Preparatory Courses
Management - 2 ECTS
Statistics - 3 ECTS
SAS 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. 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 Visualization 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. 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 Data Analytics – 5 ECTS
This course illustrates the usage of data and analytics in modern business activities. The main focus is on Data Warehousing methodology and Database Marketing set-up in a multidimensional framework. Demand Segmentation and Scoring Models will be the practical applications.