
Programs
Data Science
First year
Semester 1
COURSES | ECTS | Hours | Lectures* | Practice** | Type |
---|---|---|---|---|---|
Innovative project 1 | 5 | 60 | 2 | 2 | Obligatory |
Negotiation and conflict resolution | 4 | 45 | 2 | 1 | Obligatory |
Data Preparation | 6 | 60 | 2 | 2 | Obligatory |
Data Warehouses and Business Intelligence | 6 | 60 | 2 | 2 | Obligatory |
Introduction to Data Science | 3 | 45 | 1 | 2 | Obligatory |
Disruptive technologies | 6 | 60 | 2 | 2 | Elective |
Managing Quality in IT Projects | 6 | 60 | 2 | 2 | Elective |
Semester 2
COURSES | ECTS | Hours | Lectures* | Practice** | Type |
---|---|---|---|---|---|
Social Network Analysis | 6 | 60 | 2 | 2 | Obligatory |
Creativity and Critical Thinking | 3 | 45 | 2 | 1 | Obligatory |
Quantitative methods of data processing | 6 | 45 | 1 | 2 | Obligatory |
Machine Learning Methods | 4 | 45 | 1 | 2 | Obligatory |
Security, privacy and ethics of digital data | 5 | 45 | 1 | 2 | Obligatory |
Internet of things | 6 | 60 | 2 | 2 | Elective |
Management of Innovation | 6 | 45 | 1 | 2 | Elective |
Second year
Semester 3
COURSES | ECTS | Hours | Lectures* | Practice** | Type |
---|---|---|---|---|---|
Affective Computing | 6 | 60 | 2 | 2 | Obligatory |
Analytical techniques based on large data sets | 6 | 60 | 2 | 2 | Obligatory |
Advanced Machine Learning Methods | 6 | 60 | 2 | 2 | Obligatory |
Management of Information Services | 3 | 45 | 2 | 1 | Obligatory |
Visualisation and analytical software tools | 3 | 45 | 1 | 2 | Obligatory |
Cloud Analysis | 6 | 60 | 2 | 2 | Elective |
Structured Analytical Techniques | 6 | 60 | 2 | 2 | Elective |
Semester 4
COURSES | ECTS | Hours | Lectures* | Practice** | Type |
---|---|---|---|---|---|
Graduation Thesis | 30 | 0 | 0 | 0 | Obligatory |
- Study program duration
- 4 semesters (2 years)
- Semester duration
- 15 weeks of active teaching + 5 examination weeks
- Total number of ECTS points
- 120
- Certifications obtained during studies
-
IT SMF – ITIL Foundation
- Title
- struč.spec.ing.comp. (Professional Master of Computer Engineering with sub-specialization in Data Science)
* total hours of lectures per week
** total hours of practice per week
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