Master SDIFS

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:

1. 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.

2. 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.

3. 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

CourseLecturerHours
Foundations of the SciencesBarbara Osimani
Epistemology IMichal Sikorski
Epistemology IIAlexander Gebharter
Causation and ProbabilitiesAlexander Gebharter
Tutorial: Introduction to STATA for Data
Analysis
Riccardo Cappelli
Tutorial: PYTHONAdriano Mancini
Tutorial: R & MatlabFederico Giri
Risk and Decision-Making for Data
Science and AI
Norman Fenton
The Philosophy of Evolutionary TheoryElliot Sober

Part B Winter Semester

CourseLecturerHours
Foundations of Econometrics IClaudia Pigini
Artificial Intelligence & Logic Programming IAldo Dragoni
Fundamentals of Machine
Learning
Bayesian InferenceEric-Jan
Wagenmakers
Introduction to EpidemiologyRosaria Gesuita
Principles of Systematic
Reviews and Meta-analysis
Marica Iommi
Experimental Study DesignEdlira Skrami
Study protocol and Sample Size
Estimation
Andrea Faragalli
Statistical Schools: Concepts of Probability,
Statistical Inference, and Data Analysis

Part A Summer Semester

CourseLecturerHours
Bayesian Philosophy of ScienceStephan
Hartmann
Formal Epistemology IMichal
Sikorski
Formal Epistemology IIAlexander
Gebharter
Time-series forecasting with
Deep Learning
Alessandro
Galdelli
Foundations of Econometrics IIClaudia
Pigini
Rationality in the SciencesBarbara
Osimani
Beyond Inferential Statistics:
Abduction and Q Methodology
Raffaele
Zanoli
Casual InferenceAlexander
Gebharter

Part B Summer Semester

CourseLecturerHours
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 EconometricsGiulio
Palomba
Integrity of ResearchAndrea
Saltelli
Ethics of QuantificationAndrea
Saltelli
Questionnaire development:
How to collect data from
surveys. Do’s and Don’ts
Simona
Naspetti