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Master's degree (2 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.

Apply here

Application deadline:

April 15th
International applicants:
December 1st/April 15th

 

Start of Studies:

Autumn

Number of students:

25

Requirements:

Bachelor's degree

  • Requirements in detail

    Higher Education Entrance Qualification and a completed bachelor's degree comparable to a Norwegian bachelor's degree, including a specialization worth 80 ECTS.

    Applicants must have courses in Mathematics, Statistics and Informatics of at least 40 ECTS, of which at least 20 ECTS in Mathematics (Calculus, Linear Algebra), at least 5 ECTS in Statistics and at least 5 ECTS in Informatics (Programming). There is a minimum mark requirement of C for admission.

    English Language requirements for English taught programmes: www.nmbu.no/en/studies/language-requirements-programmes-taught-english-nmbuLanguage requirements for programmes taught in English at NMBU | NMBU

En kvinne viser en mann noe på en dataskjerm i et rom med flere dataskjermer.
Masterprogrammet i datavitenskap ved NMBU ble kåret til Norges beste masterprogram innen informasjons- og datateknologi i Studiebarometeret 2019.

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. 

Read about what it's like to be a student at NMBU.

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 
  • Learning outcomes

    Learning outcomes

    Upon completion, the candidates should be able to convey and communicate engineering related problems and solutions to both specialists and non-professionals.Be able to contribute to innovation and entrepreneurship.Complete an independent project, restricted to engineering related research- or development under supervision.

    Knowledge:

    Understand the engineering sciences overall role in a societal perspective, show insight into ethical requirements and respect to sustainable development, and be able to analyze ethical problems regarding engineering related work.Have a broad knowledgebase in mathematics, natural sciences, technology and computer technology as a foundation for understanding methods, applications, professional innovation and adaptations.Have deep knowledge in a defined area connected to active research, including an adequate professional understanding in using new research.Knowledge of computer safety, law and ethics.

    Skills:

    Be able to develop overall solutions to engineering related problems, including creating solutions in a multidisciplinary context. Be able to evaluate tools for analyzing, methods, technical models, calculations and solutions independently and critically.Plan and bring about data collection, as well as process, analyze and interpret the data. Collect and organize data, execute a multivariate analysis of high-dimensional data, pattern recognition and machine learning, evaluate the quality of data as well as the results.Be able to analyze mathematical models for processes e.g. in physics, biology, technique
  • Program structure

    Programming, computer science and data mining will give you basic skills in Data Science. Linear algebra and statistics are offered as optional courses if you lack these subjects in your bachelor degree. You will complement this basis with two specialisation courses in one of the following 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 and Economics. In your 30 ECTS master thesis, you will apply your data science competence to a specific task and hone your problem-solving skills.

    Study structure

  • More about the program
    • Societal relevance
      Modern societies produce large amounts of data.Data Sciences deal with the challenges to extract information from this data by combining the disciplines informatics, mathematics, data analysis and statistics.
    • Learning activities

      The programme may include 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-modelsExercises from previous exams or other relevant assignmentsLaboratory analysesSeminar 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
Karianne Risvik Johnsen

Karianne Risvik Johnsen

Advisor