“In the era of Industry 4.0, IoT (Internet of Things), Open and Big Data Social Media, the adoption of intelligent processes based on the analysis of large amounts of data is not just an important technological innovation, as others occurred in the past, but a real social and economic singularity that has radically changed the way in which human beings, businesses and institutions live and work. Through the data collected, economic operators are able to provide services adapted to individual preferences, understand the complex dynamics of constantly evolving contexts, predict social, cultural and market trends, generate new value. Since the year 2000, data produced by the major operators in the social media world have been used for predictive purposes or for the personalization of services. In recent years, due to the constant increase in the number of sensor and computating components integrated into production systems and the growing availability of data sources accessible to international organizations, awareness of the strategic importance of a scientific approach to the analysis of data has matured not only with large economic entities, but also in the world of small and medium-sized businesses. Increasingly, in the coming years the ability to analyze the functioning of the ecosystem of production and distribution of goods and services, business cycles, and even economic and social attitudes, will have a potentially disruptive effect on the competitiveness of the business system. Without a vigorous research and innovation effort, Italian industry will have to limit itself to a role of user of solutions developed elsewhere, without having control over usability, costs and analysis interfaces.
It therefore becomes crucial for the industry, especially of our country, to acquire new skills that are not due to the mere mix of computer science, statistical and economic competences, but which instead require the ability to think in new ways to the social and economic challenges in terms of highly dynamic, evolutionary and complex models and processes. The analysis of data is no longer just a tool with which to operate in the economic context, but becomes a guiding criterion in strategic choices and in the evaluation of the effectiveness of its action, in order to enhance its data assets, to create new models of business, and to optimize the management of resources. This new professional figure is named data scientist.”
The Master of Science in “Data Science and Economics” (DSE) aims to respond to the training needs of data scientist in the economic field by providing the skills necessary to analyze and understand the nature of data through modern data management techniques, machine learning, data mining and cloud computing, in order to extract meaningful relationships and recurring patterns, build predictive and nowcasting models that integrate company, market, administrative and social media data, perform analysis of policy effects (economic, social) or actions (investments, marketing campaigns) and any other activity related to the sectors of economy, marketing, business and finance.
The degree program aims to provide a solid and modern cultural background on computer science, statistics and economics, providing an integrated view of these skills in all its courses, in the belief that the integration of the foundational disciplines can develop for students a strong added value compared to the mere sum of skills acquired separately. The innovation in the teaching methods also has the ambition to develop, in students, the specific methodological attitude of the data scientist, forming professional figures capable of thinking in a new way the reality, starting from the challenges, thinking in terms of models, understanding the value of data, and learning how to evaluate the real impact of choices.
To this end, the modality of frontal transmission of skills will be integrated with laboratory activities that develop the ability to work in groups starting from real problems and using real data. Methods of work such as hackathons, problem solving, challenges among working groups, which already constitute personnel selection tools at the most important companies operating in the data sector, will be used intensively in the degree course with the training objective to develop the methodological attitude expected for the data scientist. The case studies and laboratory simulations will replace, as often as possible, the use of real data, without renouncing the complexity; these case studies will involve companies, research centers, institutions, economic and financial operators, communication agencies and marketing in the design of activities and interaction with students.
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