Studies

Master's degree (5 years)
Full time
Data Science

Do you want to solve the challenges of tomorrow by using big data? Our master’s programme in data science combines the theoretical elements of informatics, mathematics and statistics with practical data analysis and problem solving skills.

Application deadline:

April 15th
International applicants:
December 1st

Start of Studies:

Autumn 2023

Number of students:

25

Requirements:

Bachelor's degree

  • Requirements in detail
    Higher Education Entrance Qualification + SIVING. The study program is taught in Norwegian and applicants who do not have Norwegian, as their native language must document sufficient Norwegian skills to be admitted.
Venndiagram som viser datavitenskap, informatikk og domenekunnskap
Fotografert august 2024. Studentambassadører fotografert en augustdag på campus Ås, i og utenfor Kassa nova. Bildene er til bruk i rekruttering, og alle personene på bildene har gitt tillatelse til bruk.
Fakultet for realfag og teknologi Studenter og veileder fotografert i undervisningsrom på Sørenga. Samtykke fra studentene er innhentet.

With this master’s degree you will be proficient in machine learning, data mining and programming. Moreover, we offer optional courses ranging from applied data science to mathematical and statistical foundations of data science.

You will complement the basic components of your degree with two courses in one of the following specialised areas: 

  • Geographical Modelling and Analysis  
  • Geographical Database Systems  
  • Geographical Data Mining  
  • Climate Modelling Biostatistics  
  • Biophysics  
  • Bioinformatics  
  • Computational Neuroscience  
  • Energy Physics  
  • Building Structures  
  • Building Performance Simulation  
  • Economics  

Lastly, you will apply your data science competencies on a specific task and hone your problem-solving skills in your 30 ECTS master thesis. 

 

Career opportunities

Being an expert in the field of data science makes you highly attractive in today’s and the future labour market. Big data challenges are present in all modern societies, industry sectors and the academic sector. These challenges are also expected to grow rapidly in the future. With an MSc in Data Science, you are eligible for a meaningful, creative and varied technical job in a range of industries, such as: Communications Energy Economics Precision agriculture Construction Science and education Medicine Read interviews with some of our former students here.

  • Learning outcomes

    The data science program is designed to blend strong academic foundations with practical application. We foster our students' understanding of advanced data science concepts to apply them effectively in various academic and industrial settings with an awareness of ethical and sustainable practices.

    In this program, students develop a robust foundation in theoretical and applied aspects of data science. The curriculum is designed to foster:

    • Effective communication of complex data science principles to both specialist and lay audiences, enabling graduates to act as liaisons in multidisciplinary environments.
    • The initiative for innovation and entrepreneurial activities provides a platform for students to become leaders in technological environments.
    • The capability to conduct independent, supervised research and development projects, laying the groundwork for future academic or industry-focused pursuits.
    • In-depth competence in area of application (specialization).

    Academically, candidates will:

    • Gain a comprehensive understanding of the societal implications of data science, informed by an appreciation of ethical considerations and the principles of sustainable development.
    • Acquire state-of-the-art knowledge of data science techniques, ensuring a well-rounded background in methodology and professional adaptability.
    • Acquire specialized knowledge linked to active research areas, facilitating the integration of new research findings into professional practice.

    In terms of skills, students will:

    • Engineer solutions to complex problems, demonstrating the ability to synthesize solutions in an interdisciplinary context.
    • Independently evaluate data analysis tools, methods, and technical models, fostering a critical approach to data science solutions design and implementation.
    • Master data acquisition, processing, and interpretation, including the ability to conduct high-dimensional data analysis, pattern recognition, and machine learning.
    • Apply mathematical models to describe and solve problems across various disciplines.
    • Acquire practical technological and methodological skills, ensuring immediate and effective contributions in professional settings.
  • Exchange possibilities
    A study abroad period can be included in the third or fourth academic year.
  • Program structure

    Programme contents and structure

    All students must take the following courses:

    • Introduction course 10 credits
    • Examen Philosophicum 10 credits
    • Mathematics 30 credits
    • Informatics 10 credits
    • Physics 20 credits
    • Statistics 10 credits
    • Economics and Social studies 10 credits
    • Informatics- and Computer-Science 60 credits
    • Specializations, including project assignments and a master thesis in applied Computer-Science 120 credits

  • More about the program
    • Societal relevance

      The social relevance of this program is defined by its commitment to equip you with the knowledge and skills necessary to address some of the challenges of our time. You will:

      • Drive sustainable data science solutions with an awareness of their societal, environmental, and ethical implications.
      • Contribute to technological advancement while adhering to sustainable practices.
      • Combine comprehensive academic knowledge with hands-on research to significantly contribute to the current data science challenges.
        ​
    • Learning activities

      The programme includes the following methods:

      LecturesProject assignments, e.g. semester assignment presented to the classWhile working on their master thesis, joint meetings are arranged where the students present their workDemonstrationsGroup work in relation to topics, methods, computer-models Exercises from previous exams or other relevant assignmentsLaboratory analyses Seminar participationExcursions and study trips
    • Examination
      Some courses have a written examination, while others have an oral examination. Grades (A-F) are mostly used, but also passed/failed in some courses. Other courses have a longitudinal evaluation, where several components make up the foundation for the final grade. In the final part of the programme, the students will do an independent assignment that shall show their understanding, reflection and maturity.

Study advisor(s):

Rune Grønnevik

Rune Grønnevik

Senior Advisor
Marie Vollset

Marie Vollset

Advisor
Nora Sunde

Nora Sunde

Advisor