Data Science (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:

• Computer Science and Engineering
• Human-Centered Artificial Intelligence
• Mathematical Modelling and Computation

Mandatory course requirements

The students must complete at least 45 ECTS from the total course list (below), with at least 15 ECTS from the short-list of 6 core-competency courses (below). 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 programme to cover the full spectrum of competencies in a natural way.

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.