620087 :Data Science Regulation & Law


Voertaal Engels
Werkvorm: Interactive lectures (Collegerooster)
Tentamenvorm: written exam and 5 assignments (Tentamenrooster)
Studielast:3 ECTS credits
Blackboard informatieLink to Blackboard (Als u de melding 'Guest are not allowed in this course' krijgt, dient u nog bij Blackboard in te loggen)


mr.ir. M.H.M. Schellekens (Coördinator)

mr.dr. C.M.K.C. Cuijpers

A. Berlee LLM

prof.dr.mr. T.F.E. Tjong Tjin Tai

mr.dr. C.J. Wolswinkel

mr.dr. L.A. van Noorloos

dr. L.E.M. Taylor

Doel van de cursus

The key objective of this course is to make students aware about the role law plays in the field of data science.


After successfully completing this course, students should be able to:

    • Identify the basic functions of law.
    • List the various sources of law.
    • Describe the relation of law to different modalities of regulation.
    • Describe the different regulatory actors and different regulatory fora.
    • The student can explain and apply basic concepts of private law, such as the distinction between claims based on property right, contract, and tort, the assessment of unfair general contractual terms, contracting on use and transfer of data, and remedies in contract and tort.
    • The student can explain and apply principles of private law such as party autonomy.
    • The student can explain the role of general administrative law acts (GALA) as well as more specific administrative regulations in relation to data science.
    • The student can describe information management by administrative bodies and the role data may play in administrative decision-making and administrative (appeal) procedures.
    • The student can explain and apply basic concepts of criminal procedural law, such as the difference between inquisitorial and adversarial systems, the different actors and phases in criminal procedure, and the role of human rights (in particular the right to a fair trial) in criminal procedure.
    • The student understands the relevance of IP to, its implications for and its use by individual data science innovators
    • The student has a basic understanding of patent law.
    • The student has a basic understanding of copyright law
    • The student has a basic understanding of database protection in the EU
    • The student can critically reflect on the application of IP law to data and technologies and its implications for innovation in data science.
    • The student can describe the relation between the law, ethics, and ethical theories.
    • The student can compare different ethical outlooks, the corresponding ethical theories and apply the latter to ethical questions involving data science.
    • The student can discuss the tension between fundamental values and data science.


Inhoud van de cursus

This course provides an introduction to the role of law with regard to data science. The course is designed for students with very limited or no background in law. Law can be seen as rules that limit people’s freedom of action. However, a legal system encompasses way more then merely a set of rules. Rules are developed by public and private institutions, the system involves processes and actors for control and includes police forces and courts to enforce the rules and resolve disputes. The course is divided into 5 clusters. The first cluster provides a general introduction into Law. The other clusters cover a specific legal domain or address the relation between law and ethics. Besides covering the basics of legal science – both in general and in the dominant legal domains: private law (including intellectual property law), public law and criminal law - the course reflects upon specific legal issues pertaining data science business developments on the basis of examples and a case/scenario. This case/scenario forms the common threat of the course, linking together the domain specific issues relevant when engaged with (the development of a) data science business. This course will not delve into the distinct and important legal area of data protection as this is core to the elective course “Data science: sustainability, privacy and security”.


The course is structured in five distinct clusters:

Cluster 1: Introduction to law and data science (Colette Cuijpers)

Cluster 2: Private Law and data science (Eric Tjong Tjin Tai)

Cluster 3: Public and criminal law and data science (Anne Meuwese - Marloes van Noorloos)

Cluster 4: Intellectual Property Rights and data science (Maurice Schellekens)

Cluster 5: Ethics in data science (Anton Vedder)

Verplichte literatuur

  1. • Data Science: Business and Governance ( 2015 ).

Verplicht voor

  • Data Science: Business and Governance ( 2015, 2016, 2017 )
  • Data Science: Business and Governance (voorjaar) ( 2017 )