General Describtion
The Master aims to fill a gap in the Data Science and STEM fields by integrating theoretical tools and empirical methods for a conscious approach to data analysis, scientific experimentation, use of simulation tools in scientific inference and forecasting, and evaluation of evidence for policy purposes. The uniqueness of the Master’s training offer is linked to three aspects that are not sufficiently developed in the current education landscape:
- The integration of courses and tutorials on advanced data analysis and inferential techniques (machine learning, deep learning, AI) — as well as tutorials on some of the most widespread data processing tools (Python, STATA, R, Matlab) — with courses dedicated to the foundations of the scientific method, epistemology and philosophy of science. This allows to put inferential methodologies into perspective, relate them among each other, and contributes to their conscious use. A particular focus is directed towards the theoretical foundations of the scientific method.
- Emphasis on the distinction between pure “truth-condicive” aims and strategic goals in the scientific practice. Formal analysis of strategic interactions in the scientific ecosystem as well as political and economic analysis of science in society. Identification of the various scientific sub-systems (scientific, governmental, socio-economic institutions and society at large), and their joint work within broader socio-economic structures.
- Policy-making and the role of scientific evidence in decision-making, both personal and collective, with particular attention to the debate on the so-called “Evidence-based policy” and the related political and civil implications.
The professional profile that the Master aims to form is multifaceted and of various backgrounds: the Master is aimed at students and scholars from both the human sciences and STEM disciplines, but also at professionals who want to enrich their skills in the field of data analysis, science epistemology, evidence-based policy. The figure that emerges is essentially that of a data analyst, with a rich methodological and foundational background. The Master can very well also contribute to enriching the educational profile of journalists, politicians and professionals in any sector (from economic to healthcare to legal ones).
At the end of the Master course the student will be able to evaluate the best scientific methodology to use for his own investigation; to analyze data and studies of others in their specific sector of research, and to offer consultancy services to policy-makers. Journalists and political decision-makers will have acquired the critical tools to orient themselves in the supply of information produced in the various scientific sectors.
Program
Part A Winter Semester – Part B Winter Semester
| Course | Lecturer | Hours |
|---|---|---|
| Foundations of the Sciences | Barbara Osimani | |
| Foundations of Econometrics I | Claudia Pigini | |
| Artificial Intelligence & Logic Programming I | Aldo Dragoni | |
| Fundamentals of Machine Learning | ||
| Bayesian Inference | Eric-Jan Wagenmakers | |
| Introduction to Epidemiology | Rosaria Gesuita | |
| Principles of Systematic Reviews and Meta-analysis | Marica Iommi | |
| Experimental Study Design | Edlira Skrami | |
| Study protocol and Sample Size Estimation | Andrea Faragalli | |
| Statistical Schools: Concepts of Probability, Statistical Inference, and Data Analysis |
Part A Summer Semester
| Course | Lecturer | Hours |
|---|---|---|
| Bayesian Philosophy of Science | Stephan Hartmann | |
| Formal Epistemology I | Michal Sikorski | |
| Formal Epistemology II | Alexander Gebharter | |
| Time-series forecasting with Deep Learning | Alessandro Galdelli | |
| Foundations of Econometrics II | Claudia Pigini | |
| Rationality in the Sciences | Barbara Osimani | |
| Beyond Inferential Statistics: Abduction and Q Methodology | Raffaele Zanoli | |
| Casual Inference | Alexander Gebharter |
Part B Summer Semester
| Course | Lecturer | Hours |
|---|---|---|
| Imprecise Probabilities | ||
| Rational Choice Theory | ||
| Economics of Science and Technology | Nicola Matteucci | |
| Economics of Regulation in Science-Based Domains | Nicola Matteucci | |
| Artificial Intelligence & Logic Programming II | Aldo Dragoni | |
| Time Series Econometrics | Giulio Palomba | |
| Integrity of Research | Andrea Saltelli | |
| Ethics of Quantification | Andrea Saltelli | |
| Questionnaire development: How to collect data from surveys. Do’s and Don’ts | Simona Naspetti |