Data Science lets engineering students from different fields become experts in machine learning and artificial intelligence.
2 years/4 semesters
English
120
August every year
Academic admission requirements below Information about the application process below
About the programme
Data Science students will learn to extract relevant information from a compilation of large datasets from different sources. Data Scientists are the experts needed to understand, apply, and improve data driven methods like machine learning and artificial intelligence. The ability to create, manage and exploit data has become one of the most important challenges for practitioners in most industries. Students with expertise in Data Science will be highly sought after in the labor market, where they will contribute to the development of smart solutions and digitisation.
- This study comes in the wake of Big Data and the need to find useful information in them. Analysis, understanding, and use of these data require interdisciplinary skills and knowledge.
- This master’s programme creates opportunities for students from different engineering backgrounds who want to focus more on digitisation and data analysis.
- You can write your master's thesis in collaboration with industry and participate in innovation and research. UiS has a strong research focus on analysis of large datasets, Cloud solutions, and machine learning.
- This master's programme leads to the Norwegian professional title 'sivilingeniør'.
Career prospects
With a master’s degree in Data Science, you can get a position in almost all industries. Some examples of businesses where you can find employment are:
- consulting companies
- telecommunications companies
- energy related businesses
- hospitals and other public agencies
A degree in Data Science provides a basis for work with:
- data analysis and development of data processing systems for the whole data lifecycle
- advanced statistics
- data mining
- machine learning and processing of large data volumes
Further education
Graduates can qualify for admission to the PhD programme in Science and Technology with relevant specialisation at the University of Stavanger.
Learning outcomes
All study programmes at the UiS have a set of defined learning targets. Read more about the learning outcome for this study programme.
A graduate should have the following learning outcomes defined in terms of knowledge, skills and general competence:
Knowledge
K1: Advanced knowledge within Data Science, which includes data processing, machine learning, data extraction, statistics and typical programming languages for the area, including: Python and R.
K2: Specialised insightinto data analysis.
K3: In-depth knowledge of scientific theory and methods in Data Science.
K4: Apply knowledge about algorithms for statistical analysis, machine learning or data extraction in new areas within data science.
K5: Analyse professional issues based on the fourth science paradigm, 4Vs of big data (volume, velocity, variety, and variability), data-driven approach, CRISP-DM (cross-industry standard process for data mining).
Skills
S1: Analyse and relate critically to different sources of information, datasets and data processes; and apply these to structure and formulate data-driven reasoning.
S2: Analyse existing theories, methods and interpretations within the subject area and work independently in applying and evaluating different storage and data processing technologies.
S3: Use CRISP-DM and scientific methods to develop data analysis programs in an independent way.
S4: Conduct independent, limited data collection, analysis and evaluation according to established engineering principles in accordance with current research ethical standards.
General Competence
G1: Analyse relevant ethical issues arising from data usage and data recovery.
G2: Apply their knowledge and skills in new areas to carry out advanced tasks and projects related to data processing, data analysis and optimisation.
G3: Communicate results of comprehensive data analysis and development work, and master Data Science expressions.
G4: Communicate on issues, analyses and conclusions related to data-driven research and development, both with specialists and to the general public.
G5: Contribute to new ideas and innovation processes by introducing data-driven approaches, comprehensive data analysis and development work, and master Data Science expressions.
Studyplan with courses
Academic requirements
A Bachelor's degree in engineering or equivalent is required. The degree must include at least:
- 10 ECTS credits in programming + 10 ECTS informatics/computer science
- The equivalent of 25 ECTS credits in mathematics, 5 ECTS credits in statistics and 7,5 ECTS credits in Physics.
In case programming and computer engineering subjects cannot be confirmed through the The Bologna Process Framework for Learning Outcomes, at least 50 credits in programming and computer engineering subjects will be required.
Only degrees from accredited universities from the following countries are confirmed through the Bologna Process: List of countries.
If the country where you completed your degree is not included in the list above, a minimum of 50 credits in programming and computer engineering subjects is required.
If you have completed studies/courses outside the University of Stavanger, you must upload course descriptions that have clearly defined curriculum (learning outcomes). The course names and codes on the course descriptions must match the transcript of records. If you do not provide course descirptions, you might risk your application to not be prioritized.
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.
Supplementary rules for admission (PDF).
This study program is taught in English, see how to meet the language requirements.
Application and admission
International and local applicants have different deadlines and application procedures, depending on your citizenship, language skills and educational background.
Student life at UiS
Frequently asked questions
What is the difference between Data Science and Computer Science?
Data Science – working with data and insights
When you study Data Science, you learn how to turn raw, complex data into actionable knowledge. You work with the entire data lifecycle – from data collection and processing to analysis and practical application.
You will gain expertise in:
- advanced statistics
- data mining
- machine learning
- analysis of large datasets
The programme focuses on developing intelligent, data-driven solutions, for example for smart cities, smart energy and digitalisation. These skills are highly sought after across the modern labour market.
Computer Science – developing and building computer systems
When you study Computer Science, you learn how to design, develop and manage complex computer systems. The focus is on understanding how systems are built, how they interact, and how to ensure the operate securely and efficiently in different contexts.
You will gain expertise in:
- programming and system design
- algorithms and statistics
- security, reliability and scalability
- distributed systems, simulation and modelling
The programme provides a foundation for architecting, developing and managing commercial computer systems for a wide range of purposes and industries.
Student exchange
By going on exchange to one of our partner institutions abroad as part of your studies, you will have an opportunity to get a unique education. In addition to improving your career opportunities, you grow as a person and gain the ability to greater reflect on the topics you study as part of your degree.
Schedule for the exchange
3rd semester
Students can go on a study abroad experience during the 3rd semester of the master's programme in Data Science. Abroad, you must choose courses that provide an equivalent specialisation in your field of study, and these must be approved before you leave. It is also important that the courses you are going to take abroad do not overlap with courses you have already taken or will take later in your studies. It is recommended to think about your specialisation and/or your field of interest. You must choose at least one non-science/technological course equivalent to 5-10 ECTS (e.g. economics, languages, ethics, project management, green transition or similar).
More opportunities
In addition to the recommended universities listed below, UiS has a number of agreements with universities outside Europe that are applicable to all students at UiS, provided that they find a relevant subject offering. Within the Nordic region, all students can use the Nordlys and Nordtek networks.
Contact your study adviser at the faculty if you have questions about guidance and pre-approval of courses: Sheryl Josdal
General questions about exchange:
Go to the exchange guide in the Digital student service desk
See where you can travel
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Contact
Faculty Administration TN
Kontor for utdanningsadministrative tjenester
Department of Electrical Engineering and Computer Science