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 standardization 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 useful 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 Visualization 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 categorization are presented. Additionally, students are trained to obtain the "Machine Learning with SAS Viya" certification.
Statistical Learning and AI 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. 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, 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 data driven 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.