Unit Data Science for the quality of institutions
- Course
- Politics, administration, territory
- Study-unit Code
- A001477
- Curriculum
- Management delle amministrazioni e dei servizi
- Teacher
- Michela Gnaldi
- Teachers
-
- Michela Gnaldi
- Hours
- 63 ore - Michela Gnaldi
- CFU
- 6
- Course Regulation
- Coorte 2023
- Offered
- 2023/24
- Learning activities
- Affine/integrativa
- Area
- Attività formative affini o integrative
- Academic discipline
- SECS-S/05
- Type of study-unit
- Opzionale (Optional)
- Type of learning activities
- Attività formativa monodisciplinare
- Language of instruction
- Italian
- Contents
- The teaching, belonging to the quantitative methodological area, aims to offer the methodological basis for the measurement of complex phenomena, with particular regard to the phenomenon of corruption.
The course is organized in two parts:
i. a first mainly frontal part in which the theoretical foundations for the quantitative analysis of big data will be covered for the purpose of 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 synthetic indicators, data visualization tools and techniques, etc.)
ii. a second application part in which the students will be guided in the construction of a project work, i.e. a short report reporting the results of the analyzes (carried out by the students) of real data obtained from open source sources, such as the National Database of Public Contracts (BDNCP) held by the National Anti-Corruption Authority - Reference texts
- Misurare la corruzione oggi. Obiettivi, metodi, esperienze. A cura di
Michela Gnaldi, Benedetto Ponti. FrancoAngeli.
https://ojs.francoangeli.it/_omp/index.php/oa/catalog/book/310
OPEN ISSUES
IN COMPOSITE INDICATORS
A Starting Point and a Reference on Some State-of-the-Art Issues
Silvia Terzi, Adrian Otoiu, Elena Grimaccia, Matteo Mazziotta, Adriano Pareto - Educational objectives
- The teaching, belonging to the quantitative methodological area, aims at offering the methodological basis for the measurement of
complex phenomena, with particular regard to data mining and traditional statistical tools, such as
synthesis methods for the evaluation of public policies, of the integrity of institutions and the risk of corruption - Prerequisites
- Previous knowledge of introductory statistics is hoped pre-requisite
- Teaching methods
- Lectures, practical work and project work
- Other information
- none
- Learning verification modality
- Written exam, and project work
- Extended program
- PART I
General issues in measuring latent complex phenomena and specific issues in measuring corruption
The data sources
• Official statistics and other unofficial data sources. Big data, characterizing elements and differences between data and big data. Information potential, application margins and limits, use of traditional data and big data for public and private purposes.
• Sources of data on corruption. Potential of hard data from administrative sources for measuring corruption.
Data mining - the main methodological tool of data science
Objectives of data mining and main analytical tools useful for evaluating public policies, including: association, correlation, regression between variables; unit classification; variable clustering (composite indicators); sequential pattern discovery.
Development of measures: from statistics to indicators
Definition of the hierarchical design and of the measurement model. System of measures at the macro level: the systems of indicators. Complexity management and synthesis, including: data structure reduction; combination of indicators; modeling of indicators.
Indicators for measuring complex phenomena
Indicators for measuring corruption: characteristics, potential and limits
PART II Project work students
“The risk of corruption in public procurement”
The students work on a different sample of data obtained from the National Database of Public Contracts (BDNCP) held by the National Anti-Corruption Authority with the aim of constructing, analyzing and interpreting the risks of corruption in specific reference markets, in specific geographical areas of reference, or in periods of time in a longitudinal perspective. - Obiettivi Agenda 2030 per lo sviluppo sostenibile
- Peace, justice and strong institutions (16)