The Master is a full-time, on-campus program running from January to July 2027, followed by an internship or project work lasting four to six months. Active participation is mandatory, with a minimum attendance requirement of 80%.
Classes are held Monday to Friday, for approximately 7 hours per day (9:30 a.m.–1:00 p.m. and 2:00–5:30 p.m.). The program is entirely taught in English and combines lectures, hands-on laboratories, applied seminars, and teamwork activities.
Courses
Statistics for Data Science – 6 ECTS
This course introduces the core concepts and terminology of modern statistical thinking for data science. Topics include statistical foundations, exploratory data analysis, probability and sampling, statistical inference, and simulation-based methods. Computational approaches are used to build intuition for variability, inference, and reproducibility.
Applied Python Programming – 5 ECTS
The course focuses on Python as the core programming language for data science and analytics. Through a combination of theory and hands-on laboratories, students develop practical skills in writing efficient, modular, and reusable code to support data analysis, automation, and modeling tasks.
Digital Transformation Management – 4 ECTS
This course explores the managerial, organizational, and strategic foundations of digital enterprises operating in fast-changing, technology-driven environments. It emphasizes how digital technologies—such as data-driven systems, platforms, and AI-based solutions like AI agents—are transforming business models, value creation, and strategic decision-making.
Data Management and SQL for Data Science – 4 ECTS
The course introduces principles and practices of data management for analytics. Topics include relational databases, SQL querying and transformation processes. Students learn how to efficiently manage and query large datasets, including the use of AI-assisted tools to support SQL development and Python integration.
Data Visualization and Storytelling – 4 ECTS
This course equips students with the skills to transform data into clear, compelling visual narratives. Students learn the full workflow from data manipulation, to creating informative visualizations and producing reproducible, publication-ready reports. Tools used include Python (pandas and polars), plotnine, Quarto, and Git/GitHub.
Statistical Learning and AI for Data Science – 8 ECTS
This core course provides a comprehensive introduction to statistical learning and AI methods for data science. Topics include supervised learning (regression, classification), unsupervised learning (clustering, dimensionality reduction), model assessment, regularization, tree-based methods, and deep learning. Students gain hands-on experience building, evaluating, and deploying the most appropriate models for real-world business problems.
Large Language Models and Generative AI – 6 ECTS
The course provides an in-depth introduction to Large Language Models and generative AI technologies. Students explore the principles behind modern generative models, their integration into data-driven workflows, and their applications in areas such as text analysis, automation, and decision support for business.
Applied Business Intelligence – 4 ECTS
This course focuses on data-driven decision support systems and business intelligence applications. Students learn how to create and transform analytical outputs into actionable insights, with practical applications in areas such as performance monitoring, demand segmentation, and predictive analytics.
Hands-on Lab on Data Science and AI – 6 ECTS
This final, project-based course integrates program knowledge through real-world business cases. It features two lab components: a Data Science Lab, which emphasizes data exploration and analysis, and an AI Lab, focused on building, training, and evaluating machine learning and AI models. Working in teams, students apply these methods to real-world scenarios, enhancing their problem-solving, analytical thinking, and collaboration skills while gaining hands-on experience in addressing practical business challenges.
Internship and Project work
The final four to six months of the Master’s program are dedicated to applying and consolidating learning outcomes. Students are expected to complete an internship with a related thesis or develop and defend a project-oriented Master’s thesis.
Assessment and Diploma
To obtain the Master’s Diploma, students are required to successfully pass the final exam for each course, actively participate in project work, and complete either an internship or a Master’s thesis defense.