Unit Data Science for the quality of institutions
- Course
- Government and administration
- Study-unit Code
- A001477
- Curriculum
- Governo della democrazia e sostenibilita'
- Teacher
- Michela Gnaldi
- Teachers
-
- Michela Gnaldi
- Hours
- 60 ore - Michela Gnaldi
- CFU
- 8
- Course Regulation
- Coorte 2025
- Offered
- 2026/27
- Learning activities
- Affine/integrativa
- Area
- Attività formative affini o integrative
- Sector
- SECS-S/05
- Type of study-unit
- Obbligatorio (Required)
- Type of learning activities
- Attività formativa monodisciplinare
- Language of instruction
- Italian
- Contents
- The course, which belongs to the quantitative methodological area, aims to provide the methodological foundations for measuring complex phenomena, with particular reference to corruption.
The course is organised into two parts:
i. a first, mainly lecture-based part covering the theoretical foundations of the quantitative analysis of big data for assessing the quality of public institutions (e.g., general principles and potential of data science; traditional and big data sources; objectives and analytical tools of data mining and statistics; construction and validation of elementary statistical indicators; construction and validation of composite indicators; data visualisation tools and techniques, etc.);
ii. a second, more applied part in which students will be guided in the development of a project work, consisting of a short report presenting the results of analyses carried out by students on real-world data obtained from open-source databases, such as the National Database of Public Contracts (BDNCP) managed by the Italian National Anti-Corruption Authority (ANAC). - Reference texts
- Three reference texts will be adopted. The chapters to be studied will be specified during the course. Lecture slides and all supplementary teaching materials will be made available through the Unistudium platform. Specific compensatory and dispensatory measures will be provided for students with disabilities and/or learning disorders (DSA).
1. Fighting Corruption in Emergency Procurement through Big Data, edited by Michela Gnaldi, FrancoAngeli.
2. Measuring Corruption Today: Objectives, Methods and Experiences, edited by Michela Gnaldi and Benedetto Ponti.
3. Open Issues in Composite Indicators: A Starting Point and a Reference on Some State-of-the-Art Issues, by Adrian Otoiu, Adriano Pareto, Elena Grimaccia, Matteo Mazziotta and Silvia Terzi. - Educational objectives
- The course aims to provide methodological foundations for the management, analysis and evaluation of complex phenomena, with particular reference to data mining and traditional statistical methods as tools for assessing public policies, institutional integrity and corruption risk.
The course contributes to training professionals capable of applying quantitative methodologies to improve public policies and public administrations, in line with the objectives of the Degree Programme. The skills acquired are intended to support effective performance in managerial positions, both in the public and private sectors, and to address challenges related to democratic governance, public management, territorial policies and sustainability. - Prerequisites
- Previous completion of an undergraduate-level statistics course is desirable
- Teaching methods
- The course is primarily based on face-to-face lectures supported by multimedia tools. In the second part of the course, four classes will be devoted to the use of Excel for the calculation of corruption risk indicators required for the project work.
Throughout the semester, review exercises will also be conducted at the end of each topic. These exercises include both general questions and applied problems aimed at assessing students’ understanding of the topics covered and their ability to apply concepts to practical situations. Exercises are usually corrected and discussed in class within one week of their completion.
These exercises do not constitute an assessment tool and do not contribute to the final grade. - Other information
- None
- Learning verification modality
- Assessment consists of a written examination and a project work.
The written examination lasts one hour and thirty minutes and is designed to assess students’ knowledge of the main concepts and methods covered during lectures.
The project work is carried out independently by students during the final part of the course, under the guidance of the instructor. It consists of a report of approximately 25–30 pages presenting:
i. the results of analyses aimed at calculating corruption risk indicators;
ii. the interpretation of those results.
The project work assesses students’ ability to apply the concepts and methods learned in class to real-world contexts.
Specific compensatory and dispensatory measures will be provided for students with disabilities and/or learning disorders (DSA). - Extended program
- Part I – General Issues in the Measurement of Complex Latent Phenomena and Specific Issues in Corruption Measurement
Data Sources
• Official statistics and other unofficial data sources.
• Big data: defining characteristics and differences between traditional data and big data.
• Informational potential, opportunities and limitations of traditional and big data for public and private uses.
• Data sources on corruption.
• Informational potential of administrative hard data for corruption measurement.
Data Mining as the Main Methodological Tool of Data Science
• Objectives of data mining.
• Main analytical tools for public policy evaluation, including:
o association analysis;
o correlation;
o regression analysis;
o classification techniques;
o clustering methods (including composite indicators);
o sequential pattern discovery.
From Statistics to Indicators
• Definition of hierarchical designs and measurement models.
• Indicator systems at the macro level.
• Managing and synthesising complexity through:
o data reduction techniques;
o indicator combination;
o indicator modelling.
Indicators for Measuring Complex Phenomena
• Corruption indicators: characteristics, strengths and limitations.
Part II – Student Project Work: “Corruption Risk in Public Procurement”
Students work individually on different samples of data extracted from the National Database of Public Contracts (BDNCP) managed by ANAC. The objective is to construct, analyse and interpret corruption risks within specific procurement markets, geographical areas or time periods using a longitudinal perspective. - Obiettivi Agenda 2030 per lo sviluppo sostenibile
- Peace, Justice and Strong Institutions (16)