Master’s Degree in Data Analytics and Artificial Intelligence in Health Sciences (DAIHS)

Joint Degree with Bocconi University

New technologies based on Artificial Intelligence and Machine Learning are transforming the fields of medicine and life sciences. When considering the latest advancements 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 new Master of Science in Data Analytics and Artificial Intelligence in Health Sciences.

Thanks to the Bocconi-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 the intersection between data science, medicine and life sciences, aiming to develop professionals with advanced knowledge in data analytics, machine learning, artificial intelligence and 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.

In recent years, AI and machine learning have made enormous advances in the field of medicine, opening up new perspectives and opportunities to improve the diagnosis and treatment of diseases. ​These technologies are expected to bring further revolutionary advances to this field, while also allowing full advantage to be taken of telemedicine technology and networks in an increasingly integrated pathway between hospital and community.

The Joint Bocconi-Humanitas MSc in Data Analytics and Artificial Intelligence in Health Sciences aims to offer an integrated and innovative perspective in these areas, in line with the unique needs of the market that increasingly demands multidisciplinary skills.

Students who choose this new joint program will have the chance to learn from outstanding faculty in Bocconi’s Departments of Computing Sciences and Decision Sciences, as well as researchers at Humanitas Research Hospital. These experienced professors will share their expertise and knowledge during the program’s cutting-edge coursework.

The MSc will train comprehensive professionals who are ready to be employed by health technology companies, hospitals and healthcare organizations, as well as research institutions, pharmaceutical and biotechnology industries, health analytics companies, and regulatory bodies. After graduation, they will be able to apply their knowledge to make a positive impact on society, with a specific focus on health.

Our Partnership

In today’s rapidly changing world, there is an increasing demand for healthcare organizations to integrate the big data available to them into their daily operations. This has created a need for professional figures with an understanding of both the healthcare sector and the theoretical and practical knowledge required to implement AI and machine learning methods.

This is why Bocconi University and Humanitas University have joined forces to offer the Master of Science in Data Analytics and Artificial Intelligence in Health Sciences, with the aim of preparing these professionals for the job market. The new MSc offers a novel mix of skills and disciplines, benefitting from the distinctive specializations of both Universities.

The Bocconi-Humanitas partnership intends to leverage the extensive medical, biological and healthcare experience in medicine provided by Humanitas University and its joint hospital networks – including the flagship Humanitas Research Hospital – and the extensive experience in AI, statistics and data science from Bocconi University and Bocconi’s Departments of Computing Sciences and Decision Sciences.

The two-year program is based on a complementary blend of the two University’s strengths. The result is an interdisciplinary and well-balanced foundation that will allow participants to apply the latest technology within the complexity of modern healthcare settings.

Programme Structure

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.

1^st 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:

1^st bimester – Bocconi University

Foundational courses in Advanced Statistics for the Health Sciences, and Advanced Computer Programming.

2^nd 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.

3^rd 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.

4^th bimester – Humanitas University

Focus on biological and clinical knowledge.

2^nd 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 Objective

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.

Career Opportunities

The role of Data Scientist in Health Sciences

Graduates in this role will effectively extract, analyze, model and interpret health data through the application of state-of-the-art analytical techniques derived from statistics, machine learning and artificial intelligence, to obtain answers useful for scientific research. They will also interpret clinical-diagnostic-therapeutic pathways, understand the demands of clinicians and basic researchers, as well as identify software tools needed for clinical and biological data processing and analysis. Lastly, they will design and conduct scientific studies in the field of medicine and health sciences by collaborating effectively with health professionals and researchers from different disciplines.

They may be employed by a variety of employers, including: research institutions, pharmaceutical and biotechnology industries, health technology companies, public bodies and government institutions, hospitals and healthcare organizations, startups in the healthcare industry, consulting and professional services and research institutions.

Graduates of the program will be able to:

• Design and implement a complete process of statistical analysis of health data, from acquisition to extraction of the information of interest, with a special focus on methods and algorithms from Machine Learning and Artificial Intelligence
• Build predictive models from data
• Design and develop software to perform the analysis and interpret the results of health data analysis
• Represent and communicate the results from analyses
• Describe and implement procedures for the protection of data quality, privacy and intellectual property