Computer Science - Master of Science Degree Programme, Part-Time
Study programme description for study year 2023-2024
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 andinternational students. With a master’s in Computer Science, the door is open to some of the most challenging and interesting jobsin the field. The study programme gives a broad foundation within the field of computer science. This is aninternational study programme, and all coursesare 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 preparatory summer course in programming and system administration. The purpose of the course is to provide prerequisite knowledge and skills that students might be lacking due to variations in Computer Science Bachelor programmes.
The University of Stavanger offers a master's programme aimed at students who have completed a 3-year engineering degree in computer technology. The two-year master's degree in Computer Science comprises 120 ECTS.
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, blockchain, networks, and distributed systems.
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. The emphasis on the individual teaching forms varies to some extent between the different subject groups.
The following is described in the individual course description:
- Forms of work and teaching
- Evaluation Forms
- Syllabus
- Assessment
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
Enrolment year:
-
Computer Science - Choose specialization
-
Specialisation Data Science
-
Compulsory courses
-
DATMAS: Master Thesis in Computer Science
Year 4, semester 7
-
-
Select 2 courses 7th semester
-
DAT530: Discrete Simulation and Performance Analysis
Year 4, semester 7
-
DAT620: Project in Computer Science
Year 4, semester 7
-
STA530: Statistical Learning
Year 4, semester 7
-
-
-
Specialisation Reliable and Secure Systems
-
Compulsory courses
-
DATMAS: Master Thesis in Computer Science
Year 4, semester 7
-
-
Select 2 courses 7th semester
-
DAT620: Project in Computer Science
Year 4, semester 7
-
DAT640: Information Retrieval and Text Mining
Year 4, semester 7
-
DAT650: Blockchain Technologies
Year 4, semester 7
-
ELE510: Image Processing and Computer Vision
Year 4, semester 7
-
-
-
-
Computer Science - Choose specialization
-
Specialisation Data Science
-
Compulsory courses
-
DAT500: Data-intensive Systems
Year 3, semester 6
-
DATMAS: Master Thesis in Computer Science
Year 4, semester 7
-
-
Select one course 5th semester
-
DAT510: Security and Vulnerability in Networks
Year 3, semester 5
-
DAT640: Information Retrieval and Text Mining
Year 3, semester 5
-
ELE510: Image Processing and Computer Vision
Year 3, semester 5
-
-
Select 2 courses 7th semester
-
DAT530: Discrete Simulation and Performance Analysis
Year 4, semester 7
-
DAT620: Project in Computer Science
Year 4, semester 7
-
STA530: Statistical Learning
Year 4, semester 7
-
-
-
Specialisation Reliable and Secure Systems
-
Compulsory courses
-
DAT530: Discrete Simulation and Performance Analysis
Year 3, semester 5
-
DATMAS: Master Thesis in Computer Science
Year 4, semester 7
-
-
Select one course 6th semester
-
DAT500: Data-intensive Systems
Year 3, semester 6
-
ELE520: Machine Learning
Year 3, semester 6
-
-
Recommended elective courses 7th semester
-
DAT640: Information Retrieval and Text Mining
Year 4, semester 7
-
DAT650: Blockchain Technologies
Year 4, semester 7
-
ELE510: Image Processing and Computer Vision
Year 4, semester 7
-
-
Other elective courses 7th semester
-
DAT620: Project in Computer Science
Year 4, semester 7
-
ELE510: Image Processing and Computer Vision
Year 4, semester 7
-
ELE680: Deep Neural Networks
Year 4, semester 7
-
-
-
-
Computer Science - Choose specialization
-
Specialisation Data Science
-
Compulsory courses
-
STA500: Probability and Statistics 2
Year 2, semester 3
-
DAT550: Data Mining and Deep Learning
Year 2, semester 4
-
DAT500: Data-intensive Systems
Year 3, semester 6
-
DATMAS: Master Thesis in Computer Science
Year 4, semester 7
-
-
Select one course 5th semester
-
DAT510: Security and Vulnerability in Networks
Year 3, semester 5
-
DAT640: Information Retrieval and Text Mining
Year 3, semester 5
-
ELE510: Image Processing and Computer Vision
Year 3, semester 5
-
-
Select 2 courses 7th semester
-
DAT530: Discrete Simulation and Performance Analysis
Year 4, semester 7
-
DAT620: Project in Computer Science
Year 4, semester 7
-
STA530: Statistical Learning
Year 4, semester 7
-
-
-
Specialisation Reliable and Secure Systems
-
Compulsory courses
-
DAT610: Wireless Communications
Year 2, semester 3
-
DAT600: Algorithm Theory
Year 2, semester 4
-
DAT530: Discrete Simulation and Performance Analysis
Year 3, semester 5
-
DATMAS: Master Thesis in Computer Science
Year 4, semester 7
-
-
Select one course 6th semester
-
DAT500: Data-intensive Systems
Year 3, semester 6
-
ELE520: Machine Learning
Year 3, semester 6
-
-
Recommended elective courses 7th semester
-
DAT640: Information Retrieval and Text Mining
Year 4, semester 7
-
DAT650: Blockchain Technologies
Year 4, semester 7
-
ELE510: Image Processing and Computer Vision
Year 4, semester 7
-
-
Other elective courses 7th semester
-
DAT620: Project in Computer Science
Year 4, semester 7
-
ELE510: Image Processing and Computer Vision
Year 4, semester 7
-
ELE680: Deep Neural Networks
Year 4, semester 7
-
-
-
-
Compulsory courses
-
DAT510: Security and Vulnerability in Networks
Year 1, semester 1
-
DAT515: Cloud Computing Technologies
Year 1, semester 1
-
DAT520: Distributed Systems
Year 1, semester 2
-
DAT505: Ethical Hacking
Year 2, semester 3
-
DAT610: Wireless Communications
Year 2, semester 3
-
DAT600: Algorithm Theory
Year 2, semester 4
-
DAT550: Data Mining and Deep Learning
Year 3, semester 6
-
DATMAS: Master Thesis in Computer Science
Year 4, semester 7
-
-
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
-
DAT650: Blockchain Technologies
Year 3, semester 5
-
ELE510: Image Processing and Computer Vision
Year 3, semester 5
-
-
Other elective courses 5th and 7th semester at UiS
-
DAT535: Data-intensive Systems and Algorithms
Year 3, semester 5
-
DAT620: Project in Computer Science
Year 3, semester 5
-
ELE680: Deep Neural Networks
Year 3, semester 5
-
-
-
Exchange 5th or 7th semester
-
Exchange Studies 5th or 7th semester
-
-