Year 1, Period 1

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

Year 1, Period 2

• Distributed Data Infrastructures
• 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 I
• Data Science Seminar I
• 2 courses from List C of specialisation courses

Year 1, Period 4

• Data Science Project I (continued)
• Data Science Seminar I (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
• Computational Statistics
• Computer Vision
• Design and Analysis of Algorithms
• Introduction to Artificial Intelligence
• Inverse Problems 1: Convolution and Deconvolution
• Models and Algorithms in NLP Applications
• Data Science Seminar II (available in year 2)

• Bayesian Data Analysis (substituting Statistics for Data Science)
• Clinical Data Mining
• Cognition and Brain Function (continued)
• High Dimensional Statistics * (every other year)
• Introduction to NLP
• Machine Translation
• Survival and Event History Analysis I
• Time Series Analysis I
• Trustworthy Machine Learning
• Data Science Seminar II (continued, year 2)

* Given every other academic year (lectured in 2021-22)

• Bayesian Machine Learning
• Cloud and Edge Computing
• Information Retrieval
• Introduction to Deep Learning
• Network Analysis
• Numerical Methods in Scientific Computing** / Tools of High Performance Computing* (alternating years)
• Philosophy of Artificial Intelligence
• Data Science Project II (year 2)

* Given every other academic year
** Given every other academic year (maybe given in 2022-23)

• Advanced Course in Machine Learning
• Interactive Data Visualization
• Numerical Methods in Scientific Computing** (continued)
• Data Science Project II (continued, year 2)

** Given every other academic year (maybe given in 2022-23)

• Spatial Modelling and Bayesian Inference / Advanced Bayesian Inference (alternating years)
• Introduction to the Philosophy of Mind and Artificial Intelligence