Data Science

Nowadays, data is more important than ever. Companies such as Netflix, Amazon and Spotify collect user data to generate relevant predictions and recommendations about what their users will enjoy in the future. In order to analyse data produced by large-scale scientific experiments, advanced machine learning algorithms and data engineering platforms are needed. Many authorities also strive to collect and integrate data from several different types of sensors (air quality, sound, water distribution, and such). For this, a profession plays a pivotal role in turning information into insight: the data scientist.

The subject data science includes the general skills needed by a data scientist, as well as advanced skills in machine learning, reinforcement learning, computer-intensive statistics, data mining, data engineering, accelerator-based programming, and security. During the Master's Programme in Data Science, one can expect to understand the importance of efficient and effective data processing, learn how to make advanced statistical analyses, gain a robust understanding of both the mathematical foundations of data science and its computational aspects, work alongside internationally renowned research teams in data science, and delve into the ethical and legal aspects of data science.

The labour market forecasts for data analysts are good, and the profession has long had strong growth. Data science is one of the most sought-after competencies in Sweden and internationally, and basic research conducted at the University has a great need to use the data generated by facilities such as SciLifeLab and the MAX IV laboratory.

Programmes

Master's Programme in Data Science – Data Engineering Master's Programme in Data Science – Machine Learning and Statistics

Courses

Master's level courses

Applied Linear Algebra for Data Science, 7.5 credits Computer-Intensive Statistics and Applications, 10 credits Computer-Intensive Statistics and Data Mining DS, 7.5 credits Data Mining, 7.5 credits Data, Ethics and Law, 5 credits Foundations of Data Science, 10 credits High Performance and Parallel Computing, 7.5 credits Introduction to Data Science, 10 credits Large Language Models and Societal Consequences of Artificial Intelligence, 5 credits Mathematical Modelling of Football, 5 credits Mathematical Topics in Data Science, 7.5 credits Mining of Social Data, 10 credits Optimisation, 5 credits Reinforcement Learning, 7.5 credits Security and Privacy, 7.5 credits Statistical Machine Learning, 5 credits Theoretical Foundations for Data Science, 7.5 credits Topics in Data Science, 5 credits