Data Science

Data Science

Master of science (2 year) Full time

Being an expert in the field of data science makes you highly attractive in today’s and the future labour market.

Being an expert in the field of data science makes you highly attractive in today’s and the future labour market.

Photo
shutterstock

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 Study structure
Requirements

Bachelor's degree

Application deadline:

April 15th
International applicants:
December 1st

Number of students
25
Study start
Autumn (august)
See more
Contact study advisors
Send e-mail
Contact info

Admission office:
sit@nmbu.no
Phone: +47 67 23 01 11

 

Study advisor at REALTEK:
Drop-in counseling

 

 

 

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. 

 

Job 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

This master’s programme combines the disciplines of informatics, mathematics, statistics and data analysis. You will deepen your knowledge in these subjects and learn how to analyse real-world problems related to complex and big data. Furthermore, you will capitalise on the wide range of application areas of big data in technology, environmental- and life sciences, as well as in business and economics. 

Admission requirements

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.

 

All applicants must fill out the following application form, listing their motivations for applying to the MSc in Data Science as well as documenting how they fulfill the above mentioned admissions requirements. You do not need to submit an additional "motivational letter", there is a text box for this in the form. Applicants who fail to submit the application form within the deadline (15 December) will not be considered. 

Application form - Data Science

I chose NMBU because of the many options within the study program, and due to the famous student environment.

Elise Mühlbradt, BaneNOR (Jernbaneverket)

Study structure

Study content

Social 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 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

Contents and 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.

Learning activities:

The programme may include the following methods:

  • Lectures
  • Project assignments, e.g. semester assignment presented to the class
  • While working on their master thesis, joint meetings are arranged where the students present their work
  • Demonstrations
  • Group work in relation to topics, methods, computer-models
  • Exercises from previous exams or other relevant assignments
  • Laboratory analyses
  • Seminar participation
  • Excursions and study trips

Evaluation:

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.

Admission requirements:

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.

Cooperation with other institutions:

Arrangements, incoming exchange students: