Advanced data analytics

University of Belgrade

Level:  master academic studies
Upon end of the studies student will receive a title: master of data analysis
The program belongs to the broad study area of ​​quantitative sciences (mathematics, statistics, computer science, informatics, or a combination of some of these areas), but with a focus on the integration of these areas in the analysis of different types of data and large amounts of data.


Goals of study program

The objectives of the Master's degree program in Advanced Data Analysis at the University of Belgrade are: to improve theoretical and practical knowledge in quantitative disciplines (primarily mathematics and statistics) needed by students who want to analyze data in solving practical problems; to deepen knowledge in the field of data analytics and quantitative methods, as well as master the necessary programming and database skills as prerequisites for working on practical problems involving data analytics;enabling students to master a range of practical software tools related to programming, data analysis, data visualization, etc., and use them in working on practical problems; providing practical experience in applying the above skills and tools to work on practical, real-world problems, both in individual engagement and in teamwork, by involving students in ongoing and new practical projects;providing a basis for work on research activities, as well as for further education through relevant doctoral studies.


Outcome of study program

Students who complete the Master's program in Advanced Data Analysis at the University of Belgrade become competent in: independent work in the analysis of data sets of varying complexity in selected domains, with advanced use of existing data analysis tools and technologies, preparation, modification, adaptation and combination of data sets for analysis, from raw data obtained from various applications and other sources,involvement in various interdisciplinary work teams where data analysis skills from different disciplines are expected and mastery of existing tools and technologies for data analysis, not only in solving routine practical problems, but also in non-standard situations where creativity and an investigative approach are required, working with large data sets


Prerequisite for attending study program

Candidates who have completed four-year basic academic studies (240 ECTS credits), or four-year basic studies according to the regulations that were in force before the entry into force of the Law on Higher Education, may enroll in master's academic studies. A person who has completed integrated studies, or master's academic studies, having achieved at least 300 ECTS credits, may also enroll in master's academic studies. It is necessary that these candidates have achieved a minimum of 15 ECTS credits in the field of mathematics, statistics and programming in their basic academic studies, or that they have passed at least one subject in the field of mathematics, statistics and programming in their basic studies.Knowledge of English is required for all candidates, regardless of whether they enroll in a program in Serbian or English.
Modules on this study program:
Name Acronym Semester Module info
ADAE -
Subjects lectured on this study program:
Acronym Name ECTS Semester Module Type External URL
19.ADA03 Analytics and оptimization 10.0 1 ADAE elective
19.ADA02 Discrete structures 10.0 1 ADAE elective
19.ADA06 Introduction to complex networks theory 10.0 1 ADAE elective
19.ADA04 Introduction to statistical inference 10.0 1 ADAE elective
19.ADA01 Mathematical foundations of data analysis 10.0 1 ADAE elective
19.ADA05 Models of statistical learning 10.0 1 ADAE elective
19.ADA13 10.0 2 ADAE elective
19.ADA10 10.0 2 ADAE elective
19.ADA15 10.0 2 ADAE elective
19.ADA11 10.0 2 ADAE elective
19.ADA14 10.0 2 ADAE elective
19.ADA07 Artificial intelligence / machine learning 10.0 2 ADAE elective
19.ADA09 Databases 10.0 2 ADAE elective
19.ADA12 Neural networks and deep learning 10.0 2 ADAE elective
19.ADA08 Programming 10.0 2 ADAE elective
19.ADA20 6.0 3 ADAE elective
19.ADA21 6.0 3 ADAE elective
19.ADA23 6.0 3 ADAE elective
19.ADA16 6.0 3 ADAE elective
19.ADA18 6.0 3 ADAE elective
19.ADA19 6.0 3 ADAE elective
19.ADA17 Data analysis in biological sciences 6.0 3 ADAE elective
19.ADA22 Practical analysis of noisy and inhomogeneous time series 6.0 3 ADAE elective
PRIR Access work 6.0 ADAE obligative
MPPSP Professional practice 4.0 ADAE obligative