2024-2025

The module presents the methodological aspects, technologies and systems for designing predictive systems of Artificial Intelligence through machine learning and deep neural networks. The emphasis is placed on the analysis of application problems using examples and case studies, with practical exercises.

Prerequisites: Python & Data Mining & Machine Learning

Building on innovation management literature, this course aims to provide a broad and updated understanding of the multi-level key issues regarding the firms’ data driven innovation process. More specifically, the course aims to present how big data could drive companies’ innovation processes. After a preliminary discussion of the key aspects that characterize companies’ innovation processes, emphasis will be placed on practical tools such as business model canvas.

The Data Visualization and Visual Analytics course provides a comprehensive introduction to produce effective and efficient visualization and storytelling through data visualization. During the course, the students will explore the basics of visual encoding, data visualization mapping through encoding with visual variables, and visual analytics techniques.

In this module groups of students will be guided to design and develop an entire project in Big Data and AI: from data collection to the final delivery. The students will employ in the project methods, techniques and tools studied in the other modules. The duration of this module, differently from the others, will span across several months until the end of the lectures when the results of the project will be presented in front of a committee.

The master requires an internship to be carried out at one of the partners (companies or institutions) or on the current company a student is working on, on the basis of a well defined project work and under the supervision of a team of tutors composed of instructors and company experts. The internship might require in presence work at the partners' offices or hybrid solutions with smart working.

The course introduces the student to the main concepts of statistical analysis, the methods used and the software implementations to carry out a quantitative and rigorous study of a dataset. After introducing the basic tools of descriptive statistics, the course focuses on probabilistic statistics and its use for data modelling, estimation methods through an inferential approach and statistical hypothesis testing.