Data Science Degree Structure 2026-2030

The MSc degree in Data Science consists of 120 credits divided into core courses, data science specialisation courses, and other courses, as described below.
Core studies

The obligatory core studies in the programme consist of

(a) 25 credits worth of key data science courses:

  • Data Science
  • Statistics for Data Science
  • Engineering of Machine Learning Systems
  • Two of the following courses:
    • Machine Learning 1
    • Machine Learning 2
    • Neural Networks and Deep Learning
    • Practical Machine Learning

(b) 15 credits worth of courses on professional skills in data science:

  • Data Science Seminar
  • Data Science Project

In addition to that the programme organises events called Data Science Fests, which are meetings for data science staff and students.
Specialisation studies

You can specialise either in the core areas of data science -- machine learning and artificial intelligence algorithms, data engineering and statistics -- or in its applications. This means that you can focus on the development of new models and methods in data science, supported by the data science research carried out at the University of Helsinki; or you can become a data science specialist in an application field by incorporating studies in another subject.

Minor studies give you a wider perspective of data science. Your minor subject can be an application area of data science (such as physics, cognitive science or the humanities), a discipline that supports application of data science (such as language technology), or a methodological subject needed for the development of new data science methods and models (such as computer science, statistics, or mathematics).

You must pick at least four and at most nine elective courses from the list of data science specialisation courses below (20-45 credits). The courses are divided into thematic modules, but courses can be freely taken from any modules. Other data science courses may be offered as well, but only the ones listed below count towards the required four courses.

Ma­chine Learn­ing and Algorithms

  • Computer Vision
  • Machine Learning 2
  • Network Analysis
  • Neural Networks and Deep Learning
  • Trustworthy Machine Learning

Stat­ist­ical Data Science

  • Advanced Bayesian Inference
  • Bayesian Data Analysis
  • Computational Statistics
  • High-dimensional Statistics
  • Inverse Problems 1: Convolution and Deconvolution

Data Engineering

  • Big Data Platforms
  • Computational Methods I
  • Data Science for the Internet of Things
  • Data Warehousing and Business Intelligence
  • Distributed Systems
  • Programming Parallel Computers
  • Software Architectures

Cognition in Brain and Machines

  • Cognition and Brain Function
  • Cognitive Modelling Concepts
  • Computational Affective Modelling I
  • Computational Affective Modelling II
  • Philosophy of Mind, Language and AI
  • Probabilistic Cognitive Modelling

Natural Language Processing, Information Access and HCI

  • Human Computer Interaction
  • Information Retrieval
  • Interactive Data Visualization
  • Multilingual Natural Language Processing

Sustainability

  • Sustainability in Computer and Data Sciences I
  • Sustainability in Computer and Data Sciences II

Social Data Science

  • Factor Analysis and Structural Equation Models
  • Machine Learning for Social Sciences
  • Network Analysis for Social Sciences
  • Natural Language Processing for Social Sciences
  • (Social) Theory and (Social) Data Science
  • Text as Data
Other studies

In addition to the above required courses, you can include other courses in your degree as well, for up to 30 credits.

  • The Master's Programme in Data Science offers additional courses and seminars with varying topics.
  • The University of Helsinki offers an extensive range of courses across its eleven faculties. The Master's Programme in Data Science accepts any of these courses.
  • Useful method courses are also offered by other programmes, for instance, in , , , and .
  • Studies in an application area of data science can be especially interesting, and can range from to and from to . See .
  • You can also take courses in the international , organised each August.
  • An internship in a company or a research group can give you insight into the practice or science of data science. Work experience in data science can also earn you credits towards your degree.
More about the programme