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The master's degree course in Data Science for Economics and Health (DSEH), entirely delivered in English, aims to provide advanced education on methodological methods and tools in computer science, statistics, and mathematics designed to interpret and analyze complex phenomena in the fields of economics and health. The course of study offers advanced skills through the study of emerging information technologies about data management and scalability of analysis systems in cloud environments, advanced statistical and mathematical techniques, as well as machine learning techniques for information extraction and classification. Furthermore, the course addresses topics about economic theory, decision theory under conditions of uncertainty, econometrics, and time-series analysis, biostatistics and epidemiology. Graduates of the DSEH MSc program will receive advanced education on methodologies and tools in computer science, quantitative and methodological notions to interpret and analyze economic phenomena using approaches that integrate business, market and social media data. Among these, the MSc program focuses on the analysis of the effects of economic policies as well as the evaluation of actions and any other activity related to the sectors of economy, environment, marketing and business. Moreover, the MSc program aim to provide the foundations of epidemiology and biostatistics on which to graft the acquired knowledge of data analysis. The DSEH course bolsters the construction of solid methodological bases by addressing topics of the economic theory, decision theory under uncertainty conditions, micro-econometric techniques and time-series analysis. It also fosters the study of emerging data management technologies and scalability of analysis systems in cloud environments, as well as machine learning techniques for the extraction and classification of information.
In addition to these compulsory activities, the DSEH course allows students to autonomously customize/specialize the study plan according to their own inclinations, by choosing elective courses up to 18 ECTS in total between three different educational paths, namely "Data Science" path, "Economic Data Analysis" path, and "Health" path. A first kind of specialization focus is about the aspects of methodological and technological innovation, advanced statistical methods, techniques of social media analysis and textual analysis as well as their impact on the data-driven business. A further kind of specialization offers useful tools for economic applications in policy or investment assessment, the study of production processes, and the evolution of social phenomena, with a focus on environmental issues. Finally, the third specialization is devoted to the analysis of medical data and the study of the relationship between exposure and health in the population and to provide the tools to critically evaluate the epidemiological literature.
These specialization activities are geared, together with the external training activities, to the preparation of the dissertation and to the final exam. Therefore, the dissertation is considered as the fulfilment of the course of study and the learning process began with the choice of the educational path.
The courses of DSEH, both compulsory and elective, include lectures and laboratory classes as well as autonomous project activities and individual activities to guarantee an adequate preparation also from a practical point of view, in close contact with case studies and real data.
The in-depth studies in mathematics, statistics, computer science and economics highly qualify the educational project of Data Science for Economics and Health, and they also pave the way to students interested in PhD and research programs in the areas of Data Science, Computer Science, Economics, and Epidemiology and Public Health.
Programme overview
Main Subject
Economics and Econometrics
Degree
MA
Study Level
Masters
Study Mode
On Campus
The master's degree course in Data Science for Economics and Health (DSEH), entirely delivered in English, aims to provide advanced education on methodological methods and tools in computer science, statistics, and mathematics designed to interpret and analyze complex phenomena in the fields of economics and health. The course of study offers advanced skills through the study of emerging information technologies about data management and scalability of analysis systems in cloud environments, advanced statistical and mathematical techniques, as well as machine learning techniques for information extraction and classification. Furthermore, the course addresses topics about economic theory, decision theory under conditions of uncertainty, econometrics, and time-series analysis, biostatistics and epidemiology. Graduates of the DSEH MSc program will receive advanced education on methodologies and tools in computer science, quantitative and methodological notions to interpret and analyze economic phenomena using approaches that integrate business, market and social media data. Among these, the MSc program focuses on the analysis of the effects of economic policies as well as the evaluation of actions and any other activity related to the sectors of economy, environment, marketing and business. Moreover, the MSc program aim to provide the foundations of epidemiology and biostatistics on which to graft the acquired knowledge of data analysis. The DSEH course bolsters the construction of solid methodological bases by addressing topics of the economic theory, decision theory under uncertainty conditions, micro-econometric techniques and time-series analysis. It also fosters the study of emerging data management technologies and scalability of analysis systems in cloud environments, as well as machine learning techniques for the extraction and classification of information.
In addition to these compulsory activities, the DSEH course allows students to autonomously customize/specialize the study plan according to their own inclinations, by choosing elective courses up to 18 ECTS in total between three different educational paths, namely "Data Science" path, "Economic Data Analysis" path, and "Health" path. A first kind of specialization focus is about the aspects of methodological and technological innovation, advanced statistical methods, techniques of social media analysis and textual analysis as well as their impact on the data-driven business. A further kind of specialization offers useful tools for economic applications in policy or investment assessment, the study of production processes, and the evolution of social phenomena, with a focus on environmental issues. Finally, the third specialization is devoted to the analysis of medical data and the study of the relationship between exposure and health in the population and to provide the tools to critically evaluate the epidemiological literature.
