Classes run from Monday to Friday, 5 hours per day between 9 a.m. and 2 p.m. and the master programme is delivered on a full-time basis.
 

Course activities	Hours
Classes*	360
Individual study*	1040
Internship and final report	100
Total	1500

 
*20 hours of classes and 20 hours of individual work weekly under the supervision of a tutor.

   
Assessment and diploma

To obtain the Master diploma students are required to: successfully pass an exam at the end of each course; effectively participate in the project works; serve a four-month internship and defend a Master thesis.

Preparatory Courses

• Management - 2 ECTS
• Statistics - 3 ECTS
• Basics of Programming
   

Courses

Data management and warehousing (4 ECTS)

The course illustrates how to implement and technically maintain a data warehouse. The focus is on database data design, extraction, profiling, and standardisation along with data transformation. The course provides comprehensive coverage of SQL to handle big datasets; AI assistants to generate SQL code are presented.

 

Software development and coding with Python (5 ECTS)

The course focuses on software development with Python, with a mix of theory, hands-on laboratories and common business use cases analysis. Students will gain broad and deep software development skills to be able to independently write procedures and functions to expand and automate data analysis studies and results.

 

Statistics and the R software (6 ECTS)

The course aims to present advanced concepts of statistical inference for empirical research, both at a univariate and multivariate level. Real data applications will be an integral part of the course. The basics of the R software for statistical computing, data analysis, and inference will also be presented.

 

Management for digital enterprise (7 ECTS)

The course illustrates the business characteristics and managerial skills of a Digital Enterprise in rapidly evolving markets, including how enterprises are reshaped due to AI-Agents. At the end of the course students will be able to
understand the importance of ensuring that Digital Enterprise initiatives for their evolution and growth have clear business objectives, operating models, and the right mix of enablers (technology, data, change management).

 

Data Visualisation and Storytelling with R and SAS (4 ECTS)

The course covers the basics of data visualization and exploratory data analysis. Tailored R and SAS libraries are presented and discussed. We will be using several data visualization libraries in R / SAS. In particular, within the R
environment, the dplyr and ggplot packages will be introduced for data manipulation, exploration, cleaning and for advanced graphical representations. Methods will be exemplified on real-world cases based on economic and
financial data, among others, and stressing the importance of sharing information through narratives, in order to leave a lasting impact on the stakeholders.

 

Data and text mining (5 ECTS)

The Data Mining part of this course focuses on step-by-step instructions for the entire data
modelling process, with special emphasis on the business knowledge necessary to successfully use statistical models. Text mining, on the other hand, addresses data extraction from the web by applying classification and clustering techniques on unstructured data. Students are introduced to key phrase retrieval and filtering
methods. Practical applications on web information extraction and text categorisation are presented. Additionally, students are trained to obtain the "Machine Learning with SAS Viya" certification.

 

Statistical learning for Data Science (6 ECTS)

The purpose of this course is to provide the students with an introduction to the main techniques for statistical learning and computational methods, including cross validation, regularisation strategies, regression, classification, and clustering. Moreover, students are introduced to Knowledge graphs that are an important tool for organising and representing complex information in a way that can be easily understood and used by both humans and machines, and their integration with cutting-edge AI models like Large Language Models and Generative
Pre-trained Transformer (GPT). Participants will gain insights into the role of semantic technologies in navigating complex data landscapes, enhancing natural language processing tasks, and advancing AI capabilities through structured knowledge representation. A particular attention will be devoted to the Explainability perspective in AI and Ethics.

 

Business Intelligence and Predictive Analytics (5 ECTS)

This course illustrates the usage of data and analytics in modern business activities. The main focus is on Database Marketing in a multidimensional framework. Demand Segmentation and Scoring Models will be the practical applications.

The last four months of the programme are dedicated to enhancing and applying learning outcomes. Students are expected to either: serve an internship, typically with a partner company, and write a thesis related to their activity or write and defend a project-oriented Master thesis under the supervision of a scholar.

The 1-year Specialising Master is organized in collaboration with:

Scientific Director
Guido Consonni
 

Executive Coordinator
Riccardo Bramante

Executive  Board

• Riccardo Bramante,  Adjunct Professor of Business Statistics, Università Cattolica del Sacro Cuore
• Guido Consonni, Professor of Statistics, Università Cattolica del Sacro Cuore
• Stefano Peluso, Assistant professor, Università Cattolica del Sacro Cuore
• Federico Rajola, Professor of Corporate Organization and Head of CeTIF and  ILAB, Università Cattolica del Sacro Cuore
• Alberto Saccardi, Founding partner, Nunatac

Teaching staff

• Giuseppe Arbia, Università Cattolica del Sacro Cuore
• Matteo Borrotti, Energia Crescente
• Riccardo Bramante, Università Cattolica del Sacro Cuore
• Marco Cerri, Sky Italia
• Mauro Minella, Microsoft
• Stefano Peluso, Università Cattolica del Sacro Cuore
• Alberto Saccardi, Nunatac
• Gionata Tedeschi, Independent consultant

Graduates of the 1-year Specialising Master in Data Science for Management are ideally suited to fill jobs in the Data Analytics area across a variety of industries, ranging from ICT to consulting, from banking and finance to insurance. 

Classes began on January 19, 2026. The master runs until December 2026, on a full-time basis. 
 

Active attendance is mandatory. A minimum of 80% attendance is required. 

Classes run from Monday to Friday, 7 hours per day, 9:30 am – 1:00 pm and 2:00 – 5:00 pm. 

The graduation ceremony will be in the first week of February 2027.

International students can join Italian language courses offered by SeLdA (Servizio Linguistico d’Ateneo). These courses require a fee. 

• You can contact SeLdA via email: selda.lingueper@unicatt.