1. Name and explain core principles and notions of data protection and IP law relevant in the context of data science [Comprehension]
2. Recognize data protection- and IP law issues relevant in the context of data science applications and data-driven businesses [application & analysis].
3. Assess instances of data science application on the matter of their consistency with key principles of data protection and IP law [analysis & evaluation].
4. Formulate reasoned arguments (e.g. advice) about instances of data science based on these key legal concepts and principles [analysis & evaluation].
The goal of the course is to prepare the future data entrepreneurs to conduct a data-driven business accounting for the ways in which the law enables, protects, shapes or restricts use of data and data sets, with a particular focus on Intellectual Property and privacy and data protection law.|
Data Science applications are of growing importance to innovation and thus economic prosperity. For this reason, the protection offered by the law to innovators and entrepreneurs is all the more important. In particular, ownership of data and data sets is of increasing concern. Through the course students will gain legal and regulatory knowledge and insight in protecting data and data sets by means of database protection, copyright protection, contracts and trade secret laws. In addition, they will learn about the constraints that the Privacy, Data Protection and Intellectual Property law can impose on the data-driven business and the legal options for building and using own and third-party data sets. Importantly, the students will also learn about the rights of individuals in relation to their data, and about strategies of compliance with the data protection legislation.
In explaining relevant regulatory regimes, the course will take the perspective of developers (businesses and entrepreneurs), users, as well as data subjects (individuals) of data science applications. In reflecting on the relevant regulatory regimes, the course will use representative examples of real-life and hypothetical data sets in areas of marketing, smart industries, health, etc. Both a theoretical and case study approach is adopted to ensure that the program is as relevant as possible to a broad range of students interested in data science entrepreneurship.
Technological, economic, ethical, and sociological arguments will, in most cases, play a role in governing data ownership and privacy issues and solving related legal conflicts or will have influenced the way in which legal rules deal with problems. By means of both a theoretical and case study approach they will be challenged to take various aspects into consideration while solving cases. Students will be challenged to motivate how they think a specific problem is best solved.
Type of instructions
In view of the covid-19 related measures, the course will be taught online.
The teaching methods will involve a combination of the pre-recorded knowledge clips, ‘live’ online workshops, and self-study (including small assignments) in preparation to the workshops.
Type of exams
- open-book take-home exam (70%)
- 2 group assignments (papers) (30% in total, 15% each). 5.5 is required to be admitted to the final exam.
A 'pass' (5.5) for submission of the two group assignments is required for admission to the written exam. No resit of group assignments is allowed. The second attempt take-home exam will function as a resit for the entire course.
The courses from the Data Science and Entrepreneurship program require specific prior knowledge. It is only possible to participate in this course if approved by the admission committee and if you are enrolled for the program.