Name of the programme: Data Science
Type of programme: Master's, 2nd Bologna cycle
Degree awarded: "magister podatkovnih znanosti" equiv. to Master's degree in Data Science
Duration: 2 years (4 semesters)
Programme structure: 15 courses (84 ECTS), 2 seminars (6 ECTS), practical training (6 ECTS), Master's Thesis (24 ECTS)
Mode of study: full-time
Language of study: Slovene, English
Assist. Vida Groznik, PhD, Deputy Coordinator
For information regarding application, enrolment and other administrative procedures please contact Student Services.
About the programmetop
Data science is an interdisciplinary field that leverages knowledge in three key areas to provide useful information for business, research, and policy. The key areas of knowledge data science relies on are computer science, mathematics, and domain knowledge -- that is an understanding of the field the data is collected from and used in. As the quantity and types of data collected has increased in recent years, business, researchers, and policy makers have increasing need for Data Scientists to turn data into information. In our data science programme you will learn to gather, manage, and analyse structured and unstructured data including numbers, text, images, video, audio, etc. Students will also learn important statistical and algorithmic methods to work data, to extract information, and to create useful outputs based on this information.
Our new study programme is set in accordance with the guidelines of the European Mathematical Association (EMS). The basic objectives of the study program are based on in-depth theoretical knowledge, access to domain experts in various fields, and industry internships, through which graduates will be capable of using current techniques as well as develop new methods of processing big data to “bring data to life” and “tell stories with it”. The content of the study programme is based on the state of the art and current trends in data science and all underlying disciplines including ethical issues and regulations (e.g. GDPR) into account.
Educational and professional goalstop
Students will gain and consolidate an in-depth knowledge of special areas in the field of mathematics, statistics and theoretical computer science, which are the basis for the ability to solve real problems in the field of big data.
Students will be trained for deep understanding of data science.
Students will develop the ability of analytical thinking, proofing and argumentation in various fields of data science.
Students will develop the ability to analyse given data and select appropriate methods/techniques for collecting data in order to obtain new results.
Students will be trained for team work.
Students will be trained for the use of modern technological tools (programming languages) in solving and presenting problems and concepts of data science.
Students will be trained for collecting and managing data based on ethical principles.
During their studies, students must take a total of 15 courses (12 compulsory and 3 electives), 2 seminars, and practical training (3 weeks). They must also prepare and defend their Master’s Thesis.
The tables below outline the structure of the study programme and its compulsory and internal elective courses.
|Year of study||Study obligation||Number||ECTS-credits (ECTS)|
|ECTS||ECTS/Year of study|
|External Elective Course||1||6|
|External Elective Course||2||12|
|Practical Training (3 weeks)||1||6|
|No.||Course||ECTS||Form of contact hour|
|1.||Data Engineering and Distributed Information Systems||6||30||-||-||30||60|
|2.||Data Science Ethics||3||30||-||-||-||30|
|4.||Selected Topics in Discrete Mathematics||6||30||30||-||-||60|
|6.||Selected Topics in Information Visualisation||6||45||-||15||60|
|8.||Data Practicum I||3||-||-||-||30||30|
|9.||Databases for Big Data||6||30||15||-||15||60|
|10.||Data Science Seminar I||3||-||15||-||-||15|
|11.||Data Science Seminar II||3||-||15||-||-||15|
|12.||External Elective Course I||6|
L = lecture, SE = seminar, T = tutorial, LW = laboratory work
ECTS = ECTS-credits
|No.||Course||ECTS||Form of contact hour|
|1.||Selected Topics in Numerical Mathematics||6||30||-||30||-||60|
|2.||Mining Massive Data||6||15||15||-||30||60|
|3.||Data Practicum II||6||-||-||-||60||60|
|4.||External Elective Course II||6|
|5.||External Elective Course III||6|
|6.||Practical Training (3 weeks)||6|
Internal Elective Courses
The list shows all internal elective courses of the study programme. Every academic year, the Faculty offers a different (shorter) selection of elective courses.
Internal elective courses of the study programme Data Science:
Collection and Integration of Sensor Data: 3 ECTS, 15 hours of lectures and 15 hours of laboratory work;
Security: 6 ECTS, 3 ECTS, 15 hours of lectures and 15 hours of laboratory work.
Students can also choose the courses Graph Algorithms and Computational Social Science (from the Master's study programme Computer Science) as an internal elective course.
During their studies, students are required to take a total of 3 external elective courses (1 in the first year and 2 in the second year of study).
Students choose their elective courses from those offered by the programme. The list of internal elective courses is outlined in the Course Structure section (Table 4).
Every academic year, the Faculty offers a different selection of elective courses from the internal elective courses listed. The Faculty tries to meet student interests within the limits of its resources. The final selection of elective courses for the next academic year is published in July (before the enrolment in the next academic year).
Students can choose external elective courses from those offered by other accredited higher education institutions in Slovenia or abroad, on condition that they are from the fields of mathematics, computer science, bioinformatics, business informatics, management and communication.
Students that haven't passed a course in the field of probability, have to choose one external elective course in that field.