These specialization activities are geared, together with the external training activities, to the preparation of the dissertation and to the final exam. Therefore, the dissertation is considered as the fulfilment of the course of study and the learning process began with the choice of the educational path.
The courses of DSEH, both compulsory and elective, include lectures and laboratory classes as well as autonomous project activities and individual activities to guarantee an adequate preparation also from a practical point of view, in close contact with case studies and real data.
The in-depth studies in mathematics, statistics, computer science and economics highly qualify the educational project of Data Science for Economics and Health, and they also pave the way to students interested in PhD and research programs in the areas of Data Science, Computer Science, Economics, and Epidemiology and Public Health.
Admission Requirements
5.5+
72+
1. Curricular requirements Candidates for admission to the master's degree course may come from various bachelor's, but must have earned at least 30 ECTS in computer science and mathematics (scientific disciplinary sectors: from MAT-01 to MAT-09, INF-01, ING- INF/05) and/or in the area of economic sciences and statistics (scientific disciplinary sectors: SECS-S/01, SECS-S/02, SECS-S/03, SECS-S/06, SECS-P/05, SECS-P/01, SECS-P/02, SECS-P/03, SECS-P/07, SECS-P/08, SECS-P/10), and/or in medical sciences (scientific sector MED/01 only). Curricular requirements must be met by the date of effective submission of the application for admission. Students with a foreign qualification are required to provide an Italian qualification to show that they satisfy the minimum curricular requirements of DSEH.
2. Proficiency in English Proficiency in English at a B2 level or higher per the Common European Framework of Reference for Languages (CEFR) is required for admission.
2 Years
Oct
Scholarships
Selecting the right scholarship can be a daunting process. With countless options available, students often find themselves overwhelmed and confused. The decision can be especially stressful for those facing financial constraints or pursuing specific academic or career goals.
To help students navigate this challenging process, we recommend the following articles:
Data Science for Economics and Health
Main Campus, Milan, Italy
# 201-250QS Subject Rankings
24 monthsProgramme duration
Economics and EconometricsMain Subject Area
Programme overview
Main Subject
Economics and Econometrics
Degree
MA
Study Level
Masters
Study Mode
On Campus
The master's degree course in Data Science for Economics and Health (DSEH), entirely delivered in English, aims to provide advanced education on methodological methods and tools in computer science, statistics, and mathematics designed to interpret and analyze complex phenomena in the fields of economics and health. The course of study offers advanced skills through the study of emerging information technologies about data management and scalability of analysis systems in cloud environments, advanced statistical and mathematical techniques, as well as machine learning techniques for information extraction and classification. Furthermore, the course addresses topics about economic theory, decision theory under conditions of uncertainty, econometrics, and time-series analysis, biostatistics and epidemiology. Graduates of the DSEH MSc program will receive advanced education on methodologies and tools in computer science, quantitative and methodological notions to interpret and analyze economic phenomena using approaches that integrate business, market and social media data. Among these, the MSc program focuses on the analysis of the effects of economic policies as well as the evaluation of actions and any other activity related to the sectors of economy, environment, marketing and business. Moreover, the MSc program aim to provide the foundations of epidemiology and biostatistics on which to graft the acquired knowledge of data analysis. The DSEH course bolsters the construction of solid methodological bases by addressing topics of the economic theory, decision theory under uncertainty conditions, micro-econometric techniques and time-series analysis. It also fosters the study of emerging data management technologies and scalability of analysis systems in cloud environments, as well as machine learning techniques for the extraction and classification of information.
In addition to these compulsory activities, the DSEH course allows students to autonomously customize/specialize the study plan according to their own inclinations, by choosing elective courses up to 18 ECTS in total between three different educational paths, namely "Data Science" path, "Economic Data Analysis" path, and "Health" path. A first kind of specialization focus is about the aspects of methodological and technological innovation, advanced statistical methods, techniques of social media analysis and textual analysis as well as their impact on the data-driven business. A further kind of specialization offers useful tools for economic applications in policy or investment assessment, the study of production processes, and the evolution of social phenomena, with a focus on environmental issues. Finally, the third specialization is devoted to the analysis of medical data and the study of the relationship between exposure and health in the population and to provide the tools to critically evaluate the epidemiological literature.
