2023-2024

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 Data Journalism course provides a comprehensive introduction to produce effective and efficient visualization and to the practice of data journalism and the art of storytelling through data. During the course, the students will explore the basic of visual encoding and data visualization mapping through encoding with visual variables.

The formidable advances in computing power, data acquisition, data storage and connectivity have created unprecedented amounts of data. Data mining, i.e., the science of extracting knowledge from these masses of data, has therefore been affirmed as an interdisciplinary branch of computer science. Data mining techniques have been applied to many industrial, scientific, and social problems, and are believed to have an ever deeper impact on society.

The module presents the methodological aspects, technologies and systems for designing, populating and querying Data Warehouses for decision support. The emphasis is placed on the analysis of application problems using examples and case studies, with laboratory exercises.

Prerequisites: knowledge of basic SQL, Excel, Python programming.

The module presents the characteristics and peculiarities of "big data", highlighting through specific use cases the growing importance of the ability to extract significant information and valuable insights from this enormous amount of heterogeneous data (for example data from sensors, purchase data and consumption, data from social media and social networks, open data, etc.). The participatory methods of data collection through crowdsourcing and crowdsensing systems are also discussed, showing popular examples of application of these concepts.

The module is organised in lectures on case studies and real applications showing the use of Big Data analytics and
Social Mining. These lecture describe activities of the SoBigData.eu laboratory, companies and institutions which are
partners of the Master.

The module introduces the ethical and legal notions of privacy, anonymity, transparency and discrimination, even considering the General Data Protection Regulation. It presents technologies for implementing the privacy-by-design principle, for auditing of predictive models, and for the protection of users rights with the goal of enabling the Big Data analysis while guaranteeing personal data protection, transparency and non-discrimination.

This module presents artificial intelligence techniques aimed at defining analytics on text and data from the Web. The course is organized around three main strands: i) text analytics, where text mining methods applied to texts and social media are studied; ii) sorting techniques through the application of "learning to rank" techniques which have the purpose of estimating the relevance of objects with respect to user requirements, iii) web mining techniques aimed at exploiting user usage data to improve quality of services.

The module has the aim to align the students' competences in computer science and in basic analytics, especially in data bases, and Python programming for data science. Starting form a theoretical introduction to the basics of programming and relational database modelling the course will be focused on pratical lectures for learning to query and modelling databases and to solving problems by writing Python programs in both static and dynimic environments. This module is based on hands-on work