About the course
"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.
The in-depth studies in mathematics, statistics, information technology and economics, highly qualify the Data Science and Economics training project and prepares the graduates also for selective procedures of PhD and research programs in the areas of
- Data Science
- Computer Science
- Business Intelligence
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Graduates will have advanced theoretical knowledge and skills in the areas of economics, mathematics, statistics and information technology.
For the economic area, the required compulsory courses cover: Microeconomics and Macroeconomics, Micro-Econometrics, Causal Inference and Time Series Analysis. Suggested courses include: Economics of Government and Policy Evaluation, Labor Economics and Policy Evaluation, Patients’ Needs and Healthcare Markets, Industrial Firms and Policies, Experimental Methods and Behavioral Economics, Global Firms and Market, Game Theory.
For the mathematical-statistical area, the courses include: Graph Theory, Discrete Mathematics, Optimization, Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence, Clustering and Probabilistic Modeling, and among those suggested: Dimensionality Reduction and Sparse Systems, Text Mining and Sentiment Analysis, Social Network Analysis, Numerical Methods for Finance, Statistical Methods for Finance.
For the computer science area, the expected courses focus on: Coding for Data Science, Data Management, Machine Learning, Deep Learning, Artificial Intelligence, Cybersecurity and Privacy Preservation Techniques, Cloud and Distributed Computing, Algorithms for Massive Data, Clustering and Probabilistic Modeling, Text Mining and Sentiment Analysis, Social Network Analysis.
The exercises, which integrate all the teachings of the first year of the course, will have an important role in achieving these results. Students are also expected to extend and deepen the knowledge thus acquired through participation in seminars conducted by external experts, with consultation of bibliographic materials and thesis work. Individual learning is assessed mainly through the exam and, for some quantitative teachings, based on tests conducted in computer rooms. The thesis provides an additional opportunity to verify the understanding of the topics covered in the degree course.
Graduates will be able to apply the knowledge and skills acquired to the analysis of economic and social phenomena and to the management of business problems posed by the technological innovation process; to evaluate the effects of economic policies or investments; the quantitative assessment of the risk and the effects of decisions under conditions of uncertainty; to the study of complex and interconnected systems.
Economic area: as far as the teaching of the economic field is concerned, the skills are learned through the discussion of the main issues and problems of real economy and the evaluation of the policies for their solution.
Mathematical-statistical area: the ability to apply quantitative methods of analysis and to analytically set business decisions are learned both through the exercises of the relevant lessons and, above all, through the use of diversified data sources in the context of problems real.
Computer science area: the ability to apply knowledge and understanding is developed by the teaching of computer science with reference to data management and analysis systems; to cloud computing systems and algorithms for large amounts of data.
Individual learning is constantly verified in the exercises and evaluated mainly with written problem-solving exams.
The ability to apply the knowledge acquired in the degree course is expressed in the degree thesis that also offers an opportunity for verification.
The knowledge and skills are achieved and verified in the training activities foreseen by the Manifesto of Studies in the Economics, Mathematics-Statistics and Computer Science areas.
Graduates should acquire the ability to formulate independent and informed judgments by developing critical skills: the effects and effectiveness of the decisions of the companies and institutions in which they operate, also with reference to the ethical implications of such actions and decisions, above all in relation to the security and confidentiality of the analyzed data; the consequences and effectiveness of economic policies. They will also have to fully assimilate the principles of professional deontology that guide interpersonal relations in the occupational context of reference and will also have to acquire the fundamental principles of the scientific approach to the solution of the economic-business problems that they will face in their professional activity. The multidisciplinary approach of the degree program favors the development of autonomous judgment and critical reasoning, offering students the opportunity to compare methodological approaches belonging to different disciplines. The significant presence of both economic and quantitative and computer science courses, which provide methodological and technical skills of formal analysis, favors the learning of the scientific approach to problem solving. The acquisition of critical skills and autonomy of judgment will be verified in the company teachings through the presentation and discussion of business cases. These skills will also be verified through the provision of open questions in the examinations and, in some cases, through the evaluation of short essays and written papers.
Graduates will be able to: present and communicate effectively the results of their work within the company or institutions (projects, reporting, document analysis, etc.); argue their positions and communicate clearly and effectively in a written and oral form supported by evidence of data; set up cooperative and collaborative relationships within working groups; present proposals and solutions to the problems of reference working contexts using mathematical-quantitative tools; access a more specialized audience, for example, by publishing the results of the research. The ability to communicate effectively in working contexts is primarily acquired through the presentation and discussion of business cases. The application of quantitative methods of analysis and computer techniques in economic teaching develops the ability of students to use information and empirical evidence to support the solutions they propose in working contexts. The drafting of reports and short essays, foreseen by some teachings, and the drafting of the degree thesis allow to enhance the written communication skills. The participation to the exercise classes, the development of any internships in the company and, alternatively, participation to internal laboratories will allow students to develop communication skills and skills of relational type. The ability to communicate is verified in the examination tests as an element that contributes to the overall judgment and specifically in the case of courses that provide for the acquisition of the training objectives. The drafting and discussion of the degree thesis provide further evaluation elements.
Graduates will have the ability to develop and deepen their skills through: the consultation of specialized scientific publications; the consultation of databases and other information on the web; the analysis of information and data through mathematical, statistical and econometric tools. The degree course in Data Science and Economics also provides the methodological skills that foster the ability to further learning, both to independently undertake a professional path aimed at the exercise of managerial functions or high responsibility in industry and in the sector. financial where more and more the figure of the data scientist is affirmed, both to develop the autonomy of research functional to undertake professional activities in research institutions and study offices or to continue their studies in second level master’s degrees or in doctoral programs.
Students also have the opportunity to attend, as chosen educational activities, specific laboratories for learning methods of economic research. Furthermore, the capacity for further learning is fostered by the presence of teachings that provide methodological and technical skills of formal analysis. Finally, the preparation of the degree thesis provides students with an additional opportunity to develop learning skills through the independent elaboration of advanced research work.