Programme Structure
The study plan has been structured to merge disciplinary fields from data science with disciplinary fields from the medical-biological field.
This will allow advanced training in statistics, computer programming, machine learning, and artificial intelligence, to be combined with knowledge of biology, genetics, ethics, and regulation within the healthcare sector.
The study plan for DAIHS aims to develop a cultural and professional profile able to directly contributing to the improvement of both patients’ lives and healthcare organisations.
To reach this objective, the study plan has been structured to merge scientific disciplinary fields from the LM Data Science degree class, with scientific disciplinary fields from the medical-biological field. This will allow to combine deep training in advanced programming, statistics, Machine Learning, and Artificial Intelligence with solid knowledge of biology, genetics, ethics and specific regulation within the healthcare sector.
The course is structured over two years, and will be held entirely in English. International lecturers and experts with strong professional experience abroad are part of the faculty of the DAIHS course.
Students will also have the opportunity to learn in international experiences as part of the development of the dissertation.
The 1st year is mainly focused on providing the necessary knowledge in advanced programming, statistics, Machine Learning and Artificial Intelligence, and due to these characteristics is mainly based in Bocconi University. The 2nd year takes place at Humanitas University to immerge students into the reality of a large teaching hospital, working on biological and clinical data.
Study Plan
The first year is mainly focused on providing the necessary knowledge in advanced statistics, programming , machine learning and artificial intelligence, and it is mainly based at Bocconi University. The final part of the first year, and the whole second year, complements training mainly within Humanitas University, through an immersive, hands-on learning experience which includes the delivery of compulsory integrated teaching, elective exams, seminars, hands-on experiences and independent research.
Students will also have the opportunity to learn during international experiences as part of the development of their thesis and internship.
| LEARNING ACTIVITY | CREDITS | UNIVERSITY |
|---|---|---|
| Advanced Statistics for Health Sciences | 8 | Bocconi University |
| Advanced Computer Programming | 9 | Bocconi University |
| Artificial Intelligence – Module 1 | 6 | Bocconi University |
| Privacy, Ethics and Regulations in the Application of AI – Seminar | 2 | Bocconi University |
| Machine Learning | 8 | Bocconi University |
| Artificial Intelligence – Module 2 | 6 | Bocconi University |
| Data Systems in Healthcare | 6 | Bocconi University |
1 elective course out of:
|
6 | Bocconi University |
| Biology and Genetics | 4 | Humanitas University |
| Data Science for Clinics | 8 | Humanitas University |
| Clinical Epidemiology | 10 | Humanitas University |
| TOTAL I YEAR | 73 |
| LEARNING ACTIVITY | CREDITS | UNIVERSITY |
|---|---|---|
| Next Generation Sequencing and Bioinformatics | 7 | Humanitas University |
| Applications of Artificial Intelligence in Health Sciences | 10 | Humanitas University |
1 elective course out of:
|
6 | Humanitas University |
| Guidelines for Quality Assessment and Reporting in AI Publication – Seminar | 2 | Humanitas University |
| Foreign Language (1st sem) | 2 | Humanitas University |
| Internship | 6 | Humanitas University |
| Thesis | 14 | Humanitas University |
| TOTAL II YEAR | 47 | |
| TOTAL CREDITS | 120 |
1st Year
The first year takes place mainly at Bocconi University, with one final bimester held at Humanitas. This allows students to gain fundamental and core basic knowledge, in both data science and the medical-biological field.
The first year of the program is divided into four bimesters:
1st bimester – Bocconi University
Foundational courses in Advanced Statistics for the Health Sciences, and Advanced Computer Programming.
2nd bimester – Bocconi University
Foundational courses in Artificial Intelligence and Machine Learning, plus a specific focus on the privacy, ethical and regulatory issues associated with healthcare.
3rd bimester – Bocconi University
This bimester builds upon the core technical knowledge acquired in the first two bimesters, with additional training in artificial intelligence and on the functioning of data systems in healthcare; students will also have the opportunity to choose among different elective courses, including the topics of causal inference, natural language processing, and computer modelling.
4th bimester – Humanitas University
Focus on biological and clinical knowledge.
2nd Year
The whole second year takes place at Humanitas University, where students will take additional courses and will be exposed to problems within the reality of a large teaching hospital, working on biological and clinical data. Courses, seminars and labs focus on the application of advanced technologies in the clinical and biological sciences.
The second year allows students to personalize their path through hands-on learning experiences, such as electives, an internship and the thesis.
Learning Objectives
The MSc in Data Analytics and Artificial Intelligence in Health Sciences aims to provide students with theoretical and practical knowledge to be able to understand and implement AI and machine learning methods, while taking into account the complexity of healthcare data, within hospital and territorial enterprises, as well as clinical research institutes.
In particular, the program aims to provide:
- A solid understanding of statistical inference and modeling, and of machine learning principles and methods.
- Deep knowledge of programming, algorithms, databases, architecture and programming for small and large datasets in health.
- In-depth training in the area of new artificial intelligence techniques as applied to predictive and diagnostic models.
- In-depth training in other relevant, specific areas such as natural language processing, causal inference or computer modeling.
- Knowledge of the biological-health area with reference to biological (biology, genetics) and medical-health disciplines (human anatomy, physiology and pathology, aspects of clinical medicine, diagnostic imaging and radiology).
- Knowledge in epidemiology for the study of disease patterns, clinical data analysis, and in the impact of AI and machine learning on public health and specific patient populations and individuals.
- Knowledge of the healthcare system and the functioning of healthcare settings, as well as the associated healthcare databases.
- Problem-solving skills combined with the analytical skills necessary to identify the information technology and statistical components useful in solving problems peculiar to the medical-healthcare area with a focus on specific purposes in the patient-care setting.
- Knowledge of legal/ethic issues related to the management and protection of privacy and sensitive data in order to understand limits and conditions imposed by the law.