Course and Teaching Information 2026-2027

The academic year at the University of Helsinki is divided into four actual teaching periods and some intensive periods. The current locations of Data Science courses for Academic year 2026-2027 are given in the model schedules of studies below. The lists of specialisation courses and additional courses as well as their locations can still be changed.

Note also that the specialisation courses in Curriculum 2026-2030 and Curriculum 2023-2026 are exactly not the same!
Model schedules of studies
Model schedule 1 (students starting their studies in 2026)

This is a model schedule for students who have not studied that much machine learning before starting in the programme.

Year 1, Period 1
  • Data Science
  • Statistics for Data Science
  • Data Science Study Skills (continues in Period 2)
  • Academic Writing I (continues in Period 2)
  • 0-1 course from List A of specialisation courses
Year 1, Period 2
  • Engineering of Machine Learning Systems
  • Machine Learning 1
  • Choose:
    • Practical Machine Learning OR
    • 0-1 course from List B of specialisation courses
  • Data Science Study Skills (continues from Period 1)
  • Academic Writing I (continues from Period 1)
Year 1, Period 3
  • Data Science Project (continues in Period 4)
  • Data Science Seminar (continues in Period 4)
  • 2 courses from List C of specialisation courses
Year 1, Period 4
  • Data Science Project (continues from Period 3)
  • Data Science Seminar (continues from Period 3)
  • 2 courses from List D of specialisation courses
In­tens­ive period and sum­mer courses
  • After Period 4, in May, intensive courses and pop up courses are offered on varying topics
  • An internship can be carried out in the summer, counting towards the degree
  • Helsinki Summer School and the Open University also offer some courses during summer
Year 2, Period 1
  • (5 out of 30 cr)
  • Master thesis seminar (continues in Period 2)
  • 2-3 courses from List A of specialisation courses OR optional studies
Year 2, Period 2
  • (5 out of 30 cr; continues)
  • Master thesis seminar (continued from Period 2)
  • 2-3 courses from List B of specialisation courses OR optional studies
Year 2, Period 3
  • (10 out of 30 cr; continues)
  • 1 course from List C of specialisation courses OR optional studies
Year 2, Period 4
  • (10 out of 30 cr; continues)
  • 1 course from List D of specialisation courses OR optional studies
Model schedule 2 (students starting their studies in 2026)

This is a model schedule for students who have already completed some studies in Machine Learning.

Year 1, Period 1
  • Data Science
  • Statistics for Data Science
  • Data Science Study Skills (continues in Period 2)
  • Academic Writing I (continues in Period 2)
  • 0-1 course from List A of specialisation courses
Year 1, Period 2
  • Engineering of Machine Learning Systems
  • Choose:
    • Machine Learning 1 and 0-1 course from List B of specialisation courses OR
    • 1-2 courses from List B of specialisation courses
  • Data Science Study Skills (continues from Period 1)
  • Academic Writing I (continues from Period 1)
Year 1, Period 3
  • Data Science Project (continues in Period 4)
  • Data Science Seminar (continues in Period 4)
  • 2 courses from List C of specialisation courses
Year 1, Period 4
  • Data Science Project (continues from Period 3)
  • Data Science Seminar (continues from Period 3)
  • 2 courses from List D of specialisation courses
In­tens­ive period and sum­mer courses
  • After Period 4, in May, intensive courses and pop up courses are offered on varying topics
  • An internship can be carried out in the summer, counting towards the degree
  • Helsinki Summer School and the Open University also offer some courses during summer
Year 2, Period 1
  • (5 out of 30 cr)
  • Master thesis seminar (continues in Period 2)
  • 2-3 courses from List A of specialisation courses OR optional studies
Year 2, Period 2
  • (5 out of 30 cr; continues)
  • Master thesis seminar (continued from Period 2)
  • 2-3 courses from List B of specialisation courses OR optional studies
Year 2, Period 3
  • (10 out of 30 cr; continues)
  • 1 course from List C of specialisation courses OR optional studies
Year 2, Period 4
  • (10 out of 30 cr; continues)
  • 1 course from List D of specialisation courses OR optional studies
Model schedule 3 (students who started their studies in 2025 or earlier)
Year 1, Period 1
  • (continues in Period 2)
  • (continues in Period 2)
  • 0-1 course from List A of specialisation courses
Year 1, Period 2
  • (continues from Period 1)
  • (continues from Period 1)
  • 0-1 course from List B of specialisation courses
Year 1, Period 3
  • (continues in Period 4)
  • Data Science Seminar (continues in Period 4)
  • 2 courses from List C of specialisation courses
Year 1, Period 4
  •  (continues from Period 3)
  • Data Science Seminar (continues from Period 3)
  • 2 courses from List D of specialisation courses
In­tens­ive period and sum­mer courses
  • After Period 4, in May, intensive courses and pop up courses are offered on varying topics
  • An internship can be carried out in the summer, counting towards the degree
  • Helsinki Summer School and the Open University also offer some courses during summer
Year 2, Period 1
  • (5 out of 30 cr)
  • 2-3 courses from List A of specialisation courses OR optional studies
Year 2, Period 2
  • (5 out of 30 cr; continues)
  • 2-3 courses from List B of specialisation courses OR optional studies
Year 2, Period 3
  • (10 out of 30 cr; continues)
  • 1 course from List C of specialisation courses OR optional studies
Year 2, Period 4
  • (10 out of 30 cr; continues)
  • 1 course from List D of specialisation courses OR optional studies
Specialisation courses
List A of specialisation courses in Period 1
  • Big Data Platforms (continues in Period 2; recommended for Year 2)
  • Cognitive Modelling Concepts (continues in Period 2; recommended for Year 2)
  • Computational Statistics (recommended for Year 2)
  • Computer Vision (recommended for Year 2)
  • Inverse Problems 1: Convolution and Deconvolution (recommended for Year 2)
  • Software Architectures (recommend for Year 2)
  • 2nd Data Science Seminar (available in Year 2)
List B of specialisation courses in Period 2
  • Bayesian Data Analysis (recommended for Year 2)
  • Big Data Platforms (continues from Period 1; recommended for Year 2)
  • Cognition and Brain Function (recommended for Year 2)
  • Cognitive Modelling Concepts (continues from Period 1; recommended for Year 2)
  • Distributed Systems (recommended for Year 2)
  • Human Computer Interaction (recommended for Year 2)
  • Machine Translation (recommended for Year 2)
  • Trustworthy Machine Learning (recommended for Year 2)
  • 2nd Data Science Seminar (continued, Year 2)
List C of specialisation courses in Period 3
  • Computational Methods I
  • Cognition and Brain Function
  • High-dimensional Statistics
  • Information Retrieval
  • Network Analysis
  • Philosophy of Mind, Language and AI
  • Probabilistic Cognitive Modelling
  • Sustainability in Computer and Data Sciences I (2 cr)
  • Sustainability in Computer and Data Sciences II (3 cr)
List D of specialisation courses in Period 4
  • Advanced Bayesian Inference
  • Data Warehousing and Business Intelligence (*)
  • Data Science for the Internet of Things
  • Interactive Data Visualization
  • Machine Learning 2
  • Neural Networks and Deep Learning

