Programme structure

The programme consists of several parts, each comprising distinct courses (in total 60 ECTS). Each part is presented below.

Outline

Autumn semester (September - October)

Compulsory courses

Autumn semester (November – January)

The second period introduces elective courses, where you choose two out of three available courses that run parallel during the second half of the semester.

Spring semester (January – March)

Elective course 1 (7.5 ECTS) and Elective course 2 (7.5 ECTS).

(The selection of elective courses may vary between semesters depending on availability. Also, an announced elective course may be cancelled if too few students choose that course, and for some elective courses with high demand a selection will need to be made.]

Spring semester (April – June)

The last study period will focus entirely on the degree project. We expect students to formulate a clear and researchable research question at the beginning of this final period. The topic will be selected from a list of broad themes specified by the available supervisors.

Course overview

Year 1

Name	Type	Semester	Credits
Data Analytics and Business Economics: Introduction to Programming	Compulsory	1	3.5
Data Analytics and Business Economics: Data Visualisation	Compulsory	1	4
Data Analytics and Business Economics: Machine Learning from a Regression Perspective	Compulsory	1	7.5
Data Analytics and Business Economics: Legal Aspects of Data Analytics	Elective	1	4
Data Analytics and Business Economics: Working with Databases	Elective	1	3.5
Data Analytics and Business Economics: Advanced Machine Learning	Elective	1	7.5
Elective course 1	Elective	2
Elective course 2	Elective	2
Data Analytics and Business Economics: Master Essay I	Compulsory	2	15

Programme syllabus

The syllabus provides an overview of the programme. It outlines the admission requirements, lists the courses included, and explains which degree the studies lead to.

Syllabus (PDF, new tab)