MASTER'S STUDY PROGRAMME

Data Sciences

  • Study programme in the study year 2021/22 will be carried out mostly* via Zoom according to the existing schedule.
  • Possibility of enrolment: full-time or part-time ** – full-time study is free of charge for employed students as well.***

* In the event of worsening of the epidemiological situation, ALL lectures and tutorials will again be conducted via Zoom according to the existing schedule.
** The annual tuition fee of part-time study is 2,500 euros and is payable in 4 instalments.
***For EU citizens and citizens of the countries with signed bilateral agreement with Slovenia.

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 science. 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 following individuals can be enrolled in the first year of the master’s study programme Data Science:

  • Who has completed a bachelor’s study programme in the fields of mathematics, statistics or computer science. 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 Science. 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 (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.

Curriculum

1st year2nd yearElective courses
Linear Algebra and OptimizationData warehouses and data analyticsAnalysis of complex networks
Selected topics in probability and statisticsBig data analysisEthics and Entrepreneurial Communication
Introduction to Data ScienceMachine learning 2Selected Topics in Algorithms
Data Science ProgrammingStatistical learning and modelingMarketing Communication
Data visualizationThesis SeminarModeling complex systems
Data miningHigh Performance ComputingKnowledge Management
Categorical data analysisCreativity and Critical ThinkingNumerical mathematics
Machine learning 1Master's thesisOrganizational psychology
Elective course 1Internet Research
Elective course 2The introduction to the business, employment, contract and tort law
Elective course 3Fuzzy-Set Analysis
Discrete mathematics
Marketing Strategies based on Data Sets

Linear Algebra and Optimization

LINK TO the course syllabus

LECTURER:  izr. prof. dr. Vesna Andova

ECTS: 6

Selected topics in probability and statistics

Introduction to Data Science

Data Science Programming

Data visualization

Categorical data analysis

Machine learning 1

Elective course 1

ECTS: 5

Elective course 2

ECTS: 5

Elective course 3

ECTS: 5

Data warehouses and data analytics

Machine learning 2

Statistical learning and modeling

Thesis Seminar

High Performance Computing

Creativity and Critical Thinking

LINK TO the course syllabus

LECTURER: doc. dr. Robert Kopal

ECTS: 3

Master’s Thesis

ECTS: 22

Analysis of complex networks

Ethics and Entrepreneurial Communication

Marketing Communication

Modeling complex systems

Knowledge Management

LINK TO the course syllabus

LECTURER:  prof. dr. Nadja Damij

ECTS: 5

Numerical mathematics

LINK TO the course syllabus

LECTURER:  izr. prof. dr. Vesna Andova

ECTS: 5

Organizational psychology

LINK TO the course syllabus

LECTURERS:  izr. prof. dr. Nevenka Podgornik, doc. dr. Jana Krivec

ECTS: 5

Internet Research

The introduction to the business, employment, contract and tort law

LINK TO the course syllabus

LECTURER: izr. prof. dr. Marko Novak

ECTS: 5

Fuzzy-Set Analysis

Marketing Strategies based on Data Sets

LINK TO the course syllabus

LECTURER: doc. dr. Goran Klepac

ECTS: 5