Data Science is defined by many as the handling of data, which includes managing, curing, preparing, analysing, and making the data meaningful for an appropriate audience. Each type of audience prefers different levels and methods of communication from data analysis.

Medicine is the branch of science that deals with the diagnosis, treatment, alleviation, and control of disease. The two sciences are not mutually exclusive. Medical science needs data to make diagnoses and select appropriate interventions for patients. The better and more detailed the data, the better the diagnosis and treatment. Recently, tools based on medical data science have improved treatment and communication between specialists. Also, some AI systems can act as second opinions to help physicians avoiding mistakes. In the same way, data science also learns how medicine interprets data and uses it to develop a computer model that mimics the way the physician or medical professional interprets the data in their head.

In data science, we need both statistical knowledge and techniques to process huge amounts of data to connect and use health data from sometimes more than a million records. On the medical side, we obviously need medical knowledge, which we have. We also need to understand the needs and dynamics of healthcare environments. We need to also create some spaces for change. To connect or combine data science and medical science, we need to understand both sides very well. We have established a number of collaborations with various organisations inside and outside the country. We hope this will facilitate and foster research and innovation in medical data science at our Faculty.