MSc in Applied Data Science
University Of L'Aquila
Key Information
Campus location
L'Aquila, Italy
Languages
English
Study format
On-Campus
Duration
2 years
Pace
Full time
Tuition fees
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Introduction
Applied Data Science
- Department: Information Engineering, Computer Science and Mathematics
- Level: Master's
- Class: LM91
- Admission typology: Open admission with an assessment of personal competencies and skills
- Internationalization: International degree course
The aim of the Laurea Magistrale (Second cycle degree course) in Applied Data Science is to train specialists able to process, analyze and use data in specific and differentiated application contexts. The degree course in Applied Data Science provides for the possibility to carry out stage and training in companies as an integral part of the educational path, making it easy to transfer competencies from the University to the companies.
Admissions
Curriculum
The degree course is conceived to train specialists able to process, analyze and use data in specific application domains. Among these, life sciences, digital services for the community, process and product innovation for companies, and public administration may be considered of interest. A specific curriculum for each domain shall be activated. The curricula have an initial common educational path and then differ in order to provide adequate tools for generating, processing, and analyzing data related to the specific application field.
The common path mainly consists of mandatory units aiming at providing and/or harmonizing the basic methodological knowledge for data processing and analysis, knowledge concerning data security issues as well as knowledge concerning the notion of datum and representation in the company, legal and logical-philosophical areas. Considerable importance is given to technological knowledge in Big Data processing and legal knowledge necessary to understand and handle ethical and legal problems concerning processing and use of data in the related fields of application.
Knowledge and understanding
Graduates in Applied Data Science are familiar with Informatics, Engineering, Statistics, and Mathematics techniques relevant for processing and viewing data. They are aware of the methods for collecting data and are able to understand possible sources of uncertainty that may affect their quality. They know data analysis techniques and how to apply them to data from different fields. They know the informatics tools and software platforms for data processing and understand problems concerning data as well as software systems reliability and security. They are familiar with mathematical modeling tools necessary to understand methods for using data in order to optimize processes in companies, institutions, and public administrations. They know legal standards about data collection, processing, and use as well as the ethical issues related to the different application fields of data treated in the study course.
The expected results are obtained through class lessons, individual or group projects and laboratory activities, and the final test. Knowledge and understanding skills are tested by means of written and oral tests and by the assessment of papers prepared for project and laboratory activities and for the final test.
Applying knowledge and understanding:
Graduates in Applied Data Science are able to collect, classify and analyze data in several application domains. They are able to assess data quality and identify the most adequate informatics tools for their processing also with respect to their security. They can apply the mathematical modeling skills achieved and use data to develop products and services offered by companies, institutions, and public administrations. Their knowledge of standards about data allows them to design and carry out experiments, interpret their results, and, if necessary, develop original solutions and methodologies in accordance with the relevant rules and ethics. Thanks to the interdisciplinary feature of their training, Data Science specialists are also able to coordinate multidisciplinary projects based on the massive use of data.
Applying knowledge and understanding skills are obtained through exercises and project activities in which students shall apply methods and techniques learned as well as through training courses and the final test.
The expected results are obtained through:
- Exams, written and oral tests, concerning also project activities in which students shall apply methods and techniques learned.
- The assessment of reports and papers submitted for group work, stage, and the final test.
Making Judgements
Graduates in Applied Data Science acquire a high degree of reasoning skills and critical judgment which shall allow them to identify the most appropriate models and techniques to handle and analyze data relative to the processes and activities of an organization in order to draw new knowledge and value.
Data Science specialists are able to assess the impact of the solutions proposed within the application context as far as both technical and management issues are concerned. Moreover, they are able to identify and assess the broader and non-technical implications of Applied Data Science in the field of ethics, law, economics, and industry.
They are able to understand the limits of their knowledge and identify possible methodological tools necessary to integrate their competencies.
In order to develop the students’ independent judgment, learning methods based on their active participation shall be adopted, such as i) laboratory activities and in-depth study of specific and exemplary case studies; ii) training and internships in work contexts; iii) practical training activities related to the final test.
Independence of judgment shall be verified by means of oral exams and assessment of individual papers submitted both in single course units and in the final test.
