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 - 2 ECTS
 

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. A detailed analysis of big data quality management is provided.

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. While presenting the foundational theoretical concepts, real data applications will be discussed. The course also introduces the basics of the R software for statistical computing, data analysis, and inference.

Management for digital enterprise (7 ECTS)
The course illustrates the business characteristics of a Digital enterprise along with the impact of a Digital enterprise on the customer experience. At the end of the course, students will be able to understand the importance of ensuring that Digital enterprise initiatives have clear business objectives and identify the relationships of Digital enterprise with specific enablers (Digital marketing, Analytics and Customer Relationship Management).

Data visualisation 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.

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 hypertext documents. Students are introduced to information retrieval and filtering methods. Practical applications on web information extraction and text categorization are presented.

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, regularization strategies, regression, classification, and clustering. All methods will be introduced from a theoretical and applied perspective. Moreover, students are introduced to Knowledge graphs that are an important tool for organizing and representing complex information in a way that can be easily understood and used by both humans and machines. By representing knowledge as a network of interconnected entities and relationships, knowledge graphs provide a powerful framework for modelling complex domains and enabling sophisticated analysis.

Business intelligence and data analytics (5 ECTS)
This course illustrates the usage of data and analytics in modern business activities. The main focus is on data preparation to create suitable multidimensional database marketing frameworks. Demand segmentation and scoring models will be practical applications.

The last four months of the Master programme are dedicated to enhance and apply 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 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 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 for academic year 2024-25 begin on January 20, 2025. The master runs until December 2025, 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. 

If you are looking for a flexible and autonomous solution to learn Italian while studying at Università Cattolica, you can access the self-learning linguistic lab (CAP) of the SeLdA (Servizio Linguistico d’Ateneo) and benefit from a personalized language advisory service. 

In addition, SELDA organizes fee-paying Italian language courses, for more information contact SELDA.