Data Science and Big Data

The obtained profile provides thorough competencies in the analysis of massive data, which cover the whole spectrum from data acquisition through storage, analysis and interpretation to the application and presentation of the results.

Data Science and Big Data

This educational profile provides comprehensive competencies in the analysis of large-scale data, covering the entire spectrum from data acquisition and storage to analysis, interpretation, application, and presentation of results.

The definition of "massive data" varies based on the computational power of the platform used. Addressing issues of data security and privacy is crucial to handle data ethically and unleash its full potential.

Admission to the profile

This educational profile is typically achieved through one of the following DTU master's programmes, although others may also be eligible:

Mandatory course requirements

The students must complete at least 45 ECTS from this course list, with at least 15 ECTS from the short-list of 6 core-competency courses. This ensures that students gain exposure beyond their primary master's programme. The remaining 25-30 ECTS can be freely chosen from the list. The courses may have been passed in the student's bachelor's or master's education. The variety of courses allows students to focus on a certain topic or to become a generalist in the area.

The general idea behind the profile is to spread the activities within the program to “cover” the full spectrum of competencies in a natural way for a candidate who has specialized in Data Science.

Master's thesis

Additionally, the topic of the MSc thesis must be in the area of data analysis, covering at least two of the following four main topics: data origins and collection, data storage, analytics, consumers. The thesis should show a clear perspective to the whole subject. As a general rule, the thesis should concern a real-world data setting, preferably together with an external partner.

Knowledge requirements
Students are expected to have sound basic knowledge in programming, statistics, and algorithms in order to have the prerequisites to follow the relevant courses on the list.