SFF4000 Theories of Social Science Course description

The student will carry out a project in the field of data protection and identity technology, preferably in collaboration with a relevant IT company, individually or in a group of up to five students. The aim is to provide the students with an introduction to data protection and identity technology, while they solve a commercial problem in the form of an extensive project assignment with a work load equivalent to 10 hours a week over a 12-week period. If the project is carried out during the summer, the work must correspond to four days a week over a six-week period.

The increasing use of digital media and internet to solve more and more of our tasks in both our private life and our work life (banking, shopping, health, education, exams, employment, news, tourism etc.), increases the chance of a data breach or misuse of personal information. In order to prevent this and ensure that trust in digital solutions is maintained, we need good data protection. By good data protection we mean that personal data must be treated carefully and used in such a way that it benefits users, customers and employees.

The aim of the new legislation GDPR (General Data Protection Regulation) is to focus on these issues and demand that all businesses that process personal data have a good data protection system in place, which among other things means that the registered person’s rights are maintained in a secure and reassuring way. These rights are about the right to access, deletion, portability, correction of wrong data and limits to processing. To comply with the strict demands for good personal data protection, it is necessary to have good technical support. This could be technology that supports the identification of persons, process automation, fraud prevention, handling the rights and consent of the data subjects, administration and quality assurance of data processor agreements, internal control support etc.

In addition to the projects on offer, students can find their own projects within a relevant company, public organization or nonprofit. In this case, it is the student's responsibility to find a supervisor for the project within the external organization. All student-initiated projects must be approved by the course coordinator before the start of the project.

Completion of the course requires a placement in the relevant health care environment corresponding to two days a week over a 12-week period.If the project is carried out during the summer, the work must correspond to four days a week over a six-week period.

The elective course will only run if a sufficient number of students a registered.

No requirements over and above the admission requirements.

After completing the course, the student is expected to have achieved the following learning outcomes defined in terms of knowledge, skills and competence:

Knowledge

The student:

• has a basic technological understanding of the most important concepts in machine learning, data science and artificial intelligence
• has knowledge of the most important methods in machine learning, data science and artificial intelligence
• has knowledge of platforms that can be used to complete major data science projects (for instance IBM Watson’s cloud services)

Skills

The student:

• masters basic data science tools and can extract and visualise information from large quantities of data
• understands the workflow in bigger data science, artificial intelligence or machine learning projects
• is capable of using open-source and commercial tools that are used in industrial projects in the fields of data science, machine learning or artificial intelligence

General competence

The student:

• masters methods and tools used to develop and carry out projects in data science, machine learning or artificial intelligence
• is familiar with the different methods that are used to find the right tools to carry out data science projects
• has an overview of how to visualise and manipulate data and how to develop predictive methods for solving industry problems and other issues relevant to working life

Regular follow-up of the project work by a project supervisor.

The students will work in groups of three to five students to complete a project in data science, machine learning or artificial intelligence in cooperation with relevant external parties such as companies or public organisations.

The supervisor(s) can suggest suitable online courses in AI and data science that the students can take during the first few weeks of the course. The students are also encouraged to take other courses (https://cognitiveclass.ai) that will be useful in order to carry out the chosen project assignment. These courses may, among other things, deal with the following areas: Blockchain, the Internet of Things, Chat Bots, advanced use of data science, etc.

The course can be carried out individually by agreement with the course coordinator.

Projects are selected/distributed at the start of the semester.

The following work requirements are mandatory and must be approved in order to prepare for the exam:

• A project outline that describes how the group will organise their work on the project.
• A standard learning agreement must be entered into between the project provider / supervisor and the student(s), and this must be approved by the course coordinator before the project can start.
• Three meeting minutes from supervisory meetings during the project period.
• An oral mid-term presentation, individual or in groups (max 5 students), 10 minutes + 5 minutes Q&A.

The deadlines for submitting the project outline and minutes of the meetings will be presented in the teaching plan, which is made available at the beginning of the semester.

Written project report (100% of the final grade).

A written project report delivered at the of the semester, individual or in groups (max 5 students), 4000 words +/-10 %.

For group projects, all members of the group receive the same grade. Under exceptional circumstances, individual grades can be assigned at the discretion of the project supervisor(s) and Head of Studies.

The exam result can be appealed.

All aids are permitted, as long as the rules for source referencing are complied with.

Grade scale A-F.

One internal examiners. External examiners are used regularly.

The course builds on DAPE1400 Programming and DAPE2000 Mathematics with statistics. Students that do not have a basic knowledge of programming and statistics must be prepared to make considerable individual efforts to acquire such knowledge.