(*) Given every other academic year (not lectured in 2027)
Specialisation courses (MOOCs or teaching period currently unknown) and other advanced courses

Specialisation courses

  • Computational Affective Modelling I (2 cr; MOOC)
  • Computational Affective Modelling II (3 cr; MOOC)
  • Factor Analysis and Structural Equation Models
  • Machine Learning for Social Sciences
  • Multilingual Natural Language Processing
  • Natural Language Processing of Social Sciences
  • Network Analysis for Social Sciences
  • Programming Parallel Computers (MOOC)
  • (Social) Theory and (Social) Data Science
  • Text as Data

Other optional advanced Data Science courses

  • Data Science for Monitoring Aquatic Ecosystems (MOOC)
  • Gaussian Processes for Machine Learning
  • Probabilistic Graphical Models
Data Science Seminars
Autumn 2026

As Data Science seminars you can take any of the seminars offered by the Master's Programme in Data Science or some of the seminars offered by the Master's Programme in Computer Science or the Master's Programme in Life Science Informatics.

Seminars of the Master's Programme in Data Science, Autumn 2026

  • Seminar on Human-Centered Data Science

Seminars of the Master's Programme in Computer Science, Autumn 2026

  • Seminar on Big Data Management
  • Seminar on Empirical Software Engineering (?)

Seminars of the Master's Programme in Life Science Informatics, Autumn 2026

  •  
Spring 2027

As Data Science seminars you can take any of the seminars offered by the Master's Programme in Data Science or some of the seminars offered by the Master's Programme in Computer Science or the Master's Programme in Life Science Informatics.

Seminars of the Master's Programme in Data Science, Spring 2027

  • ???

Seminars of the Master's Programme in Computer Science, Spring 2027

  • Seminar on the Internet of Things
  • Seminar on Algorithms (???)
Other courses offered by the programme

Every academic year, the programme offers also some additional courses (). These courses cannot, however, counted as a part of the 20 credits of specialisation studies in Data Science in the degree structure of Curriculum 2026-2030. Instead, they can be a part of the optional other studies in the master's degree. In 2026-2027, such courses are, for example,

  • AI in Society: Introduction (MOOC; 1.5 cr)
  • AI in Society: AI and Democracy (MOOC; 0.5 cr)
  • AI in Society: AI and Discrimination (MOOC; 0.5 cr)
  • AI in Society: AI and Disinformation in the Digital Age (MOOC; 0.5 cr)
  • AI in Society: AI, Justice and Security (MOOC; 0.5 cr)
  • AI in Society: AI and One Health (MOOC; 0.5 cr)
  • AI in Society: AI and Privacy (MOOC; 0.5 cr)

In the degree structure of Curriculum 2023-2026, courses 

  • AI in Society: Introduction (MOOC; 1.5 cr)
  • AI in Society: AI and Democracy (MOOC; 0.5 cr)
  • AI in Society: AI and Discrimination (MOOC; 0.5 cr)
  • AI in Society: AI and Disinformation in the Digital Age (MOOC; 0.5 cr)
  • AI in Society: AI, Justice and Security (MOOC; 0.5 cr)
  • AI in Society: AI and One Health (MOOC; 0.5 cr)
  • AI in Society: AI and Privacy (MOOC; 0.5 cr)
  • Computational Affective Modelling I (MOOC; 2 cr)
  • Computational Affective Modelling II (MOOC; 3 cr)

cannot be counted as a part of the 25 credits of specialisation studies. Instead, they can be a part of the optional other studies in the degree. 
More about the programme