Computer Science - Master of Science Degree Programme, Part-Time


Study programme description for study year 2024-2025

Facts

Credits (ECTS)

120

Studyprogram code

M-DATENG-D

Level

Master's degree (2 years)

Leads to degree

Master of Science

Full-/Part-time

Part-time

Duration

8 Semesters

Undergraduate

No

Language of instruction

English

The master’s programme in Computer Science at University of Stavanger is open to Norwegian and international students. With a master’s degree in Computer Science, the door is open to some of the most challenging and interesting jobs in the field. The study programme gives a broad foundation within the field of computer science. This is an international study programme, and all courses are given in English. The programme is organized under the Faculty of Science and Technology, Department of Electrical Engineering and Computer Science.

Programme content, structure and composition

The University of Stavanger offers a master's programme aimed at students who have completed a 3-year engineering degree in computer technology. The master's degree in Computer Science comprises 120 ECTS. The part-time programme lets you spread these courses over 4 years, taking one to two courses every semester.

The programme has practical courses that build on mathematics, statistics, and basic computer science courses from the bachelor's degree in Computer Science. The programme contains advanced algorithmic topics, security, networks, distributed systems, machine learning and data mining.

The programme offers a variety of work and teaching activities, from traditional lecture series and exercises, project work, self-study and laboratory teaching to introduction and practice in the use of modern software.
Which teaching forms are used varies between different subjects and topics.

The following is described in the individual course description:

  • Forms of work and teaching
  • Evaluation Forms
  • Syllabus
  • Assessment

Using technology for a better world


The UN's Sustainable Development Goals (SDGs) are the world's collective action plan to eradicate poverty, combat inequality, and stop climate change by 2030. With a master’s in computer science, you gain skills that can directly contribute to achieving these goals for a better world. ICT can be used to help with all the SDGs.

For example, ICT systems can help document, analyze, and streamline resource utilization. For instance, the sharing economy can contribute to more people using the same cars and houses, thus contributing to SDG 12: Responsible Consumption and Production.

Cybercrime, including fraud and identity theft, is a growing threat. With a master's degree in computer science, you learn how to design secure IT systems, contributing to SDG 16: Peace, Justice, and Strong Institutions.

For SDG 9: Industry, Innovation, and Infrastructure, a computer science program will teach you about the IT infrastructure behind today's and tomorrow's internet. In the master's program in computer science, you will learn about cloud technology, wireless networks, and the networks of future mobile phones like 6G.

The university aims to offer all the study programmes as planned but must make reservations about sufficient resources and / or students to complete the offer. Over time, it will be natural for the academic content and offering of courses to change due to the general developments in the field of study, the use of technology and changes in society at large.

Learning outcomes

After having completed the master’s programme in Computer Science, the student shall have acquired the following learning outcomes, in terms of knowledge, skills and general competences:

Knowledge

K1: Have advanced knowledge in Computer Science including Cloud computing, security, blockchain, networks, distributed systems, etc.

K2: Have deep knowledge in the subject areas’ scientific theories and methods.

Skills

S1: Use relevant methods for research and software development in an independent manner.

S2: Analyseand relate in a critical manner to different information sources and apply these to structure and formulate professional reasoning within information technology.

S3: Perform an independent, limited research- or development project under guidance and in line with established ethical norms for research.

S4: Exploit knowledge in wireless communication, sensor networking, and distributed communication systems.

S5: Design, model, simulate, and develop advanced network-based computer systems with focus on dependability and security.

General Competence

G1: Analyse relevant professional, and research ethical problems.

G2: Apply one’s knowledge and skills to new areas to conduct complex tasks and projects.

G3: Communicate comprehensively about own work and master the subject area’s form of expression.

G4: Communicate professional problems, analyse, and draw conclusions within the subject area, both with specialists and the general public.

Career prospects

Developers and researchers in Computer Science are indispensable in almost all industries. Some examples of businesses where they find employment: consulting companies, telecommunications companies, oil-related businesses, hospitals and other public agencies. We encounter digital technology everywhere, and researchers and developers in Computer Science are crucial in making information society and digitalization a reality.

A completed master’s degree in Computer Science provides the basis for admission to the PhD programme in Information technology, mathematics and physics.

Course assessment

Schemes for quality assurance and evaluation of studies are stipulated in the Quality system for education

Study plan and courses

  • Computer Science - Choose specialization

    • Specialisation Data Science

      • Compulsory courses

        • DATMAS: Master Thesis in Computer Science

          Year 4, semester 7

          Master Thesis in Computer Science (DATMAS)

          Study points: 30

      • Select 2 courses 7th semester

        • DAT530: Discrete Simulation and Performance Analysis

          Year 4, semester 7

          Discrete Simulation and Performance Analysis (DAT530)

          Study points: 10

        • DAT620: Project in Computer Science

          Year 4, semester 7

          Project in Computer Science (DAT620)

          Study points: 10

        • STA530: Statistical Learning

          Year 4, semester 7

          Statistical Learning (STA530)

          Study points: 10

    • Specialisation Reliable and Secure Systems

      • Compulsory courses

        • DATMAS: Master Thesis in Computer Science

          Year 4, semester 7

          Master Thesis in Computer Science (DATMAS)

          Study points: 30

      • Recommended elective courses 7th semester

        • DAT640: Information Retrieval and Text Mining

          Year 4, semester 7

          Information Retrieval and Text Mining (DAT640)