While choosing external elective courses, students are recommended to consult the study programme coordinator and then choose courses of at least 6 ECTS-credits related to social sciences, in the field of communication, critical thinking, organization or management, offered by the faculties of the University of Primorska. In case students choose an external subject outside the University of Primorska, the syllabus of the course must be obtained and the course shall be approved (or rejected) by the coordinator of the study programme. As external elective course, students can also choose a distance learning course. Students are recommended to take three external courses of 6 ECTS-credits each, or four courses – two of which are rewarded with 3 ECTS-credits and two with 6 ECTS-credits.
Study programme coordinator will help students in the process of choosing elective courses.
In their 2nd year of study, students must undertake a 3-weeks-long practical training. The purpose of the training is to transfer theoretical knowledge into practice.
Students should contact the preferred organization and arrange the practical training themselves. The list of organizations (that already accepted students for practical training) is available from the coordinator of the practical training.
Student must submit the application for practical training in the Student Information System (SIS) before starting the practical training. The application must be approved by the coordinator of the practical training at the Faculty. After the approval, the Student traineeship cooperation agreement must be signed from all three parties: student, UP FAMNIT and the receiving institution. Student can start the practical training only after the approval of the request/application and the signature of the agreement. Students will be assigned a mentor in the organization who is responsible for the supervision and guidance of their work.
Student must submit a report in SIS at the end of the training.
All forms are available in SIS - application, student traineeship cooperation agreement, report, certificate of the completion (only in Slovene).
Admission to the first year of study shall be granted to applicants having:
completed a first-cycle study programme in the field of mathematics, computer science or bioinformatics; or
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.
Continuation of studies according to the transfer criteriatop
Transfers between study programmes are possible on the basis of the Higher Education Act, Criteria for Transferring between Study Programmes and in accordance with other regulations of this field.
The transition between study programmes is the enrolment in the higher year of the study programme, in case of leaving the education at the initial study programme and continuing the study process at another study program of the same degree. The transition takes into account the comparability of the study programmes and the completed study obligations of the candidate in the initial study program.
Access to Year 2 of the Master’s programme of Data Science on the basis of the Criteria for Transferring between Study Programmes is granted to candidates, provided that the following conditions have been met:
the candidate fulfils the requirements for admission to the study programme of Data Science;
completion of the first study programme which the candidate is transferring from ensures the acquisition of comparable competencies as those envisaged by the study programme of Data Science; and
other conditions in accordance with the Criteria for Transferring between Study Programmes have also been met (a comparable course structure, course requirements completed).
Individual applications for transfer shall be considered by the relevant committee of UP FAMNIT. Apart from comparability between both fields of study, the committee shall also consider comparability between the study programmes, in accordance with the Criteria for Transferring between Study Programmes. The applicant may also be required to complete differential exams as defined by the relevant Faculty committee.
Enrolment on the basis of the Criteria for Transferring between Study Programmes is also open to candidates of a related study programme abroad who have been, in the process of recognition of their studies abroad, legally granted the right to continue their educational training in the study programme of Data Science.
In the case of limited enrolment, applicants shall be selected on the basis of the average grade obtained during the study programme they are transferring from.
Progression to the second year of study is granted to students who have collected at least 42 ECTS-credits in the first year of study.
Under exceptional circumstances, exemption from this requirement is also granted to students who have justifiable reasons for not having obtained the required 42 ECTS-credits, eg. parenthood, extended illness, extraordinary family and social circumstances, status of student with special needs, active participation in top scientific, cultural and sporting events, active participation in university bodies, etc. In such cases, entry into the second year of study shall be decided upon by the relevant committee of UP FAMNIT (the student is obliged to submit a formal written request to the committee).
Students who have not completed all the requirements (as defined by the study programme) for advancement to the next year of study may repeat a year only once during their studies, in accordance with the Higher Education Act. A minimum of 18 ECTS-credits in courses from the current year of study are required in order to repeat the year.
Student status, along with the corresponding rights and benefits provided by law and the Statute of UP, is retained by advancing to the next year of study and also by repeating a year. In accordance with the law and the Statute of UP, students can apply for an extension of student status for a maximum of one year.
To complete their studies, students are required to fulfill all their academic obligations as defined by individual course curricula and the study programme.
Students are required to write a master's thesis under the guidance of an advisor (mentor), usually selected in the second year of study. The master's thesis must be completed in accordance with the applicable guidelines for the preparation of the master’s thesis. A student can submit the application for the Master's thesis topic proposal after completion of all compulsory courses.
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.
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.
Graduate employment opportunitiestop
Data science practitioners are in high demand in the world where 2.5 quintillion bytes of data are produced every day. Data scientists have a passion of understanding data and can turn seemingly meaningless data into a recommendations to support businesses understand their customers, improve the way customer data is used, improve products, services or experiences, and help businesses grow.
These days, start-ups and well-established companies, along with researchers and policy makers, increasingly rely on data to drive and support decision making. As a consequence data scientists are valued in various employment environments such as research institutions, IT companies, banks, insurance companies, transport organizations, and other businesses which produce data in increasing quantities.
Experts predict that by 2020 there will be 40 zettabytes of data. As such, career opportunities for data scientists will expand. In addition to data scientist, some of the important job titles of data scientists are data architect, data manager, data analyst, business analyst, business intelligence manager, etc.