Course and Teaching Information 2024-2025

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 of 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
• Cognition and Brain Function (continues in period 2; recommended for year 2)
• Computational Statistics
• Computer Vision (recommended for year 2)
• Inverse Problems 1: Convolution and Deconvolution (recommended for year 2)
• Software Architectures (recommended for year 2)
• 2nd Data Science Seminar (available in year 2)

• Bayesian Data Analysis
• Cognition and Brain Function (continues from period 1; recommended for year 2)
• Distributed Systems (recommended for year 2)
• Human Computer Interaction (recommended for year 2)
• Trustworthy Machine Learning (recommended for year 2)
• 2nd Data Science Seminar (continued, year 2)

• Computational Methods I
• Information Retrieval
• Network Analysis
• Probabilistic Cognitive Modelling
• Sustainability in Computer and Data Sciences I (2 cr)
• Sustainability in Computer and Data Sciences II (3 cr)

• Advanced Bayesian Inference
• Advanced Course in Machine Learning
• Data Warehousing and Business Intelligence *
• Interactive Data Visualization
• Neural Networks and Deep Learning

* Given every other academic year (not lectured in 2024-25)

• Cognitive Modelling Concepts
• High-dimensional Statistics
• Machine Translation
• Models and Algorithms in NLP Applications
• Philosophy of Artificial Intelligence
• Programming Parallel Computers (MOOC)