Data Science Degree Structure 2020-2023

The obligatory core studies in the programme consist of

(a) 20 credits worth of key data science courses:

• Introduction to Data Science
• Introduction to Machine Learning
• Bayesian Data Analysis
• Distributed Data Infrastructures

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

• Data Science Seminar
• Data Science Project
• Academic Skills for Data Science
• Data Science Fest (a monthly meeting for data science staff and students)

You can specialise either in the core areas of data science -- algorithms, infrastructure 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. In addition to mainstream data science topics, the programme offers two largely unique opportunities for specialisation: the data science computing environment and infrastructure, and data science in natural sciences, especially physics.

Minor studies give you a wider perspective of Data Science. Your minor subject can be an application area of Data Science (such as physics 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 eleven elective courses from the list of data science specialisation courses below (20-55 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

• Advanced Course in Machine Learning
• Bayesian Machine Learning
• Computer Vision
• Introduction to Deep Learning
• Design and Analysis of Algorithms
• Information Retrieval
• Network Analysis
• Trustworthy Machine Learning

Stat­ist­ical Data Science

• Advanced Bayesian Inference
• Computational Statistics (before Computational Statistics I)
• High Dimensional Statistics
• Inverse Problems 1: Convolution and Deconvolution
• Spatial Modelling and Bayesian Inference
• Survival and Event History Analysis I
• Time Series Analysis I

Data Science In­fra­struc­tures

• Big Data Platforms
• Cloud and Edge Computing
• Numerical Methods in Scientific Computing (before Scientific Computing III)
• Tools of High Performance Computing

Com­puters and Cog­ni­tion

• Introduction to Artificial Intelligence
• Interactive Data Visualization
• Cognition & Brain Function
• Introduction to the Philosophy of Mind and Artificial Intelligence
• Philosophy of Artificial Intelligence

Interdisciplinary Data Science

• Clinical Data Mining
• Introduction to Natural Language Processing (NLP) 
• Machine Translation
• Models and Algorithms in NLP Applications

In addition to the above required courses, you can include other courses in your degree as well, for up to 35 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 MSc programme accepts any of these courses. 
• Useful method courses are also offered by other programmes, for instance, in computer science, statistics and mathematics, language technology, digital humanities, life science informatics and computational social sciences.
• Studies in an application area of data science can be especially interesting, and can range from ecology to urban studies and from languages to global politics. See a list of all English master's programmes.
• You can also take courses in the international Helsinki Summer School, 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.