Entry requirements

• Bachelor's or engineering degree (at least 180 credits), in computer science or related subjects such as mathematics, informatics, telecommunications, electrical engineering, physics.
• At least 15 credits in programming.
• At least 7.5 credits in mathematics.
• Knowledge equivalent to English 6 at Swedish upper secondary level.
• At least 45 credits completed within the main area of ​​the Computer Science program: Applied Data Science, master's program including 5 credits from Computer Science Research Methods and Computational Theoretical Foundations (DA634E or DA658E)

Progression level in relation to degree requirements

The course is part of the Computer Science: Applied Data Science, Master's program, and can be included in the Master's degree in Computer Science (120 credits).

Course contents

The course consists of three parts: problem definition and project planning; the thesis project; and opposition on another student's thesis.

The thesis project has two phases:

• Carrying out the project and documenting it in writing (in the form of the thesis), and
• Presenting and defending the thesis orally

The opposition consists of carefully studying and critically analyzing another student's thesis, producing a written opposition, and acting as an opponent at the presentation of another student's thesis.

Learning outcomes

Knowledge and understanding

To pass the course, the student must be able to:

1. Demonstrate in-depth knowledge in computer science with a special focus on Data Science.

2. Explain the research process and its planning

Skills and abilities 

To pass the course, the student must be able to:

3. Independently identify, formulate and manage complex problems

4. Plan and carry out research and development projects within given time frames

5. Describe a research project's contribution to a field of knowledge

6. Actively seek and analyze relevant information about a given research problem

7. Apply research methods

8. Orally and in writing present the results of a research project in a scientific manner in international and national contexts.

9. Communicate the results of research projects to different target groups

10. Critically analyze a scientific report and identify its main strengths and weaknesses

Judgment and approach

To pass the course, the student must be able to:

11. Choose a research method for a given scientific problem and argue for its suitability

12. Assess and analyze relevant research questions of importance to data science

13. Make data science considerations based on scientific, societal and ethical aspects

14. Show insight into the possibilities and limitations of science, its role in society and people's responsibility for how it is used

15. Show the ability to identify their need for further knowledge and to take responsibility for their knowledge development.

Learning activities

The teaching is project-based and adapted to the student's previous knowledge, ability and experience. The main activities are thesis work, supervision and seminars. The student will also present orally and act as an opponent on another thesis.

The supervisor assigned to the student (and any external contact person, e.g. a user of the project results) supports and guides the student through the project, but the student should be proactive in requesting support. The student is expected to continuously report to the supervisor during the project work

Assessment

Students' performance is assessed through

• a project plan (3 credits, assessed with U/G), - learning objectives: 2,3,4,5,6,7,11,15
• a written degree project (25 credits, assessed with UA), - learning objectives: 1,2,3,4,5,6,7,8,9,11,12,13,14,15
• an oral presentation (1 credit, assessed with U/G), - learning objectives: 8,9
• Written and oral opposition (1 credit, assessed with U/G). - learning objectives: 10

For a pass (at least E) all elements must be passed.

The final grade is based on the written degree project.

Course literature

• Dawson, Christian (2009). Projects in computing and information systems. A student’s guide, 2nd edition. Addison Wesley
• Oates, B.J. (2022). Researching Information Systems and Computing. Sage Publications, UK
• Zobel, J. (2004). Writing for Computer Science – The art of effective communication, 2nd edition. Springer, UK

Individual literature is selected by the student in consultation with the supervisor

Course evaluation

Malmö University provides students who participate in, or who have completed a course, with the opportunity to express their opinions and describe their experiences of the course by completing a course evaluation administered by the University. The University will compile and summarise the results of course evaluations. The University will also inform participants of the results and any decisions relating to measures taken in response to the course evaluations. The results will be made available to the students (HF 1:14).

Interim rules

If a course is no longer offered, or has undergone significant changes, the students must be offered two opportunities for re-examination based on the syllabus that applied at the time of registration, for a period of one year after the changes have been implemented.

The syllabus is a translation of a Swedish source text.

Additional information

If a student has a Learning support decision, the examiner has the right to provide the student with an adapted test, or to allow the student to take the exam in a different format. The syllabus is a translation of a Swedish source text.