Data Science - Master of Science Degree Programme


Study programme description for study year 2024-2025

Facts

Credits (ECTS)

120

Studyprogram code

M-APPDAT

Level

Master's degree (2 years)

Leads to degree

Master of Science

Full-/Part-time

Full-time

Duration

4 Semesters

Undergraduate

No

Language of instruction

English

A master's degree in Data Science makes you eligible for the most demanding and interesting work tasks within data analysis, smart solutions (such as smart cities, smart energy), and digitalization. The Master’s programme in Data Science is an international programme where the teaching language is English.

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 or similar with necessary background in programming and computer science (at least 20 ECTS). The two-year master's degree in Data Science comprises 120 ECTS.

The programme has practical courses that build on mathematics, statistics, and basic computer science courses from the bachelor's degree. The programme contains advanced statistical topics, processing of large dataset, Cloud solutions, machine learning, and data mining.

The programme offers a variety of study and learning 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 United Nations' Sustainable Development Goals (SDGs) serve as the global roadmap for eradicating poverty, combating inequality, and addressing climate change by 2030. Through the master's programme in Data Science, you can acquire the skills to directly contribute to achieving these goals. Information and Communication Technology (ICT) can be utilized to assist in all the SDGs.

Here are some examples based on our work at IDE:

Researchers at IDE are involved in analyzing image data to aid doctors in diagnosing diseases or studying heart data to detect cardiovascular conditions before they manifest. This aligns with the third SDG, which focuses on ensuring good health and well-being.

With the increasing power and climate crises, it has become crucial to generate as much energy as possible. By installing solar panels on rooftops and implementing small-scale hydropower plants on individual farms, a smarter grid system needs to be developed to manage electricity more efficiently. IDE has undertaken multiple master's and PhD projects concerning smart grids, making it a potential avenue for those pursuing a master's in data science. This work directly contributes to SDG 7 (affordable and clean energy), SDG 11 (sustainable cities and communities), and SDG 13 (climate action).

Other


The university aims to offer all the study programs 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.

After admission to the programme, you can apply for a part-time study programme. Alternatively, you can apply directly to a part-time study.

Learning outcomes

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

Knowledge

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

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. 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 Data Science provides the basis for admission 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

  • Compulsory courses

    • DASMAS: Master's thesis in Data Science

      Year 2, semester 3

      Master's thesis in Data Science (DASMAS)

      Study points: 30

  • 3rd semester at UiS or Exchange Studies

    • Courses at UiS 3rd semester

      • Recommended electives 3rd semester

        • DAT640: Information Retrieval and Text Mining

          Year 2, semester 3

          Information Retrieval and Text Mining (DAT640)

          Study points: 10

        • ELE510: Image Processing and Computer Vision

          Year 2, semester 3

          Image Processing and Computer Vision (ELE510)

          Study points: 10

        • STA500: Probability and Statistics 2

          Year 2, semester 3

          Probability and Statistics 2 (STA500)

          Study points: 10

        • STA530: Statistical Learning

          Year 2, semester 3

          Statistical Learning (STA530)

          Study points: 10

      • Other electives 3rd semester

        • DAT530: Discrete Simulation and Performance Analysis

          Year 2, semester 3

          Discrete Simulation and Performance Analysis (DAT530)

          Study points: 10

        • DAT605: Reinforcement Learning

          Year 2, semester 3

          Reinforcement Learning (DAT605)

          Study points: 5

        • DAT620: Project in Computer Science

          Year 2, semester 3

          Project in Computer Science (DAT620)

          Study points: 10

        • ELE680: Deep Neural Networks

          Year 2, semester 3

          Deep Neural Networks (ELE680)

          Study points: 5

    • Exchange 3rd semester

  • Compulsory courses

  • 3rd semester at UiS or Exchange Studies

    • Courses at UiS 3rd semester

      • Choose one course

      • Recommended electives 3rd semester

        • DAT605: Reinforcement Learning

          Year 2, semester 3

          Reinforcement Learning (DAT605)

          Study points: 5

        • DAT640: Information Retrieval and Text Mining

          Year 2, semester 3

          Information Retrieval and Text Mining (DAT640)

          Study points: 10

        • ELE510: Image Processing and Computer Vision

          Year 2, semester 3

          Image Processing and Computer Vision (ELE510)

          Study points: 10

        • STA530: Statistical Learning

          Year 2, semester 3

          Statistical Learning (STA530)

          Study points: 10

      • Other electives 3rd semester

        • DAT530: Discrete Simulation and Performance Analysis

          Year 2, semester 3

          Discrete Simulation and Performance Analysis (DAT530)

          Study points: 10

        • DAT620: Project in Computer Science

          Year 2, semester 3

          Project in Computer Science (DAT620)

          Study points: 10

        • ELE680: Deep Neural Networks

          Year 2, semester 3

          Deep Neural Networks (ELE680)

          Study points: 5

        • STA500: Probability and Statistics 2

          Year 2, semester 3

          Probability and Statistics 2 (STA500)

          Study points: 10

    • Exchange 3rd semester

Student exchange

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. a period of study abroad is facilitated. Abroad, you must choose courses that provide an equivalent specialization 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. One tip is to think about your specialization 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.

