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Master's Programme (120 credits) in Information Technology, 120 credits

At this programme you can further develop your knowledge and ability in Information Technology with a particular focus on machine learning and data science. You also gain experience in project work for research and service development, and of acting in an international environment. Typical topics for the courses of the programme are artificial intelligence, big data parallel programming, data mining and digital service innovation. These topics are all relevant for many future societal challenges such as applications in autonomous vehicles and health care.

The main goal of this programme is to develop both theoretical and practical competence for research, development and implementation in Computer Science and Engineering. The basis of the programme is a data science oriented perspective on information technology with close collaboration with the industry. A part of the programme is studied in connection to the Master’s programme in Informatics, where students from both programmes get experience of cross-disciplinary collaboration to develop technical solutions and identify both societal needs and new potential services.

Upon completion of the programme, a degree certificate will be issued bearing the degree in English: Master of Science (120 credits) with a major in Computer Science and Engineering.

Specific eligibility requirements
Bachelor of Science degree (or equivalent) in an engineering subject or in computer science.

Courses in computer science, computer engineering or electrical engineering of at least 90 higher education credits, including thesis.

Courses in mathematics of at least 30 higher education credits or including calculus, linear algebra and transform methods.

Degrees from other countries than Sweden must be at the same level as a Swedish Bachelor's degree in electrical engineering.

Applicants must have written and verbal command of the English language equivalent to English course 6 (Swedish Upper-Secondary School). This can be proved by grades from English education or by such tests as:
  • IELTS: score (Academic) of 6.5 or more (with none of the sections scoring less than 5.5)
  • TOEFL paper based: score of 4.5 in written test and a total score of 575
  • TOEFL internet-based: score of 20 in written test and a total score of 90

Selection rules and procedure
Selection is made on the basis of the required educational background.

The programme is intended for full time studies over four semesters.

Instruction is generally in the form of lectures, seminars, laboratory work, consultation and project work. Several courses have compulsory assignments that shall be presented both in writing and orally. Instruction in all courses will be conducted in English.

A student who takes part of the education at another university, for example as part of an exchange programme, may include other, equivalent courses from the other university for the degree.

The following courses are offered within the programme

Year 1, Autumn Semester
Algorithms, Data Structures and Problem Solving, 7.5 credits
Perspectives on Data Science, 7.5 credits
Artifcial Intelligence, 7.5 credits
Engineering Mathematics, 7.5 credits

Year 1, Spring Semester
Learning Systems, 7.5 credits
Edge Computing and Internet of Things, 7.5 credits
Big Data Parallel Programming, 7.5 credits
Image Analysis, 7.5 credits

Year 2, Autumn semester
Data Mining, 7.5 credits
Digital Service Innovation, 7.5 credits
Deep Learning, 7.5 credits
Thesis, 7.5/30 credits

Year 2, Spring Semester
Electable course, 7.5 credits (see below)
Thesis, 22.5/30 credits

Electable courses in year 2:
Computer Vision in 3D, 7.5 credits
Intelligent Vehicles, 7.5 credits
Artificial Intelligence for Health, 7.5 credits
Or other relevant course offered by Halmstad University.

The University reserves the right to cancel courses chosen by less than 12 students.

Programme information

Rate of study:
Full time(100%)/daytime

Study period:
Autumn semester 2018

Closed for late

Educational level:


Study programme:
Link to study programme (PDF)Study programme     

Further information:
Programme director
Stefan Byttner

Updated 2018-09-05