Studies of Data Science combine mathematics, statistics and informatics and covers the whole Data Science process. This includes defining the scientific goal, obtaining the data, exploring the data, building, fitting and validating models as well as communicating and vizualising the results. 

The University of Oslo offers both specialized studies within Data Science as well as a range of courses applying Data Science methodology to various fields.

Study programs


From the autumn of 2017, there is a specialization towards Statistics and Data Science within the Mathematics with informatics bachelor program. Within this program the first three semesters will contain fundamental topics in mathematics, informatics and statistics while the last three semesters will be more specialised towards Data Science. The program will include both theoretical aspects and demonstrations of the methodologies in many real cases. 

NEW!! From the autumn of 2018 there will be a master program in Data Science. This program will be for students with a solid background in mathematics, informatics and statistics. The program will focus on both the width and depth of various subjects. The master program has the following specializations:

  • Statistics and Machine Learning
  • Database Integration and Semantic Web
  • Data Science and Life Science

In addition to these specialized Data Science programs, Data science related courses are offered within many other programs. Some relevant courses are seen on the right panel.


Data Science courses

The list below has a focus here on courses containing machine learning. In addition there will be many courses within statistics, data base management, bioinformatics, language processing, robotics that are related to Data Science issues. See the lists of courses within the Department of mathematics and Department of Informatics for further details.

  • STK2100 - Machine learning and statistical methods for prediction and classification
    • Introductory course to machine learning with statistical viewpoint. Based on knowledge in mathematics and statistics. Well suited for students within mathematics bachelor program
  • IN3050 - Introduction to Artificial Intelligence and Machine Learning
    • Introductory course i machine learning and AI with an algorithmic approach. Well suited for bachelor students with good informatics background.
  • STK-INF3000/4000 - Selected Topics in Data Science
    • The course provides insight into selected contemporary relevant topics within Data Science.  Well suited for students at the end of bachelor/start of master level within mathematical or informatics programmes.
  • FYS-STK3155 – Applied data analysis and machine learning
    • The course introduces a variety of central algorithms and methods essential for studies of data analysis and machine learning. Require a good background in mathematics and some programming skills.
  • IN4080 – Natural Language Processing
    • Probabilistic and machine learning techniques applied to natural language processing. Well suited for students aiming at a master  in informatics: Language Technology and in Data Science.
  • STK-IN4300 – Statistical learning methods in Data Science
    • An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background. 
  • INF4490 - Biologically Inspired Computing
    • An introduction to self-adapting methods also called artificial intelligence or machine learning. Well suited for bachelor students with good informatics background.
  • IN-STK5000 – Adaptive Methods for Data-Based Decision Making
    • Methods for adaptive collection and processing of data based on machine learning techniques. Based on knowledge in mathematics, statistics and programming.
  • IN5400 – Maskinlæring for bildeanalyse/INF5860 – Machine Learning for Image Analysis
    • An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too. Well suited for master/PhD students with good informatics  background or students with good background in mathematics and some Python programming.
  • TEK5040 – Dyp læring for autonome systemer
    • The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments. 

      This course is well suited for master/PhD students who already have basic knowledge in linear algebra, programming and machine learning, including prior familiarity with neural networks.

  • TEK 5020 Mønstergjenkjenning
    This course covers Bayesian classifiers, supervised classification, parametric, and non-parametric methods, linear discriminant functions,  and selected unsupervised methods. 
    The course is in Norwegian. The course is suited for Master/PhD students with a backround in linear algebra. A background in stastitics is not required.