Computing - Data Science

The Computing programme is focused on the computer as a universal information processing machine, and methods of its application in various domains.

The computing field includes theory, analysis and synthesis, design and construction, application and operation of computer systems. Students will gain competencies to solve advanced engineering problems, design complex systems, act as project leaders, and conduct research and development in data science.

 

Duration of study programme: 2 years
ECTS points: 120
Qualification awarded: Master of Science in Computing

Data science combines computer science and statistics with the goal to solve exciting data-intensive problems in industry and science. Increasing amounts of data are collected in many areas generating a high demand for professional data scientists. Data science profile provides students with strong foundations in mathematics, statistical modelling, machine learning, and other knowledge required to work on data science applications such as analysis of massive data sets, complex systems, information and social networks, knowledge discovery, business analytics, signal processing, natural language processing, bioinformatics, computer vision, big data, deep learning, financial series analysis, and high-performance computing.


Course Structure

A visual guide to the study programme is presented below.
The numbers in rows represent academic semesters (two semesters per year, a total of two years) and the numbers in columns represent ECTS points (30 ECTS points per semester).

Semester 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1 Statistical Data Analysis* Introduction to Data Science* Fundamentals of Signal Processing Machine Learning 1 Elective course Seminar 1 Trans.
2 Multivariate Data Analysis Elective course of the Profile Elective course of the Profile Elective course of the Profile Elective course Seminar 2 Trans.
3 Advanced Algorithms and Data Structures Elective course of the Profile Elective course of the Profile Elective course Research seminar  
or Elective course
Project Trans.
4 Diploma thesis
                                                             
* The course is also offered at the bachelor level (if the student has previously completed this course, it will be replaced by one of the recommended elective courses)
  Mandatory courses of the Study programme
  Mandatory courses of the Profile (core courses and core-elective courses)
  Recommended elective courses of the Profile
  Free elective courses (a course from any Study programmes offered at FER)
  Mentor driven courses
  Transversal courses

Courses

Academic year
Required courses
5
ECTS
45+0+10+20 Fundamentals of Signal Processing (223374)
D. Petrinović, T. Petković
5
ECTS
45+0+0+12 Introduction to Data Science (223394)
B. Dalbelo Bašić, A. Jović, A. Sović Kržić, M. Šikić
5
ECTS
45+0+15+15 Machine Learning 1 (223767)
B. Dalbelo Bašić, J. Šnajder
3
ECTS
6+0+0+0 Seminar 1 (223146)
5
ECTS
45+0+0+15 Statistical Data Analysis (210682)
B. Dalbelo Bašić, Z. Kostanjčar, I. Velčić
Transversal Courses (18539)
Number of ECTS credits to select: at least 2.0
2
ECTS
30+15+0+0 Circular Economy (223035)
D. Škrlec
2
ECTS
30+15+0+0 Economic and Environmental Public Policies of EU (222972)
D. Škrlec
2
ECTS
30+0+0+0 Environmental Sustainability and Climate Change Mitigation (210553)
N. Debrecin, D. Škrlec, Ž. Tomšić, I. Rajšl, S. Šadek
2
ECTS
15+0+0+0 Intellectual Property in Industry (222987)
D. Babić
3
ECTS
30+0+7+0 Knowledge Management (210558)
S. Pleslić
Recommended elective courses (18540)
Number of ECTS credits to select: at least 5.0
5
ECTS
30+0+0+15 Neural Networks (222982)
S. Lončarić, M. Subašić, T. Petković
5
ECTS
30+5+0+13 Social Networks (223063)
V. Podobnik
Required courses
5
ECTS
45+0+15+6 Multivariate Data Analysis (222937)
B. Dalbelo Bašić, Z. Kostanjčar, D. Pintar
3
ECTS
30+0+0+0 Seminar 2 (223748)
Transversal Courses (18541)
Number of ECTS credits to select: at least 2.0
2
ECTS
30+0+0+0 Risk Management (210557)
T. Capuder, N. Debrecin
Recommended elective courses (18542)
Number of ECTS credits to select: at least 20.0
5
ECTS
45+0+0+5 Analysis of Massive Datasets (222942)
G. Delač, S. Srbljić, M. Šilić, K. Vladimir
5
ECTS
30+0+0+5 Bioinformatics 1 (223333)
M. Šikić, M. Domazet-Lošo
5
ECTS
30+0+0+15 Business Intelligence (223099)
L. Brkić, I. Mekterović
5
ECTS
45+0+0+18 Data Mining (223066)
A. Jović
5
ECTS
30+0+0+15 Digital Image Processing and Analysis (223335)
S. Lončarić, M. Subašić, T. Petković
5
ECTS
45+5+0+0 Mathematics of Finance (222950)
Z. Kostanjčar, P. Posedel Šimović
5
ECTS
30+0+0+6 Text Analysis and Retrieval (222925)
J. Šnajder
Required courses
5
ECTS
3
ECTS
6+0+0+0 Project (223120)
Transversal Courses (18544)
Number of ECTS credits to select: at least 2.0
2
ECTS
30+15+0+0 Circular Economy (223035)
D. Škrlec
2
ECTS
30+15+0+0 Economic and Environmental Public Policies of EU (222972)
D. Škrlec
2
ECTS
30+0+0+0 Environmental Sustainability and Climate Change Mitigation (210553)
N. Debrecin, D. Škrlec, Ž. Tomšić, I. Rajšl, S. Šadek
2
ECTS
15+0+0+0 Intellectual Property in Industry (222987)
D. Babić
3
ECTS
30+0+7+0 Knowledge Management (210558)
S. Pleslić
Recommended elective courses (18545)
Number of ECTS credits to select: at least 20.0
5
ECTS
30+0+0+5 Bioinformatics 2 (223007)
5
ECTS
26+0+0+9 Complex Networks (223747)
5
ECTS
30+0+0+15 Neural Networks (222982)
S. Lončarić, M. Subašić, T. Petković
5
ECTS
30+0+15+8 Random Signals and Processes (223746)
5
ECTS
6+0+0+0 Research Seminar (223006)
5
ECTS
30+5+0+13 Social Networks (223063)
V. Podobnik
Required courses
30
ECTS
0+0+0+0 Diploma thesis (223060)