Course-specific learning objectives
The increasingly widespread diffusion of the Internet, mobile apps, IoT (Internet of Things) technologies, etc., makes a huge amount of data available. This data is valuable for guiding decision-making processes within companies. Indeed, being able to collect, clean, analyze and interpret data also means being able to adapt more easily to market trends and be more reactive to changes, resulting in greater ability to acquire new customers while retaining old ones, as well as optimizing the administrative, production and management processes of the company. However, being able to give meaning to the large amounts of data available and use this meaning to support decision-making processes, being able to transform raw data into knowledge and information that can be used to define strategic actions or actionable insights, requires the co-presence of multidisciplinary knowledge in the same figure. These challenges require the acquisition of skills in various areas:
- Data strategy, which deals with how data can be used to achieve the economic objectives of the company. In other words, it seeks to maximize the value that can be obtained from the extracted data, using it to guide business strategies. Managerial skills are of primary importance in this area.
- Data governance, which deals with ensuring that data is handled correctly, its quality, its security, its efficient management, compliance with regulations and standards, such as those relating to the use of sensitive data, hence the need for legal skills.
- Data Science, which has a neutral approach to data, limiting itself to collecting, processing, analyzing and making it usable without an economic objective, but with the aim of generating reports based on such data to be provided to the company, which will then take care, through the data strategy, of converting the content of such reports into economic value. In this case, mathematical/statistical/computer skills are of primary importance.
The Master's Degree in Data Science for Management is specifically designed to provide students with the transversal skills required by these professional profiles, which include the ability to communicate, problem solving, technological innovation, as well as the ability to understand and anticipate ethical and privacy issues, to govern the data life cycle, to manage quality, to prevent the improper use of data or analytical results.
The achievement of the learning objectives of the Master’s Degree in Data Science for Management is based on the following thematic areas:
• mathematics, with the ability to create mathematical models that are able to give abstract representations of the real contexts from which the data are extracted, easier to analyze through analytical tools and on which to apply optimization algorithms to support decision-making processes;
• statistics, necessary to extract indices that allow to give a synthetic description of large amounts of data, to verify hypotheses, to identify correlations between distinct data, to develop predictive models, to extract useful information from the data (data mining);
• computer science, necessary to learn how to use the main software tools for (structured and unstructured) data management, to ensure data security and protection, to apply artificial intelligence techniques through which to process data in order to perform specific tasks or, with the advent of generative artificial intelligence, to generate new content, all relying on machine learning approaches and, more specifically, deep learning;
• accounting, necessary to support economic-business decisions in the context of balance sheet analysis (in order to extract useful information to evaluate company performance, identify risks and opportunities, and support investment and financing decisions), management control (in order to optimize company strategies through planning, programming and control activities), audit and accounting review activities (in order to identify and prevent fraudulent activities within the company), data visualization for the communication of accounting information, as well as business intelligence in companies of any sector;
• financial, aimed at performing financial data analysis to monitor the performance of financial markets, evaluate stocks and bonds, as well as identify profitable investment opportunities. Particular attention is paid to the development of pricing and risk management models, as well as portfolio and asset allocation analyses;
• legal, with particular attention to the issues related to the use of data, sensitive and otherwise, to comply with the complex regulations set by the GDPR, by Italian legislation and by the provisions adopted by the Data Protection Authority within its own competences;
• marketing, necessary to apply data analysis techniques and skills to the study of the customer base between online and offline channels, to learn and develop analysis and segmentation models and Key Performance Indicators (KPI), design personalized Customer Relationship Management (CRM) and Digital Marketing campaigns, measure the performance of marketing activities with the right KPIs and make data-driven decisions.
The use of specific teaching and assessment methodologies is foreseen for the development of transversal skills necessary for students to face work and professional contexts. In particular, the Data Science for Management student will develop the ability to work in a group, to operate in an autonomous way, and to promptly fit into work environments. External activities are foreseen such as training internships at companies, public structures, laboratories and study visits at other Italian, European and non-European universities, useful for achieving the educational objectives and subsequent entry into the job market.