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Goals and Competences of the master Computing Science

The Computing Science study programme aims to enable students to work and think at an academic level and to ensure that graduates of the programme:

  1. have thorough academic knowledge and insight in the area of their specialisation (discussed in more detail below per specialisation), are experts in a sub-area within their specialisation and can contribute to the further academic development within this sub-area, and are able to acquire knowledge, insight and skills in other sub-areas of Computing Science;
  2. can apply their knowledge and skills to research and system development issues, both independently and in small teams. Depending on the chosen specialisation and expertise, the emphasis here may be on research or system development;
  3. understand the social aspects of ICT;
  4. and are able to communicate at a professional level and to provide a clear oral and written presentation of completed work.

In addition, the following specialisation specific learning outcomes are defined.

  1. For the Cyber Security specialisation, graduates have a broad knowledge of information and computer security (including organisational, software, hardware, network, cryptographic, legal and privacy aspects), can evaluate the security aspects of existing systems and systems yet to be developed and to this end are able to formulate and prioritise safety requirements, are experienced in specifying, designing or developing applications in which safety plays an important role, and can contribute to discussions on the role of cyber security and privacy in society.
  2. For the Cyber Security and AI specialisation, graduates have a broad knowledge of information and computer security and artificial intelligence. They can also apply state-of-the-art artificial intelligence within the context of security and have experience in specifying, designing and developing secure machine learning systems. They have a broad overview of the role of artificial intelligence in the design of secure systems and understand the importance of secure machine learning systems.
  3. For the Data Science specialisation, graduates have a broad overview of the data science discipline (incl. algorithmic, organisational, software, hardware and ethical aspects), are able to use appropriate data science techniques to extract insights from data, are experienced with specifying, designing and creating applications in which data science plays an important role, and can contribute to discussions about the role of data science in society.
  4. For the Mathematical Foundations of Computer Science (MFoCS) specialisation, graduates have a broad knowledge of theoretical computing science and the mathematics that serve as its foundation and can apply mathematical techniques (such as logic and algebra) in modelling and analysing computing science concepts.
  5. For the Software Science specialisation, graduates possess broad knowledge of state-of-the-art techniques for the development and analysis of software (including software technology, domain-specific languages, computer-aided analysis, and the use of mathematic models and modelling techniques) and are able to apply these techniques.

Finally, for the societal specialisations, the following additional learning outcomes apply:

  1. Students who choose the Science, Management and Innovation specialisation:
    • have knowledge of the Sustainable Development Goals (SDGs) from their own discipline and social context
    • can set up and carry out an interdisciplinary study independently
    • can work towards sustainable and innovative solutions based on research
    • can make proposals intelligible to relevant stakeholders (academic and non-academic)
    • can create support for the achievement of the SDGs.
  2. Students who choose the Science in Society specialisation are:
    • capable of analysing the role of scientific expertise in societally relevant issues;
    • capable of designing and conducting independent, methodologically sound research about the interface of science and society, and contributing to academic research;
    • capable of understanding and implementing public and stakeholder engagement in research and innovation;
    • capable of analysing, improving and evaluating interdisciplinary collaborations with multiple stakeholders, integrating different perceptions, interests and types of knowledge (experiential, professional and scientific);
    • capable of substantiating and communicating the relevance of his/her scientific discipline in society.