Introduction

In recent years, decision-making in the public sector has become increasingly complex due to the various challenges that economies and societies pose to the role of regulating, funding, and providing public services. Governments and public sector organizations face a range of complex problems, such as rising healthcare costs, aging populations, and climate change, which require sophisticated and innovative solutions. In this context, the role of data analytics has become increasingly crucial.

Data analytics refers to the process of extracting meaningful insights and knowledge from data using various analytical and statistical techniques. It involves the use of advanced tools and technologies, such as machine learning algorithms, data mining, and predictive analytics, to analyze large and complex data sets. By leveraging the power of data analytics, public sector organizations can gain valuable insights into the needs and preferences of citizens, improve service delivery, and optimize resource allocation.

The use of data analytics in the public sector has already shown promising results in a variety of domains, such as healthcare, transportation, and public safety. For instance, data analytics has been used to predict and prevent disease outbreaks, optimize traffic flow in cities, and detect and prevent fraud in government programs. However, the potential of data analytics is far from being fully realized, and there are still many challenges and barriers to overcome.

The purpose of the thesis is to explore the potential of data analytics in the public sector, and to identify the key challenges and opportunities associated with its adoption. By examining case studies from different domains and countries, and by analyzing the literature on data analytics in the public sector, the thesis aims to provide a comprehensive overview of the state of the art in this field, and to offer insights and recommendations for public sector organizations seeking to leverage the power of data analytics. Moreover, and more specifically, the thesis might deal with specific cases of data analytics as examples in the domains of (i) educational and/or (ii) local government.

Theses’ activities and topics

Several MSc theses are available, in the framework of several projects that the research group is conducting, as for examples

1. Predicting students’ performance in primary and secondary schools using administrative data. This thesis topic involves using administrative data from the Ministry of Education, evaluation agencies, and school registers to predict students’ academic performance in primary and secondary schools. The aim is to develop predictive models that can identify students who are at risk of underperforming and provide targeted interventions to support their learning. The thesis may involve analyzing large and complex datasets using statistical and machine learning techniques, such as regression analysis, decision trees, and neural networks. The thesis may also involve exploring the factors that influence academic performance, such as socio-economic status, gender, and ethnicity.
2. Examining how entrance test is related to university students’ academic performance – at both bachelor and master level. This thesis topic involves examining the relationship between entrance test scores and university students’ academic performance at both the bachelor and master level. The aim is to understand the predictive power of entrance tests in predicting academic success and to identify the factors that may moderate this relationship, such as socio-economic background, gender, and prior educational achievement. The thesis may involve conducting a meta-analysis of existing studies that have examined this relationship, or collecting new data from universities and using statistical techniques to analyze the data.
3. Assessing the efficiency of schools around the world using international databases, and integrating indicators for cognitive and socio-emotional skills. This thesis topic involves assessing the efficiency of schools around the world using international databases and integrating indicators for both cognitive and socio-emotional skills. The aim is to develop a comprehensive framework for evaluating the effectiveness of schools in promoting student learning and well-being. The thesis may involve collecting and analyzing data from international databases, such as the Programme for International Student Assessment (PISA), and developing new indicators for socio-emotional skills, such as self-regulation, resilience, and social awareness. The thesis may also involve exploring the factors that influence school efficiency, such as teacher quality, school leadership, and community engagement.
4. Evaluating the efficiency of local governments using administrative data (public expenditures, quantity levels of service, etc.) and citizens’ satisfaction. This thesis topic involves evaluating the efficiency of local governments using administrative data on public expenditures, quantity levels of service, and citizens’ satisfaction. The aim is to develop a comprehensive framework for assessing the effectiveness of local government services and identifying areas for improvement. The thesis may involve analyzing large and complex datasets using statistical techniques, such as data envelopment analysis (DEA), to identify efficient and inefficient local governments. The thesis may also involve exploring the factors that influence citizen satisfaction, such as responsiveness, accountability, and transparency, and developing strategies for improving service delivery based on citizen feedback.