We inform prospective students that the academic studies master program “Applied mathematics – Data science” has been continued in the new accreditation period starting from the 2023/2024 academic year. The purpose of the study program, its goals, and its graduates’ competences remain similar as in the former accreditation period. The main novelty is that the program has become a single module program, i.e., all students follow the same curriculum, according to a unique module. The structure of obligatory and elective courses stays practically unchanged, while the elective courses that were offered within the specific modules now jointly become available to all students through the unique module.
Name and the Goals of the Study Programme
The goal of the study programme Applied Mathematics: Data Science is to produce data scientists-applied mathematicians, who will work in various sectors, including industry (emphasis on ICT), agriculture, medicine, economy and finance.
Type of the Study and the Outcome of the Education Process
Applied Mathematics: Data Science belongs to the group of master academic studies with the duration of two years (four semesters). The total ECTS credits is 120.
It enables students to acquire sound mathematical skills, learn how to apply them on practical problems, and how to model and analyze phenomena from various practical fields. Students acquire skills needed to analyze data, extract meaning, develop models, use relevant algorithms and add value to a relevant entity, in various sectors. Students will:
- Adopt main concepts and principles in their relevant disciplines
- Gain the ability to effectively communicate and exchange ideas with engineers and other professionals within companies from the sectors relevant to the chosen discipline
- Gain a significant expertise in programming and software in modern data science tools
Professional Title, Academic, or Scientific Title
After completing the two year program and collecting 120 ECTS credits, students obtain the title Master in mathematics.
Admission Conditions
Admission is performed based on the call issued by the University of Novi Sad, and conducted by the Faculty of Sciences, Novi Sad. The enrollment condition is completed undergraduate academic studies in mathematics, computer science or engineering. The candidates ordering is based upon the average grade and duration of the undergraduate academic studies.
The Structure of the Study Programme
Master of Science in Applied Mathematics: Data Science contains obligatory courses, elective modules, elective courses, and final (master) thesis. There are 9 obligatory courses distributed over three semesters. There are 21 elective courses, distributed over three semesters.
The Time Allotted for the Realization of Particular Study Forms
Master of Science in Applied Mathematics: Data Science lasts for four semesters (two years), during which a student collects 120 ECTS credits. The first three semesters are dedicated to courses only. The forth semester is fully dedicated to the preparation and defense of master thesis.
Credit Values of Particular Courses
Each course is assigned an ECTS credit value according to the projected amount of work necessary for the student to complete the course.
Diploma Work
The final (master) thesis is a student’s individual work and represents a final test – exam before obtaining the title Master in mathematics. It is expected to be a non-trivial application of the learned concepts and tools of data science for a chosen application. The thesis can also be a theoretical contribution in a field relevant to data science. The corresponding credit is 30 ECTS.
Prerequisites for the Registration for Particular Courses or Group of Courses
For each particular course, the prerequisites are stated in the corresponding course description document.
Way of Choosing Courses from the Other Study Programmes
Due to its interdisciplinary profile, the program contains a number of courses from different programs of study relevant to data science, both within the group of obligatory courses and the group of elective courses.
Transferring from Another Study Programme
There is a possibility for students to transfer among different programs of study, where it is possible to carry the ECTS credits for the same or similar courses.
The purpose of the program Applied Mathematics: Data Science is education of professionals in data science – applied mathematics, with the academic degree Master in mathematics.
Students will be qualified to work in a very wide range of application domains, including business, finance, agriculture, medicine and industry.
The overall objective of the program is to provide students with competitive, up to date knowledge and tools in data science. The backbone of the fundamental knowledge will be acquired through 9 obligatory courses. These courses cover the needed knowledge and skills in several data science related disciplines, including optimization and machine learning, graph theory and networks, and software/programming.
Students will be qualified to work in organizations where knowledge is extracted from data, leading to added value to the organization. These students will be able to achieve this through the skills in machine learning, optimization, and algorithms that they will acquire. Sectors of potential employment include telecom, power systems, business, finance, agriculture/agrifood, medicine, etc.
Students will also be qualified to work in companies which deal with cloud computing and, more generally, in companies which develop technologies for managing big data. This includes, e.g., telecom sector, internet providers, and more broadly, any company which requires management of large volumes of data, including, e.g., power systems, business, finance, medicine, biology/genetics, etc.
General and Course-specific Competencies of Students
By completing this program of study, students acquire the following general and data science-specific skills:
- The ability of analytical thinking, modeling and formulating a real-world problem in a formal, mathematical way
- Obtain knowledge of fundamentals and current trends in data science
- Ability to apply the taught mathematical and discipline-specific tools to solve real-world problems
- Familiarity and understanding of main concepts and principles of the chosen discipline
- Effectively communicate with engineers and other professionals in the relevant field
- Ability to effectively use the up to date software tools relevant to data science
- Ability for creative thinking, combining the taught concepts and skills to create innovative real-world solutions
The Outcomes
Upon successful completion of this program of study, students will have a sound overview of concepts and acquire advanced knowledge in the key disciplines of data science. Moreover, students will be able to apply the gained skills and knowledge in data science and combine them in a non-trivial way to solve practical, real-world problems. Given a specific real-world problem, students will be able to adequately model the problem, and efficiently solve it by selecting and applying the most appropriate tool from the pool of taught methods.