These specialization activities are geared, together with the external training activities, to the preparation of the dissertation and to the final exam. Therefore, the dissertation is considered as the fulfilment of the course of study and the learning process began with the choice of the educational path.
The courses of DSEH, both compulsory and elective, include lectures and laboratory classes as well as autonomous project activities and individual activities to guarantee an adequate preparation also from a practical point of view, in close contact with case studies and real data.
The in-depth studies in mathematics, statistics, computer science and economics highly qualify the educational project of Data Science for Economics and Health, and they also pave the way to students interested in PhD and research programs in the areas of Data Science, Computer Science, Economics, and Epidemiology and Public Health.
Programme overview
Main Subject
Economics and Econometrics
Degree
MA
Study Level
Masters
Study Mode
On Campus
The master's degree course in Data Science for Economics and Health (DSEH), entirely delivered in English, aims to provide advanced education on methodological methods and tools in computer science, statistics, and mathematics designed to interpret and analyze complex phenomena in the fields of economics and health. The course of study offers advanced skills through the study of emerging information technologies about data management and scalability of analysis systems in cloud environments, advanced statistical and mathematical techniques, as well as machine learning techniques for information extraction and classification. Furthermore, the course addresses topics about economic theory, decision theory under conditions of uncertainty, econometrics, and time-series analysis, biostatistics and epidemiology. Graduates of the DSEH MSc program will receive advanced education on methodologies and tools in computer science, quantitative and methodological notions to interpret and analyze economic phenomena using approaches that integrate business, market and social media data. Among these, the MSc program focuses on the analysis of the effects of economic policies as well as the evaluation of actions and any other activity related to the sectors of economy, environment, marketing and business. Moreover, the MSc program aim to provide the foundations of epidemiology and biostatistics on which to graft the acquired knowledge of data analysis. The DSEH course bolsters the construction of solid methodological bases by addressing topics of the economic theory, decision theory under uncertainty conditions, micro-econometric techniques and time-series analysis. It also fosters the study of emerging data management technologies and scalability of analysis systems in cloud environments, as well as machine learning techniques for the extraction and classification of information.
In addition to these compulsory activities, the DSEH course allows students to autonomously customize/specialize the study plan according to their own inclinations, by choosing elective courses up to 18 ECTS in total between three different educational paths, namely "Data Science" path, "Economic Data Analysis" path, and "Health" path. A first kind of specialization focus is about the aspects of methodological and technological innovation, advanced statistical methods, techniques of social media analysis and textual analysis as well as their impact on the data-driven business. A further kind of specialization offers useful tools for economic applications in policy or investment assessment, the study of production processes, and the evolution of social phenomena, with a focus on environmental issues. Finally, the third specialization is devoted to the analysis of medical data and the study of the relationship between exposure and health in the population and to provide the tools to critically evaluate the epidemiological literature.
These specialization activities are geared, together with the external training activities, to the preparation of the dissertation and to the final exam. Therefore, the dissertation is considered as the fulfilment of the course of study and the learning process began with the choice of the educational path.
The courses of DSEH, both compulsory and elective, include lectures and laboratory classes as well as autonomous project activities and individual activities to guarantee an adequate preparation also from a practical point of view, in close contact with case studies and real data.
The in-depth studies in mathematics, statistics, computer science and economics highly qualify the educational project of Data Science for Economics and Health, and they also pave the way to students interested in PhD and research programs in the areas of Data Science, Computer Science, Economics, and Epidemiology and Public Health.
Admission Requirements
Candidates for admission to the master's degree course may come from various bachelor's, but must have earned at least 30 ECTS in computer science and mathematics (scientific disciplinary sectors: from MAT-01 to MAT-09, INF-01, ING- INF/05) and/or in the area of economic sciences and statistics (scientific disciplinary sectors: SECS-S/01, SECS-S/02, SECS-S/03, SECS-S/06, SECS-P/05, SECS-P/01, SECS-P/02, SECS-P/03, SECS-P/07, SECS-P/08, SECS-P/10), and/or in medical sciences (scientific sector MED/01 only). Curricular requirements must be met by the date of effective submission of the application for admission. Students with a foreign qualification are required to provide an Italian qualification to show that they satisfy the minimum curricular requirements of DSEH.
2. Proficiency in English
Proficiency in English at a B2 level or higher per the Common European Framework of Reference for Languages (CEFR) is required for admission.
Scholarships
Selecting the right scholarship can be a daunting process. With countless options available, students often find themselves overwhelmed and confused. The decision can be especially stressful for those facing financial constraints or pursuing specific academic or career goals.
To help students navigate this challenging process, we recommend the following articles:
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