Year 1, Period 1

• Introduction to Data Science
• Data Science Study Skills
• Academic Writing I
• Statistics for Data Science
• 0-1 course from List A of specialisation courses

Year 1, Period 2

• Engineering Machine Learning Systems
• Introduction to Machine Learning
• Data Science Study Skills (continued)
• Academic Writing I (continued)
• 0-1 course from List B of specialisation courses

Year 1, Period 3

• Data Science Project
• Data Science Seminar
• 2 courses from List C of specialisation courses

Year 1, Period 4

• Data Science Project (continued)
• Data Science Seminar (continued)
• 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

• MSc Thesis (5 out of 30 cr)
• 2-3 courses from List A of specialisation courses OR optional studies

Year 2, Period 2

• MSc Thesis (5 out of 30 cr) (continued)
• 2-3 courses from List B of specialisation courses OR optional studies

Year 2, Period 3

• MSc Thesis (10 out of 30 cr) (continued)
• 1 course from List C of specialisation courses OR optional studies

Year 2, Period 4

• MSc Thesis (10 out of 30 cr) (continued)
• 1 course from List D of specialisation courses OR optional studies

• Big Data Platforms (continues on period 2)
• Cognitive Modelling Concepts (continues on period 2)
• Computational Statistics
• Computer Vision (recommended for year 2)
• Inverse Problems 1: Convolution and Deconvolution (recommended for year 2)
• Models and Algorithms in NLP Applications (recommended for year 2)
• Programming Parallel Computers (MOOC)
• Software Architectures (recommended for year 2)
• 2nd Data Science Seminar (available in year 2)

• Bayesian Data Analysis
• Big Data Platforms (continued from period 1)
• Cognitive Modelling Concepts (continued from period 1)
• Distributed Systems (recommended for year 2)
• Human Computer Interaction (recommended for year 2)
• Machine Translation (recommended for year 2)
• Programming Parallel Computers (MOOC)
• Trustworthy Machine Learning (recommended for year 2)
• 2nd Data Science Seminar (continued, year 2)

• Cognition and Brain Function
• Computational Methods I
• High-dimensional Statistics * (every other year)
• Information Retrieval
• Network Analysis
• Philosophy of Artificial Intelligence
• Probabilistic Cognitive Modelling
• Programming Parallel Computers (MOOC)
• Sustainability in Computer and Data Sciences I and II (2+3 cr)

* Given every other academic year (lectured in 2023-24)

• Advanced Bayesian Inference *
• Advanced Course in Machine Learning
• Data Warehousing and Business Intelligence *
• Interactive Data Visualization
• Neural Networks and Deep Learning
• Programming Parallel Computers (MOOC)

** Given every other academic year (lectured in 2023-24)

A list of courses whose placement is unclear.