Designing Compliant Data Products (1 ECTS)

Assessment criteria

A passing grade requires that the student obtains at least 50 percent of the points from the assignments in the course.

Location and time

The course is completed at your own pace in the learning environment built in Canvas

Materials

Anwar, M. J., Gill, A. Q., & Beydoun, G. (2018): A review of information privacy laws and standards for secure digital ecosystems Bendiek, A., & Römer, M. (2019): Externalizing Europe: the global effects of European data protection Cennamo, C., Kretschmer, T., Constantinides, P., Alaimo, C., & Santaló, J. (2023): Digital platforms regulation: An innovation-centric view of the EU’s Digital Markets Act Daggubati, L. S. (2024): Designing Digital Payment Experiences: The Crucial Role of User-Centered Design and Effective User Feedback Integration. Mitchell, A. D., & Mishra, N. (2023): Cross-Border Data Regulatory Frameworks: Opportunities, Challenges, and a Future-Forward Agenda Mujawar, J. S.: Creating Value through Data: The Definitive Exploration of Product Management for Data Products Stepanov, I. (2020): Introducing a property right over data in the EU: the data producer’s right – an evaluation Svantesson, D. (2020): Data localisation trends and challenges: Considerations for the review of the Privacy Guidelines Turillazzi, A., Taddeo, M., Floridi, L., & Casolari, F. (2023): The digital services act: an analysis of its ethical, legal, and social implications Voss, W. G. (2021): The CCPA and the GDPR are not the same: why you should understand both.

Exam schedules

The course is completed at your own pace in the learning environment built in Canvas.

Completion alternatives

According to the degree regulations (Tutkintosääntö 2024), “students have the right to apply for recognition of prior learning regardless of where, how, and when the knowledge was acquired. At Laurea, recognition of prior learning is implemented through two different procedures: a) credit transfer (knowledge acquired in studies at another higher education institution) and b) competence demonstration (knowledge acquired in other ways). The initiative for recognition of prior learning is made by the student. It is the student’s responsibility to demonstrate and verify their competence.” More information about credit transfer and competence demonstration can be found on the student intranet.

Evaluation methods and criteria

The assessment of competence focuses on the learning objectives, and the level of competence is evaluated based on the assessment criteria specified in the course implementation plan (Degree Regulation /Tutkintosääntö 2024). The course is graded on a pass/fail scale based on the points received from assignments. All members of Laurea are expected to adhere to good scientific practices, which include proper citation of sources. Familiarize yourself with these practices and ask if needed. All assignments are to be completed individually unless otherwise instructed. The teacher will inform separately about the use of plagiarism checks during the assignment review.

Student workload

The workload for the course is designed so that achieving the learning objectives of the course, one credit corresponds to an average of 26.7 hours of student work. The actual time required varies individually, depending on factors such as prior knowledge.

Content scheduling

User-Centric View on Compliance Considerations When Designing Data Products: You will develop an understanding of how to incorporate compliance requirements, such as data privacy, consent, and transparency, into the user experience. The Role of Legal Design in Internal Communication to Help Address Data Product Compliance Challenges: You will be introduced to the concept of legal design, with a focus on how it can improve internal communication between product teams and legal professionals. Continuous Compliance Throughout the Data Product Lifecycle: You will learn how to implement continuous compliance strategies, ensuring your data products align with evolving legal standards.

Teaching methods

The course is completed entirely independently according to the instructions in the learning environment built in Canvas. The course requires self-direction. The course does not include joint meetings or pair or group work. You will study the entirety in the learning environment built in Canvas, which includes study materials and assignments/tests. No personal feedback is given on the assignments: feedback is based on checking the correctness of the assignments and sharing model answers after the assignment submission.

Further information

The course is master's level and therefore bachelor's level degree student can not enroll for the course. Laurea degree students and pathway students, and cross institutional students enroll in the course through Pakki. Enrollments are processed almost daily, allowing you to start the course flexibly when it suits you. You will receive a completion record for the course approximately two weeks after finishing. During holiday periods, enrollments and completion records are processed less frequently. If the course is not completed by the end date of the implementation, the student can enroll in the next implementation. Incomplete assignments cannot be transferred to the next implementation; all tasks must be redone. For content-related inquiries, contact the responsible teacher. For other inquiries, email mooc@laurea.fi.

Evaluation scale

Approved/Failed