Find out more

Contact your student adviser at the Faculty if you have questions about guidance and pre-approval of topics: Sheryl Josdal

General questions about exchange:

Go to the exchange guide in the Digital student service desk

Student exchange

  • All countries

    Aalborg Universitet

    Aalborg Universitet (AAU) er kjent for å benytte seg av problembasert læring i grupper, noe som kan by på en spennende læringsprosess.

    Grenoble Institute of Technology

    Bli Erasmus+-student og studer i de franske alper i Frankrikes beste studentby Grenoble!

    Lodz University of Technology

    Do you want to study at one of the best technical universities in Poland? Apply to Lodz University of Technology (TUL) and enjoy a 70 years long tradition and experience in Engineering education!

    Politecnico di Milano University

    Politecnico di Milano er Italias største tekniske universitet med om lag 40.000 studenter og er høyt rangert på en rekke internasjonale rankinglister.

    RWTH Aachen University

    Er du på utkikk etter en spennende mulighet i Tyskland er RWTH Aachen University det naturlige valget! Universitetet streber etter å bli det beste tekniske universitetet i Tyskland og er på god vei til målet.I tillegg er de høyt rangert innen økonomi. Bli med på en del av reisen – bli utvekslingsstudent i Aachen!

    Technical University of Munich

    The Technical University of Munich, also known as TUM, accounts for major advancements in the field of natural sciences. TUM is one of the best universities in Germany and has several awarded scientists and Nobel Prize winners. The Technical University of Munich strives for excellent teaching and research quality.

    The University of Adelaide

    Universitetet ligger i Adelaide, Australias femte største by. Med sine 1.2 millioner innbyggere er Adelaide en trygg, kosmopolitisk by som er betraktelig rimeligere å bo i enn flere sammenlignbare byer i landet. Universitetet er medlem av Group of Eight, en koalisjon av de åtte ledende universitetene i Australia.

    University of Pisa

    Study at one of Europe's oldest and most prestigious universities - founded as early as 1343.

    University of Twente, Enschede

    Opplev Europa og det internasjonale studiemiljøet i Nederland. University of Twente er UiS` partneruniversitet i ECIU-nettverket og tilbyr utvekslingsmuligheter for mange studenter ved UiS. Det er et moderne og innovativt campus-universitet som satser stort på entreprenørskap.

  • Australia

    The University of Adelaide

    Universitetet ligger i Adelaide, Australias femte største by. Med sine 1.2 millioner innbyggere er Adelaide en trygg, kosmopolitisk by som er betraktelig rimeligere å bo i enn flere sammenlignbare byer i landet. Universitetet er medlem av Group of Eight, en koalisjon av de åtte ledende universitetene i Australia.

  • Danmark

    Aalborg Universitet

    Aalborg Universitet (AAU) er kjent for å benytte seg av problembasert læring i grupper, noe som kan by på en spennende læringsprosess.

  • Frankrike

    Grenoble Institute of Technology

    Bli Erasmus+-student og studer i de franske alper i Frankrikes beste studentby Grenoble!

  • Italia

    Politecnico di Milano University

    Politecnico di Milano er Italias største tekniske universitet med om lag 40.000 studenter og er høyt rangert på en rekke internasjonale rankinglister.

    University of Pisa

    Study at one of Europe's oldest and most prestigious universities - founded as early as 1343.

  • Nederland

    University of Twente, Enschede

    Opplev Europa og det internasjonale studiemiljøet i Nederland. University of Twente er UiS` partneruniversitet i ECIU-nettverket og tilbyr utvekslingsmuligheter for mange studenter ved UiS. Det er et moderne og innovativt campus-universitet som satser stort på entreprenørskap.

  • Polen

    Lodz University of Technology

    Do you want to study at one of the best technical universities in Poland? Apply to Lodz University of Technology (TUL) and enjoy a 70 years long tradition and experience in Engineering education!

  • Tyskland

    RWTH Aachen University

    Er du på utkikk etter en spennende mulighet i Tyskland er RWTH Aachen University det naturlige valget! Universitetet streber etter å bli det beste tekniske universitetet i Tyskland og er på god vei til målet.I tillegg er de høyt rangert innen økonomi. Bli med på en del av reisen – bli utvekslingsstudent i Aachen!

    Technical University of Munich

    The Technical University of Munich, also known as TUM, accounts for major advancements in the field of natural sciences. TUM is one of the best universities in Germany and has several awarded scientists and Nobel Prize winners. The Technical University of Munich strives for excellent teaching and research quality.

Admission 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.

Contact information

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

Study Adviser: Sheryl Josdal.