Data Analytics and Artificial Intelligence in Health Sciences

New technologies based onArtificial Intelligence and Machine Learningare transforming the fields of medicine and life sciences. When considering the latestadvancements in information technology applied to health sciences, biotechnology and medical research– such as the development in "omics" data, imaging, electronic health records and digitalized biology applications – the massive volume of data that these new technologies generate every day can be surprising.

However, these data mines are often underutilized due to the challenges associated with their analysis, leading to the emerging need for experts who can bridge the gap between data science and health sciences, while taking advantage of innovations, such as partial automation of healthcare processes and the development of new patient-oriented tools and services. To address these unmet demands from hospitals, patients, research centers and companies, Bocconi University and Humanitas University have joined forces to develop a newMaster of Science in Data Analytics and Artificial Intelligence in Health Sciences.

Thanks to theBocconi-Humanitas partnership, the program provides multidisciplinary training in statistics, mathematics, computer science, and medicine-biology. The ultimate goal is to improve patient care and quality of life, advance scientific research, harness the value of data within the health sciences and meet the challenges presented when analyzing such data.

The two-year MSc, in English, specifically focuses on theintersection between data science, medicine and life sciences, aiming to develop professionals with advanced knowledge indata analytics, machine learning, artificial intelligenceand all the knowledge necessary to apply these advanced skills within hospitals, research laboratories, companies and regulatory bodies. Graduates are expected to possess the analytical skills valuable in health sciences. They will master data analysis combined with an understanding of the peculiarities of healthcare data, national healthcare systems, and the ethical and regulatory implication of collaboration between academia and industry.