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Master of Science in Applied Data Science – part-time

Applied Data Science is a Master's programme that aims to increase the ICT competence of engineers from different fields.

Publisert: Endret:

4 years/8 semesters

Number of study places


ECTS credits


Language of instruction


Study start

August every year

No tuition fees

Kim Alstad foran pc skjerm humanoid robot koding datateknologi datavitenskap data student. Foto: Elisabeth Tønnessen

The programme is offered as a 2-year full-time study and as a part-time study over 4 years.

This part-time study can be taken in combination with work or other activities for those who live in the region and can follow the educational plan provided. Courses are taken together with full-time students who complete the degree in two years, but as a part-time student you will have fewer courses per semester, distributed over four years. The part-time study takes place during the day and most of the courses are based on laboratory work and project work in groups with compulsory attendance. Lectures are usually not streamed, but books and other materials cover the syllabus. You will usually need 1-2 days per week (depending on the semester) to follow mandatory activities.

Applied Data Science students will learn to extract relevant information from a compilation of large datasets from different sources. The ability to create, manage and exploit data has become one of the most important challenges for practitioners in most disciplines, sectors, and industries.

Students with expertise in Applied Data Science will be highly sought after in the future labor market, where they will contribute to the development of smart solutions and digitization.

The new 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. Applied Data Science creates opportunities for students from different engineering backgrounds who want to focus more on digitization and data analysis.

For students with a bachelor's degree in computer science or similar, we recommend the master's program in Computer Science, specialisation Data Science.

Applied Data Science is crucial for creating smart solutions, such as the development of smart cities, smart energy, smart healthcare. Applied Data Science specialises in information retrieval, data recovery and further in-depth study of statistics.

The Master's programme has a practical profile, where after specialisation in statistics and computer science you use algorithms on real data sets.

UiS has a strong research focus on analysis of large datasets, Cloud solutions, and machine learning.

University of Stavanger offers and encourages the student to take a preparatory summer course in programming and system administration. The first part is web-based and taken in June/July and the second part is 5 days on-campus in the first half of August. More information about the summer course: Preparatory course offer.

UiS is offering break rooms, reading rooms and computer rooms especially reserved for the master students. There is also a student organization for these students, ISI, which is arranging both academic and social gatherings.

You may also be interested in:

Computer Science, Data Science master

Computer Science, reliable and secure systems master

Career prospects

With a Master in Applied 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. Specialisation in Data Science provides a basis for work in data analysis and development of data processing systems for the whole data lifecycle. It builds knowledge and skills in advanced statistics, data mining, machine learning and processing of large data volumes.

Completed master’s degree in Applied Data Science provides the basis for admission the PhD programme in Information technology, mathematics and physics.

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 candidate with a completed 2-year Master’s degree in Applied Data Science shall have the following total learning outcomes defined in terms of knowledge, skills and general competences:


K1: Advanced knowledge within Data Science, which includes data processing, data, machine learning, data extraction, statistics and typical programming languages for the area, including: Python and R.

K2: Specialized insight into 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).



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.

Academic requirements

A Bachelor's degree in engineering or equivalent is required.

The degree must include at least:

  • 10 ECTS credits in informatics/computer sciences/an introductory course for engineers including programming
  • 30 ECTS credits in mathematics/statistics/calculus

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.

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.

How to apply?

English requirement, documentation requirement and more

Important information

Only Nordic citizens and applicants residing in Norway can apply to MSc in Applied Data Science.

Application period: 1 February - 15 April

(NB! The deadline for applicants with foreign educational background is 1 March.)


For inquiries regarding admission, send an email to

Study plan and courses
Already a student? Find the full study programme description and study plan here

Frequently asked questions

spørsmål og svar SOS

What is the main difference between the three master's programs?

The difference between ADS (Applied Data Science) and DS (Data Science) is that in ADS, students receive more basic training in programming and databases that students in DS (and CS (Computer Science)) already have. Otherwise, there is a large overlap between ADS and DS. DS and CS have the same admission basis, while ADS has a different admission basis.

What is the difference between ADS / DS and CS (Reliable and secure systems)?

Specialization: Reliable and secure systems

The specialization in reliable and secure systems teaches you the management, design and programming of computer systems. The ability to integrate knowledge and skills in security, reliability and scalability together with algorithm theory and statistics is necessary to respond to challenges in computer systems in all industries.

Specialization in reliable and secure systems provides a basis for work with the development and planning of commercial computer systems for various purposes. You gain knowledge and skills in network security, reliability of distributed systems, simulation and modeling.

What do you learn in the specialization Data Science?

The specialization in Data Science teaches you to extract relevant information from a compilation of large data sets from different sources. The ability to create, manage and utilize data has become one of the most important challenges for practitioners in almost all disciplines, sectors and industries.

Specialization in Data Science provides a basis for work in data analysis and development of data processing systems for the entire data life cycle. You gain knowledge and skills in advanced statistics, data mining, machine learning and processing large amounts of data. The study will be highly sought after in the future labor market, with the development of smart solutions such as in smart cities, with smart energy and digitalisation.

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. All about exchange

In the 3rd semester of the master's programme in Applied Data Science, 30 ECTS points have been reserved for elective courses. During this semester you can choose to study abroad. Abroad, you need to choose courses at a corresponding level and these must be pre-approved before you leave. It is important that the courses you are going to take abroad do not overlap with the courses you have taken or will take later in the study program. One tip is to think about your specialization and your field of interest. Pre-approved course packages will be available when students apply for exchange.

For master's students in Applied Data Science we recommend an exchange in the 3rd semester, to one of the following institutions

  • Aalborg Universitet (Nordtek), Denmark
  • Danmarks Tekniske Universitet (Nordtek), Denmark     
  • Universiteit Twente, The Netherlands
  • Universitas Lodziensis, Poland
  • RTWH Aachen University, Germany
  • Institut polytechnique de Grenoble, France

Other institutions

  • University of Adelaide, Australia
  • Universidad de Sevilla, Spain
  • California State University Los Angeles, USA

The Faculty of Science and Technology is a member of a Nordic network of technical universities. Check your possibilities for exchange in the Nordic region:

You should start planning early. The application deadline for exchange abroad is 1st of September for the spring semester and 1st of February for the fall semester.



Faculty of Science and Technology

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Faculty of Science and Technology

Department of Electrical Engineering and Computer Science
Our department

Department of Electrical Engineering and Computer Science

Welcome to the Department of Electrical Engineering and Computer Science!

The department is responsible for teacing, research and development within electrical and computer engineering at UiS. As computer technology becomes increasingly important in society, so this department keeps growing in size and expanding its activities.

Relevant studies

The department of Electrical Engineering and Computer Science offers a growing range of studies, from bachelor to Phd level (see full list on our Norwegian pages).

The master programme in Computer Science is international and taught in English.

ICT research

Research in the department is concentrated on four main areas - follow the links to our Research pages for more information:

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