          Study points: 10

        • ELE510: Image Processing and Computer Vision

          Year 4, semester 7

          Image Processing and Computer Vision (ELE510)

          Study points: 10

      • Other elective courses 7th semester

  • Computer Science - Choose specialization

    • Specialisation Data Science

      • Compulsory courses

      • Select one course 5th semester

        • DAT510: Security and Vulnerability in Networks

          Year 3, semester 5

          Security and Vulnerability in Networks (DAT510)

          Study points: 10

        • DAT640: Information Retrieval and Text Mining

          Year 3, semester 5

          Information Retrieval and Text Mining (DAT640)

          Study points: 10

        • ELE510: Image Processing and Computer Vision

          Year 3, semester 5

          Image Processing and Computer Vision (ELE510)

          Study points: 10

      • Select 2 courses 7th semester

        • DAT530: Discrete Simulation and Performance Analysis

          Year 4, semester 7

          Discrete Simulation and Performance Analysis (DAT530)

          Study points: 10

        • DAT620: Project in Computer Science

          Year 4, semester 7

          Project in Computer Science (DAT620)

          Study points: 10

        • STA530: Statistical Learning

          Year 4, semester 7

          Statistical Learning (STA530)

          Study points: 10

    • Specialisation Reliable and Secure Systems

      • Compulsory courses

        • DAT530: Discrete Simulation and Performance Analysis

          Year 3, semester 5

          Discrete Simulation and Performance Analysis (DAT530)

          Study points: 10

        • DATMAS: Master Thesis in Computer Science

          Year 4, semester 7

          Master Thesis in Computer Science (DATMAS)

          Study points: 30

      • Select one course 6th semester

      • Recommended elective courses 7th semester

        • DAT640: Information Retrieval and Text Mining

          Year 4, semester 7

          Information Retrieval and Text Mining (DAT640)

          Study points: 10

        • ELE510: Image Processing and Computer Vision

          Year 4, semester 7

          Image Processing and Computer Vision (ELE510)

          Study points: 10

      • Other elective courses 7th semester

  • Compulsory courses

  • 5th or 7th semester at UiS or Exchange Studies

    • Courses at UiS 5th and 7th semester

      • Recommended elective courses 5th and 7th semester at UiS

        • DAT640: Information Retrieval and Text Mining

          Year 3, semester 5

          Information Retrieval and Text Mining (DAT640)

          Study points: 10

        • DAT650: Blockchain Technologies

          Year 3, semester 5

          Blockchain Technologies (DAT650)

          Study points: 10

        • ELE510: Image Processing and Computer Vision

          Year 3, semester 5

          Image Processing and Computer Vision (ELE510)

          Study points: 10

      • Other elective courses 5th and 7th semester at UiS

    • Exchange 5th or 7th semester

  • Compulsory courses

  • 5th or 7th semester at UiS or Exchange Studies

    • Courses at UiS 5th and 7th semester

      • Choose one course

      • Recommended elective courses 5th and 7th semester at UiS

        • DAT530: Discrete Simulation and Performance Analysis

          Year 3, semester 5

          Discrete Simulation and Performance Analysis (DAT530)

          Study points: 10

        • DAT535: Data-intensive Systems and Algorithms

          Year 3, semester 5

          Data-intensive Systems and Algorithms (DAT535)

          Study points: 5

        • DAT640: Information Retrieval and Text Mining

          Year 3, semester 5

          Information Retrieval and Text Mining (DAT640)

          Study points: 10

        • DAT655: Blockchain Technologies and Application

          Year 3, semester 5

          Blockchain Technologies and Application (DAT655)

          Study points: 5

      • Other elective courses 5th and 7th semester at UiS

        • DAT620: Project in Computer Science

          Year 3, semester 5

          Project in Computer Science (DAT620)

          Study points: 10

        • ELE510: Image Processing and Computer Vision

          Year 3, semester 5

          Image Processing and Computer Vision (ELE510)

          Study points: 10

        • ELE680: Deep Neural Networks

          Year 3, semester 5

          Deep Neural Networks (ELE680)

          Study points: 5

        • STA510: Statistical Modeling and Simulation

          Year 3, semester 5

          Statistical Modeling and Simulation (STA510)

          Study points: 10

    • Exchange 5th or 7th semester

Student exchange

Going abroad is a possibility for all UiS students, although special arrangements may be necessary for part-time students.

For more information, see Master of Science in Computer Science.

Admission requirements

A bachelor´s degree within the following disciplines is required:
- Computer Engineering
- Computer Science, Informatics or similar with at least 50 ECTS credits in computer science/computer engineering

Applicants must have the equivalent of 25 ECTS credits in mathematics, 5 ECTS credits in statistics and 7,5 ECTS credits in Physics.

If you have completed studies/courses outside the University of Stavanger, you must upload course descriptions that have clearly defined curriculum (learning outcomes), together with your transcript of records. The course names and codes on the course descriptions must match the transcript of records. If you do not provide course descriptions, you might risk your application to not be prioritized.

The course descriptions should be submitted in English or in Norwegian, but a translation does not have to be provided by an authorized translator.

Admission to this master's programme requires a minimum grade average comparable to a Norwegian C (according to ECTS Standards) in your bachelor's degree. Applicants with a result Second-class lower Division or lower are not qualified for admission.

Contact information

Faculty of Science and Technology, tel 51 83 17 00, E-mail: post-tn@uis.no.

Study Adviser: Sheryl Josdal.