfers between Study Programmes are met (comparable curriculum of the study programme and completed study requirements of the candidate).
Individual applications for transfer shall be considered by the Committee for Study and Student Affairs of UP FAMNIT. Apart from comparability between both fields of study, the committee shall also consider the comparability between the study programmes, in accordance with the Criteria for Transferring between Study Programmes. The Committee may also assign bridging exams to the candidate.
A candidate transferring from a related study programme abroad may also be admitted under the transfer criteria, provided that, in accordance with the law, they have been granted the right to continue their studies in the master’s study programme in Data Science through the recognition procedure of foreign education.
In the case of limited enrolment, candidates are selected based on the average grade of all completed study requirements in the study programme from which the candidate is transferring.
A student may progress to the next year if they accumulate at least 42 ECTS credits from the enrolled year.
In special cases involving individual circumstances (such as illness or extraordinary situations), a student may be allowed to progress to the next year even with a lower number of ECTS credits. In such cases, the decision on enrollment is made by the Committee for Study and Student Affairs of UP FAMNIT.
A student who has not completed all the requirements specified by the study programme for progression to the next year may, during the course of their studies, repeat a year once, provided they have obtained at least 18 ECTS credits in the enrolled year. If a student repeats a year, they are not entitled to absolvent year, and their student status expires at the end of the 2nd year.
By progressing or repeating a year, a student retains student status and, consequently, the rights and benefits defined by law. In accordance with the law, a student may apply for an extension of student status, but for no more than one year.
During the course of their studies, students complete three external elective courses: one in the 1st year and two in the 2nd year.
External elective courses may be chosen from accredited study programmes in Slovenia or abroad, covering fields such as mathematics, computer science, informatics, bioinformatics, business informatics, management, and communication, subject to prior approval by the coordinator (for courses taken outside UP).
A student who has not completed a course in probability at the undergraduate level must take one external elective course in this field.
More information on elective courses and study tracks is available in the document “Curriculum” (see above).
In the 2nd year, students are required to complete compulsory practical training in a work environment. The training lasts 3 weeks and is worth 6 ECTS.
The purpose of the practical training is to build on theoretical knowledge, acquire practical competences, develop problem-solving skills in the field of data science, and connect theoretical knowledge with business processes.
Each student is assigned a mentor within the selected organisation where the training takes place. The mentor is responsible for supervising and guiding the student’s work. In addition, the student’s work is also monitored by the practical training coordinator within the study programme.
General competencies
- in-depth theoretical, methodological, and analytical knowledge with elements of research in high demand for professional work in the field of data science;
- ability to analyze, synthesize, and predict solutions and consequences in data science;
- critical assessment of the developments in the field of data science;
- development of communication skills;
- ability to cooperate, work in a group, and work on projects;
- ability to autonomously seek and acquire expertise and to integrate it with existing knowledge;
- ability to search for new information and their interpretation and positioning in the context of data science;
- autonomy in professional work;
- accepting responsibility for decisions related to activities, processes, and the management of complex and heterogeneous groups.
Subject-specific competencies
- ability to describe a given situation using the proper use of data science concepts;
- ability to explain understanding of concepts and principles of data science;
- resolve (real) problems in data science using modern technology;
- apply an algorithmic approach: Develop an algorithm to solve a given problem;
- develop the ability to analyze a given problem numerically, graphically, and algorithmically;
- being able to derive new logical conclusions from the given data;
- ability to effectively present results with data visualization tools, including knowledge of techniques
- for analyzing statistics, software tools for data processing and data transformation;
- confidently confront a given problem in data science and find its solution, taking into account ethical principles in the context of mass data.
Professionals in the field of data science are in high demand. These experts have a passion for understanding data and can transform seemingly meaningless data into actionable insights that support companies and policymakers, improve user understanding, enhance the use of customer data, improve products, services, or user experiences, and help organisations grow.
Today, both start-ups and established companies, as well as researchers and policymakers, increasingly rely on data-driven insights to support decision-making. As a result, data science professionals are essential in a wide range of working environments, such as research institutions, IT companies, banks, insurance companies, transport organisations, and other organisations that collect large volumes of data.
Career opportunities are expected to continue to grow. In addition to the title of data scientist, other job titles used in the labour market include data architect, data manager, data analyst, business analyst, and business intelligence manager, among others.