Key features of the MSc course
The Master’s Degree in Data Science at the University of Naples Federico II provides advanced interdisciplinary training aimed at preparing graduates to operate effectively in research, innovation, industry, public administration, and knowledge-intensive services. The program builds on the scientific expertise of several Departments of the University and offers a rigorous educational path integrating computer science, statistics, mathematics, artificial intelligence, data management, machine learning, and domain-specific applications.
The structure of the program reflects a clear and coherent educational progression. The first year is devoted to the acquisition of solid methodological foundations in the core areas of Data Science, enabling students to understand, model, process, analyze, and interpret complex and heterogeneous data. The second year allows students to deepen their competences within specific application domains, through four curricula: Data Science for Public Administration, Economics and Management; Data Science for Information Technologies; Data Science for Fundamental Sciences; and Data Science for Intelligent Systems.
A central feature of the course is the ability to combine methodological expertise with domain knowledge. Students are trained to interact with specialists from different fields, to identify relevant data-driven problems, and to design appropriate analytical and computational solutions. The program also promotes critical thinking, autonomy of judgement, communication skills, and the capacity to work in multidisciplinary contexts, with particular attention to the responsible and effective use of data.
Graduates acquire a professional profile suitable for highly qualified roles such as data scientist, data analyst, machine learning specialist, AI specialist, data engineer, business intelligence expert, and consultant for data-driven decision-making. They may find opportunities in private companies, public institutions, research centers, technology firms, consulting organizations, and innovative start-ups, as well as continue their studies in PhD programs and advanced research paths.
Eligibility and Admission Guidelines
Admission to the Master’s Degree in Data Science at the University of Naples Federico II is open to candidates holding a Bachelor’s degree, or an equivalent qualification, with an academic background consistent with the interdisciplinary nature of the program. Applicants are expected to have acquired at least 30 ECTS/CFU in areas such as engineering, computer science, mathematics, statistics or physics; within these credits, at least 6 ECTS/CFU must refer to computer science-related subjects. A solid previous academic record is also required, with a Bachelor’s degree score normally above 7 on a 10-point scale. Since the program is entirely taught in English, candidates must provide evidence of English language proficiency at B2 level or higher, issued by an officially recognized certification body.
International students must apply through the UNIVERSITALY portal, which represents the official entry point to the Italian university system for foreign applicants. Candidates who fully meet the admission requirements may submit their application directly through the portal. Applicants whose background does not entirely match the stated prerequisites may request a preliminary evaluation, although admission in such cases is subject to assessment by the competent academic committee.
Students who already hold a Master’s degree, obtained either in Italy or abroad, may also apply, provided that their overall academic background is compatible with the admission criteria. After enrolment, they may request an evaluation of their previous academic career in order to verify whether some examinations can be recognized. This assessment is carried out by the committee, which makes the final decision on possible credit recognition.
Although the official enrolment deadline may extend to the end of March, students are strongly encouraged to enroll before the beginning of the first semester, in order to follow the program effectively from the start.
