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Linköping University

Statistics and Machine Learning, Master's Programme - Second admission round (open only for Swedish/EU students)

Unleash the power of data and statistics to make the right decisions happen. The rapid development of information technologies has overwhelmed society with enormous volumes of information generated by large or complex systems from telecommunications, robotics, medicine, business and many other fields. This master’…

  • Higher education
  • Information unavailable
  • 24 August 2026
  • Information unavailable
  • Information unavailable
  • 100 %

Overview

Unleash the power of data and statistics to make the right decisions happen. The rapid development of information technologies has overwhelmed society with enormous volumes of information generated by large or complex systems from telecommunications, robotics, medicine, business and many other fields. This master’s programme meets the challenges of learning from these complex volumes by means of models and algorithms from machine learning, data mining and other computer-intensive statistical methods. By joining us, you will increase the efficiency and productivity of the systems and make them smarter and more autonomous. We integrate statistical modelling and analysis with machine learning, data mining and data management to give you unique skills. The programme focusses on modern methods from machine learning and database management that use the power of statistics to build efficient models and make reliable predictions and optimal decisions. You will gain deep theoretical knowledge as well as practical experience from extensive amounts of laboratory work. Depending on your interests, you will work towards your thesis at a company, a governmental institution or a research unit at LiU. There you can apply your knowledge to a real problem and meet people who use advanced data analytics in practice or you can go deeper into the research. There is a rapidly increasing demand for specialists who are able to exploit the new wealth of information in large and complex systems. Business, telecommunications, IT and medicine are just a few examples of areas where you will be wanted for advanced analytical positions. You will also be well prepared for an academic career, should you choose to continue into research and pursue PhD studies.

Admission scores

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Entry requirements

Bachelor's degree equivalent to a Swedish Kandidatexamen in one of the following subject areas: -statistics -mathematics -applied mathematics -computer science -engineering or a similar degree Completed courses with passing grade in following subjects: - calculus - linear algebra - statistics - programming English corresponding to the level of English in Swedish upper secondary education (Engelska 6 or Engelska nivå 2) Exemption from Swedish

The text is reproduced from the Susa source. Antagningsdata does not map GY11 and GY25 or assess personal eligibility.

Source, measure and data quality
Source
Skolverket Susa-navet
Period
2026-08-24
Measure
Entry-requirement text reproduced from the published Susa data; no personal eligibility assessment is made.
Population
Education offering e.uoh.liu.f7mml.90006.20262
Last checked
2026-09-23T10:38:02.783888+00:00
Limitation
Antagningsdata does not map GY11 and GY25. General and specific conditions are not separated without structured source data.

Programme content

Uppgift saknasVerified data is not connected to this education offering.

Study structure

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Application and important dates

  1. Programme or course starts
  2. Programme or course ends

Salary and salary distribution

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Common occupations after graduation

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Students

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Geographical background

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Previous upper-secondary schools and programmes

Uppgift saknasVerified data is not connected to this education offering.

Completion and outcomes

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About the provider

Linköping University

Provider for the published education offering.

Sources and data quality

Education facts for the selected offering come from Skolverket Susa-navet.

Retrieved . Published . Times are shown in Swedish local time.

Source identity and publication version
Publication version
8e217193-f5fa-4778-b085-a4521fd03e8d
Education identity in the source
i.uoh.liu.f7mml.90006.20262
Offering identity in the source
e.uoh.liu.f7mml.90006.20262
Education-form source code
HS
Education code in the source
F7MML
Change time according to the source
2025-11-14T09:26:15

The provider, education and education offering are separate identities. Application information should be checked on the official website. Supplementary statistics have not been obtained from this source.