Communication skills
Data Science specialists are able to:
- Communicate clearly the most appropriate outcomes and strategies resulting from the analysis of data, also through adequate presentation of the results;
- Discuss the feasibility and effectiveness of the solutions proposed in technological, economic, and legal terms;
- Speak and write fluently in English, mastering also technical terminology;
- Talk, by using proper language tools, information, ideas, problems, and solutions to specialist and non-specialist stakeholders;
- Communicate effectively with other parties, especially if they are the group coordinators;
- Use the necessary communication skills to understand the problem requirements through the interaction with customers and experts of the domain of interest.
These objectives are obtained through training activities which include group work, written and oral project reports in English, and, finally, written and oral presentation of the final test.
The achievement of such objectives shall be verified by means of examination tests and the assessment of reports and papers submitted for group work, stage, or the final test.
Learning skills
Graduates in Applied Data Science:
- Have the learning abilities necessary to autonomously carry on with the studies as well as update their knowledge as required by the development of technology and the social, legal, economic, and production systems;
- Are able to read and understand scientific literature at graduate and post-graduate level, use technical handbooks concerning software for different applications;
- Are able to enter post-graduate study courses such as second-level master’s degrees, Ph.D. and/or professions in the field of research;
- Have the skills to read and learn in English.
Learning skills acquired during the study course are verified by assessing individual or group application projects and an original final test, in which the graduate may choose autonomously the methodologies and technologies to be adopted.
Program Outcome
The aim of the Laurea Magistrale (Second cycle degree course) in Applied Data Science is to train specialists able to process, analyze and use data in specific and differentiated application contexts. Graduates are trained to:
- Identify, classify and process data handled by an organization in a specific application domain, paying attention to the quality of data and to the rules concerning their processing;
- Identify which data shall be acquired outside the organization and related ways of collection, in economic as well as in legal terms;
- Explore, validate, model, and analyze data in order to add value to the organization both in terms of processing and of the product;
- Be able to discuss and prove the effectiveness of the solutions proposed.
The Second cycle degree course provides the basic methodological knowledge to process and analyze data as well as the informatics skills oriented to Data Science and Big Data (programming, databases, web services, open data); skills about data security, mathematical skills for networks, and decision making; skills in statistics and statistical learning. The Second cycle degree course in Applied Data Science also provides skills in the field of company management and law related to management, economic, and legal aspects concerning data processing and use. Special emphasis is given to training aimed at developing the understanding of ethical and legal problems connected to data processing and use in the fields of applications. Finally, the degree course provides skills in the sociological area for social analyses, logical-philosophical problems, notions of data, and representation.
The degree course is characterized by a remarkable number of laboratory units, which are its fundamental, characterizing, and essential features. Moreover, it includes practical activities in specific application fields in which to apply theories and methods learned. Such activities shall be carried out in dedicated laboratories.
The degree course in Applied Data Science provides for the possibility to carry out stage and training in companies as an integral part of the educational path, making it easy to transfer competencies from the University to the companies.
Program Tuition Fee
Career Opportunities
Role in a work environment
Graduates in Applied Data Science play high-profile technical and management roles in fields that require a good knowledge of informatics, mathematics, and social sciences and in-depth knowledge of data processing. To such a professional figure refer the activities of collecting, analyzing, processing, interpreting, disseminating and reading quantitative and qualitative data of an organization for analytical, prediction or strategical purposes. They identify, collect, prepare, validate, analyze, interpret data relevant to different activities of an organization in order to draw information (deriving from synthesis or analysis), also by developing prediction models to generate organized knowledge systems.
Skills associated with the function
Role competencies:
Data Science Specialists are able to:
- Identify, classify and process data handled by an organization in a specific application domain with care for the quality of data and the standards which rule its handling;
- Identify which data shall be acquired outside the organization and relevant ways of acquisition both in economic and legal terms;
- Explore, validate, model, and analyze data to add value to the organization both in terms of process and of the product;
- Discuss and prove the effectiveness of the solutions proposed.
Data Science Specialists are therefore the analysts of a great number of highly complex data (Big Data and Open Data), who anyhow are able to combine the methods and techniques of company management and public, private, and tertiary sector administration with the technologies and methodologies of informatics and social sciences, having expertise in each area.
Professional status
Professional opportunities:
- Public and private bodies
- Public and private research bodies
- Automated manufacturing industries
- Bank and insurance institutions
- Public administrations
- Public and private healthcare institutions
- Smart City service providers
- Tourism service providers
- Art and cultural service providers
- Transportation service providers
- IT companies
- Telecommunication companies
- Media
- Large-scale retail trade
- E-commerce service providers
- Energy distributors
- Water distributors