Master’s degreeData Science LM-DATA

Lieu des études	Italie, Bari
Type	Master's Degree, temps plein
Langue d'étude	italien
Attributions	Master’s degree
Code de cursus	8991/PDS0-2022/2022

Frais de scolarité	For information please refer to the Regolamento tasse e contributi

Conditions d'admission

Licence (ou plus) Open access course. To be admitted to this Master’s Degree Course you must be in possession of the curricular requirements and have passed the verification relating to the adequacy of the personal preparation. Please consult the Regulation of the course that you can find on the course description (scheda corso) at the following link Les diplômes sont acceptés dans les langues suivantes : anglais / italien. Votre école peut généralement vous fournir un relevé de notes convenable. Dans le cas contraire, les copies devront être accompagnées de traductions officielles. Vous devrez apporter vos certifications/diplômes lorsque vous vous rendrez à l’université.

Exigences linguistiques

italien Non-eu students residing abroad who would like to enroll in a study course completely taught in Italian, have to prove their knowledge of the language through an Italian language test. The test will be organized by the CLA (University Language Centre), only after pre-enrollment on the Universitaly portal. For further information, please refer to the following page The following categories are excluded from this obligation: 1. students who intend to enroll in degree courses in English; 2. students in possession of an Italian language certification of level B2 or higher (such as CILS, CELI, Roma3 or PLIDA).

Autres exigences

The Course is open to all graduate students. Admission requirements consist of having completed a Bachelor’s degree at Uniba or in other universities, in Italy or abroad, having acquired • at least 15 ECTS in mathematical-statistical scientific-disciplinary sectors (MAT/* and SECS-S/*) • at least a further 15 ECTS (altogether) in the scientific-disciplinary sectors: SECS-P/*, INF/01, ING-INF/*, IUS/*, MAT/*, SECS-S/*, BIO/* Students must, in addition, have English language proficiency at the B1 level. Students with these curricular requirements will be eligible for a personal assessment, which is compulsory and will take the form of an oral interview and/or a written test. Students must pass the test in order to fulfill enrollment.

Plus d'informations	uniba.it/..corso-di-laurea-in-data-science

Aperçu

The Master Degree Course intends for future graduates to acquire specific knowledge and skills in the field of Data Science. It is distinguished into curricula to match the strong demand for innovative professional figures coming from advanced production sectors. More specifically, the course of study is aimed at training professionals able to use mathematical-statistical and computer science techniques within public and private companies and administrations, including scientific and technological research bodies or institutes, with special attention to the acquisition, management, processing, analysis and use of large volumes of data, also by effectively working alongside experts in specific applicative areas.

Structure du programme

https://www.uniba.it/it/corsi/cdl-data-science/studiare/piano-di-studi

Opportunités de carrière

The master degree graduate in Data Science will be able to cover high-profile technical and/or managerial roles in areas that require advanced and appropriate knowledge and skills to identify, collect, manage, analyze, and interpret large collections of data (big data), supporting innovative business development, understanding of social phenomena, new data-driven methodologies of scientific inquiry, and design and implementation of data products, i.e., tangible results based on descriptive, predictive, or prescriptive analytics for complex systems.
Specifically, master graduates can fill the roles of data scientist (or data analyst), data specialist, and data manager. They may also cover the roles of heads of departments for the development and management of information technology methodologies to support decision-making processes, or they may be technical figures, in teams with specialists, who deal with the analysis and processing of physical, chemical, biological, health-related, and more generally scientific-technological data.