ARTIFICIAL INTELLIGENCE AND DATA ENGINEERING Degree Programme Profile
Master Degree in ARTIFICIAL INTELLIGENCE AND DATA ENGINEERING
EHEA Second cycle, EQF level 7
Number of Years/credits
2 years; 120 ECTS
Mode of Study
*All Degree Programmes are planned and organised for full-time students. It is possible, however (without special arrangements), to proceed through the course of study at one's own pace. This makes it possible, if necessary, to accommodate employment or other non-university activities or obligations.
Name of the Course Director and other contact information
President of the Degree Course Council:
Prof. MARCO AVVENUTI
Department of INGEGNERIA DELL'INFORMAZIONE
Internationalization Coordinator (CAI):
Prof. Luca Sanguinetti
Language of Teaching
Italian First cycle qualification (Laurea) or foreign equivalent in the same or related subject area, with possible extra work if required competences are lacking.
Possible assessment prior knowledge and competences
Obligatory entrance exam for orientation purposes (non-selective).
Required knowledge and competences support programmes
Students whose curricula show lacuna may need to take extra first cycle course units before admission.
The Master programme in Artificial Intelligence and Data Engineering provides a solid in-depth education that enables the graduates to design and implement, on one side, systems for efficiently managing large amount of data and extracting useful knowledge from this data, and, on the other, intelligent systems by exploiting artificial intelligence techniques. The MSc advances the student knowledge portfolio in both computer infrastructures for intensive data management, and methods for data analytics and artificial intelligence. These competences allow graduates to interact with professionals (even non-engineering ones) in different domains and contexts where data processing is required, as well as to complete their mastering of computer engineering.
The course is structured to admit not only students with already a strong background in computer engineering, but also students coming from different disciplines with at least a proper knowledge of programming languages. Graduates in computer engineering will have the opportunity for going in-depth into engineering and methodological disciplines; the graduates in other disciplines will complete their knowledge of base methodologies of computer engineering, including operating systems, computer networks, databases, algorithms and advanced programming.
Compulsory activities are in English. Elective activities are in English or Italian. The total of ECTS given in English is sufficient to complete the programme and take the degree, hence knowledge of the Italian language is not a prerequisite. However, learning activities covering basic subjects in computer engineering, meant for admitted students not having a strong background in this field of study, are only taught in Italian.
Key Learning Outcomes
Graduates of the Master programme in Artificial Intelligence and Data Engineering will be able to demonstrate an advanced knowledge in the following disciplines:
- platforms and technologies for storing, managing and analyzing data on a large scale, cloud computing, fog computing;
- methodologies for heterogeneous data visualization, data mining, process mining;
- methodologies for machine learning, deep learning, automated reasoning, computational intelligence.
Graduates will be allowed to develop a knowledge of related disciplines in the fields of optimization, statistics, business management and law, as well as complementary disciplines such as bioengineering and robotics.
Occupational Profile/s of Graduates
- Big Data Engineer
- Data Service/Platform Engineer/Manager
- Data Analytics Engineer/Manager
- Data Technologies Engineer
- Big Data Infrastructure Engineer
- Business Process Engineer/Manager
- Artificial Intelligence Software Engineer/Architect
- Machine Learning Engineer/Architect
- Big Data/AI Consultant
- Researcher in public/private labs
Access to further study
The Laurea Magistrale degree in ARTIFICIAL INTELLIGENCE AND DATA ENGINEERING allows the graduate to compete for entry into a Third Cycle programme/doctoral school.
Assessment methods, examination regulations, and grading
Assessment is normally by means of an oral or written examination; in some cases there are intermediate exams during the course; other elements (participation in discussion, written or oral reports, commentary of texts etc. ) are foreseen in specific course units and are described in the Course Unit Profiles.
The grading system for the course units consists of 30 possible points, plus 'lode' (cum laude) in case of excellence. Marks are given by the lecturer based on the performance as ascertained in a public examination by a board of at least two teachers. The main exam sessions are held in June/July; September; and January; students may resit exams**. Actual grading curves differ in different degree programmes. The University of Pisa provides an ECTS Grading Table, which shows the actual distribution, of the examination and final grades among students for each degree programme, in order to facilitate the comparison with other grading systems. ---> Link to ECTS Grading Table
An overall mark is given on the occasion of the 'Final Exam', when a written research text is presented and discussed. The final overall mark is calculated based on the results of the marks obtained in the single course units and the final exam, and is based on 110 possible points, with the possible further mention of honours ("lode" or cum laude).
**The exam sessions are organised into sessions (the dates vary according to the Department and are published in the Department's academic calendar). In each session there are a certain number of 'appelli' [calls], or dates on which the examination for each course unit may be taken. The 'appelli' are fixed by the teacher. The students choose which of the appelli they wish to respond to. In most cases, it is obligatory to sign up before the specified date.
Requirements (regulations) to obtain the qualification
See course structure diagram and available courses.
For additional information, go to the programme website https://computer.ing.unipi.it/aide-lm