Program Overview

Program Overview

The Master's program in Big Data: Statistical Methods for the Knowledge Society aims to provide advanced training in the use of statistical methods and techniques for Big Data analysis. The program focuses on enhancing the quality of existing data sources, adopting new data collection methodologies, and integrating databases and statistical surveys.

Program Details

• ID: 29034
• Course Type: Master
• Academic Year: 2025/2026
• Degree Level: M2
• Length: Annual
• Tuition Fee: 6000
• CFU: 60

Admission Criteria

• The program has a limited number of places available, with 5 positions reserved for students who meet the requirements of the PA 110 e lode protocol.
• The tuition fee for students admitted under the PA 110 e lode protocol is 5400.
• The deadline for submitting applications is January 31, 2026.

Educational Goals

The program aims to equip students with advanced skills in statistical methods and techniques for Big Data analysis, with a focus on:

• Improving the quality of existing data sources
• Adopting new data collection methodologies
• Integrating databases and statistical surveys
• Using Open Data in the Public Administration
• Analyzing trends and processes of change in demographic, socio-economic, and health fields
• Developing predictive capabilities for business, administrative, and policy decisions
• Evaluating the impact of investment choices in public and private contexts

Program Structure

The program includes a range of courses and activities designed to provide students with a comprehensive understanding of Big Data analysis and statistical methods. The program is structured around the following areas:

• Data Collection: Manual and automatic data collection techniques
• Data Analysis: Statistical methods and techniques for Big Data analysis
• Data Integration: Integrating databases and statistical surveys
• Open Data: Using Open Data in the Public Administration

Research Areas

The program focuses on the following research areas:

• Big Data analysis
• Statistical methods and techniques
• Data collection and integration
• Open Data in the Public Administration
• Predictive analytics
• Impact evaluation

Faculty and Department

• Director: Fiorenza Deriu
• Department: Scienze statistiche
• Faculty: Facoltà di Ingegneria dell'informazione, informatica e statistica

Location

• Location: Roma, Italia