Special Master's track Data Science in Engineering

A Master of Science in Data Science in Engineering (DSiE) is a multidisciplinary academic expert in many aspects of handling data and information. Growing amounts of data will significantly change the jobs of (future) engineers.

A data scientist understands how to transform data into actionable information that can be used to influence operational processes, e.g. reducing waiting times in care processes, improving compliance in banks and making high-tech systems more robust. To this end the DSiE master combines topics from computer science, mathematics and industrial engineering.

The DSiE master is embedded as a special track within the Computer Science and Engineering (CSE) master and the Industrial and Applied Mathematics (IAM) master. There are two streams, leading to a DSiE master associated with either the CSE or the IAM master diploma.

Learning outcomes

A graduate from the master program 

  • is qualified to degree level in the domain of science, engineering and technology; 
  • is competent in the relevant domain-specific discipline, namely computer science and engineering; 
  • is capable of acquiring knowledge independently; 
  • approaches computer-science problems in a thorough and scientifically founded manner; 
  • is capable of critical thinking, can reason logically and form opinions; 
  • has design skills, presentation skills, and communication skills; 
  • has insight into the role of computer science in industry, society, and science; 
  • and, in addition to a recognizable domain-specific profile, possesses a sufficiently broad basis to be able to work in an interdisciplinary and multidisciplinary context. 

In addition to these general learning outcomes, a graduate from DSiE    

  • has a broad view of data science; 
  • should be able to understand and develop technology for handling structured and semi-structured and possibly distributed big data; 
  • should be able to analyse data to draw meaningful conclusions from data, effectively turning data into value; 
  • should understand the role of data in organisations, enabling the shift towards data-driven decision making in industry; 
  • should understand legal and social aspects of collecting, owning and manipulating data.