The teaching language of the courses as well as of the preparation of the dissertation is English.
In order to obtain the degree, compulsory attendance and successful examination in all courses, which are divided into the two semesters (A and B) and the preparation of the Thesis in the third semester, are required. Attendance of the courses is compulsory.
The MSc consists of compulsory courses and optional compulsory courses. A total of fifteen (15) courses are offered.
The compulsory courses are offered in the 1st and 2nd semester and are ten (10) in total. The elective compulsory courses offered in the 2nd semester are five (5) in total and students are asked to choose two (2) out of five (5). Each semester lasts 12 full weeks and corresponds to a workload of 30 credits (ECTS).
The Master’s thesis is prepared by all students compulsorily in the last semester of their studies and corresponds to 30 credits (ECTS)
The Programme of Study is completed with the accumulation of 90 credits (ECTS).
The thesis can also be written in the form of a submitted/published article in a peer-reviewed scientific journal and presented in English.
First Semester Fall
| No | Cource Code | Type | Course Title | Hours | ECTS |
|---|---|---|---|---|---|
| 1 | M01 | Mand. | Innovative software development methods for smart cities | 100 | 5 |
| 2 | M02 | Mand. | Smart Cities Platforms | 100 | 5 |
| 3 | M03 | Mand. | Research Methodologies | 100 | 5 |
| 4 | M04 | Mand. | Skills for smart cities workforce | 100 | 5 |
| 5 | M05 | Mand. | Smart Cities: Context, Policy and Governnance | 100 | 5 |
| 6 | M06 | Mand. | Social innovation and citizen engagement | 100 | 5 |
| Total teaching hours and ECTS | 600 | 30 | |||
Second Semester Spring
| No | Cource Code | Type | Course Title | Hours | ECTS |
|---|---|---|---|---|---|
| 7 | M07 | Mand. | IoT and cloud computing for smart cities | 100 | 5 |
| 8 | M08 | Mand. | Smart city infrastructure, technologies and applications | 100 | 5 |
| 9 | M09 | Mand. | Smart city economy and financing | 100 | 5 |
| 10 | M10 | Mand. | AI and big data analytics for smart city applications | 100 | 5 |
| 11 | M11 | Elective | Smart City Resilience: Internal and external influences | 100 | 5 |
| 12 | M12 | Elective | Smart city digital twins | 100 | 5 |
| 13 | M13 | Elective | Green and Sustainable Smart Cities | 100 | 5 |
| 14 | M14 | Elective | Smart cities security and privacy | 100 | 5 |
| 15 | M15 | Elective | Data-Driven Management and Analytics (DDMA) within Smart Cities | 100 | 5 |
| Total teaching hours and ECTS | 600 | 30 | |||
Third Semester Fall
| No | Course Code | Course | ECTS |
| 16 | DIS | Diploma thesis | 30 |