There are 9 obligatory courses – 52 ECTS in total, 4 in the first semester (24 ECTS, 6 ECTS each course), 3 in second (18 ECTS, 6 each), and 2 in third (10 ECTS, 5 each). The fourth semester is dedicated to master thesis – 27 ECTS and praxis – 3 ECTS. In the first semester, students choose one elective course (6 ECTS) out of 2 offered (First elective courses group). In the second semester, students choose 2 elective courses (12 ECTS, 6 each), out of 6 offered (Second elective courses group). In the third semester, students choose 4 elective courses (20 ECTS, 5 each) out of 11 offered, each carrying 5 ECTS. Hence, overall, 52 ECTS are gained through obligatory courses, 38 ECTS are for elective courses, 27 ECTS are for master thesis, and 3 ECTS are for Praxis.
A Distribution of the Courses into Semesters and Academic Years – Obligatory courses
| No. | Course code | Course title | Semester | Active teaching hours | ECTS | |
| L | E | |||||
| FIRST YEAR | ||||||
| 1 | 19.MA0001 | Programming for Data Science | 1 | 3 | 2 | 6 |
| 2 | 19.MA0003 | Stochastic Processes | 1 | 3 | 2 | 6 |
| 3 | 19.MA0004 | Numerical Linear Algebra 1 | 1 | 3 | 2 | 6 |
| 4 | 19.MA0005 | Fundamentals of Numerical Optimization | 1 | 3 | 2 | 6 |
| 5 | 19.MA0002 | Graph Theory | 2 | 3 | 2 | 6 |
| 6 | 19.MA0006 | Pattern Recognition and Machine Learning | 2 | 3 | 2 | 6 |
| 7 | 19.MA0007 | Distributed Optimization with Applications | 2 | 3 | 2 | 6 |
| Active teaching in total (obligatory courses) | Fall: 8 Spr.: 6 | Fall: 12 Spr.: 9 | ||||
| Total ECTS | 42 | |||||
| SECOND YEAR | ||||||
| 8 | 19.MA0008 | Network Science | 3 | 2 | 2 | 5 |
| 9 | 19.MA0010 | Large Scale Data Mining | 3 | 2 | 2 | 5 |
| 10 | 19.MA00011a | Master thesis research | 4 | 20 (SRW*) 0+0+20+0+0 | 7 | |
| 11 | 19.MA00011b | Master thesis | 4 | 60 (OFT**) 0+0+0+0+4 | 20 | |
| 12 | 19.MA00011c | Praxis | 4 | 90 (OFT**) 0+0+0+0+6 | 3 | |
| Active teaching in total (obligatory courses) | Fall: 4 Spr.: 0 | Fall: 4 Spr.: 0 | ||||
| Total ECTS | 40 |
- L – Lecture
- E – Exercise
- *SRW – Study Research Work
- **OFT – Other Forms of Teaching
Elective subjects of the Study Program
| No. | Course code | Course title | Semester | Active teaching hours | ECTS | |
| L | E | |||||
| FIRST YEAR | ||||||
| 1 | 19.MA0016 | Signals and Systems | 1 | 2 | 3 | 6 |
| 2 | 19.MA0017 | Modeling Seminar | 1 | 2 | 3 | 6 |
| 3 | 19.MA0018 | Time series | 2 | 2 | 3 | 6 |
| 4 | 19.MA0018 | Numerical Analysis | 2 | 2 | 3 | 6 |
| 5 | 19.MA0020 | Numerical Linear Algebra 2 | 2 | 2 | 3 | 6 |
| 6 | 19.MA0021 | Introduction to Digital Signal Processing | 2 | 2 | 3 | 6 |
| 7 | 19.MA0022 | Statistics Theory for Learning and Signal Processing | 2 | 2 | 3 | 6 |
| 8 | 19.MA0023 | Theory of Information and Networks | 2 | 2 | 3 | 6 |
| SECOND YEAR | ||||||
| 9 | 19.MA0024 | Communication and Storage Networks for Big Data | 3 | 2 | 2 | 5 |
| 10 | 19.MA1003 | Introduction to Image Processing | 3 | 2 | 2 | 5 |
| 11 | 19.MA0025 | Theory of Algorithms | 3 | 2 | 2 | 5 |
| 12 | 19.MA1004 | Big Data in Medicine and Biology | 3 | 2 | 2 | 5 |
| 13 | 19.MA1005 | Acoustics and Speech Processing | 3 | 2 | 2 | 5 |
| 14 | 19.MA1006 | Graphical Models and Probabilistic Inference | 3 | 2 | 2 | 5 |
| 15 | 19.MA0027 | Applied data analysis for decision support systems | 3 | 2 | 2 | 5 |
| 16 | 19.MA2003 | Databases | 3 | 2 | 2 | 5 |
| 17 | 19.MA2003 | High Performance Computing | 3 | 2 | 2 | 5 |
| 18 | 19.MA2006 | Advanced Programming for Mathematicians | 3 | 2 | 2 | 5 |
| 19 | 19.MA2007 | Data Structures and Algorithms | 3 | 2 | 2 | 5 |
| Total ECTS | 103 |
- L – Lecture
- E – Exercise