Study Plan
| Codice esame | Denominazione insegnamento | SSD | Modulo | CFU | Semestre | TAF | ||
| I ANNO | ||||||||
| U5383 | Mathematical methods for Data Science | MAT/08 | MATH-05/A | unico | 6 | I | B | |
| U5387 | Statistical Learning & Data Analysis | SECS-S/01 | STAT-01/A | A | 6 | 12 | I | B |
| SECS-S/01 | STAT-01/A | B | 6 | B | ||||
| U5393 | Fundamentals of programming & Data management | ING-INF/05 | IINF-05/A | A | 6 | 12 | I | B |
| ING-INF/05 | IINF-05/A | B | 6 | I | B | |||
| U5386 | Hardware and Software for big data | ING-INF/05 | IINF-05/A | A | 6 | 12 | II | B |
| ING-INF/05 | IINF-05/A | B | 6 | II | B | |||
| U5390 | Data Mining & Machine Learning | SECS-S/01 | STAT-01/A | A | 6 | 12 | II | B |
| INF/01 | INFO-01/A | B | 6 | B | ||||
| U5396 | Theory and Ethics of Big Data and AI | M-FIL/03 | PHIL-03/A | unico | 6 | II | B | |
| II ANNO | ||||||||
| Insegnamento curriculare I (*) | 12 | I/II | C | |||||
| Insegnamento curriculare II | 6 | I/II | C | |||||
| Scelta autonoma dello studente (**) | 6 | I/II | D | |||||
| Scelta autonoma dello studente | 6 | I/II | D | |||||
| Ulteriori conoscenze (***) | 6 | F | ||||||
| Tirocinio-stage o progetto (****) | 8 | F | ||||||
| Esame di laurea | 16 | E | ||||||
(*) Gli Insegnamenti Curriculari sono definiti in Tabella 1 per ciascun Percorso di Studi (PdS) tra i seguenti:
- Curriculum Fundamental Sciences, declinato nei percorsi:
- Physics inspired Methodologies – FSC/PM
- Mathematical Methodologies – FSC /MM
- Life Sciences – FSC /LS
- Curriculum Information Technologies, declinato nei percorsi:
- Text and Speech Processing – ITE/TS
- Signal and Video Processing – ITE/SV
- Robotics and Statistics – ITE/RS
- Industrial Applications – ITE/IA
- Design and Security of AI Systems – ITE/AI
- Curriculum Public Administration, Economy and Management – ECO
- Curriculum Intelligent Systems – ISY
(**) I due insegnamenti a scelta autonoma dello studente da 6 CFU possono essere sostituiti da un unico insegnamento a scelta autonoma dello studente da 12 CFU. Gli insegnamenti a scelta autonoma dello studente possono essere selezionati tra tutti quelli offerti nei percorsi di Laurea Magistrale dell’Università di Napoli Federico II, purché siano coerenti con il percorso formativo scelto dallo studente. La Tabella 2 riporta l’elenco degli insegnamenti a scelta autonoma di automatica approvazione per ciascun Percorso di Studi (PdS). In alternativa, lo studente presenta la richiesta di piano di studi individuale che deve essere approvata dalla Commissione Didattica del Corso di Studi.
(***) Per i soli studenti stranieri questi CFU saranno concessi dopo il conseguimento di un certificato di conoscenza della lingua italiana (corsi erogati dal Centro Linguistico di Ateneo – CLA). Per gli italiani, i crediti potranno essere conseguiti acquisendo competenze tecniche esterne al corso di studi (e.g. partecipazione a seminari, congressi, attività di tutoraggio, ecc.).
(****) ll tirocinio extramoenia è svolto presso aziende, centri di ricerca o altri enti pubblici e/o privati, italiani o esteri, con affiancamento un tutor dell’azienda o dell’ente e la supervisione di un tutor universitario. Il tirocinio intramoenia è svolto presso laboratori di ricerca dell’ateneo con affiancamento di almeno un tutor universitario (docente o ricercatore).
LEGENDA
Tipologia di Attività Formativa (TAF):
B = Caratterizzante
C = Affine o Integrativa
D = Attività a scelta
E = Prova finale
F = Ulteriore attività formativa e conoscenze linguistiche
Elenco delle propedeuticità: Il percorso di studi non presenta propedeuticità.
| TABELLA I – SCELTA CURRICULARE II ANNO (12 CFU + 6 CFU) – TAF C | ||||||||||
| Codice esame | Denominazione insegnamento | Modulo | Settore | CFU | Sem | CdS di riferimento | ||||
| PdS FSE/PH – Curriculum Fundamental Sciences/Physics inspired Methodologies | ||||||||||
| U5450 | Advanced Statistical Learning and Modeling | Advanced Statistical Learning and Modeling Mod A | SECS-S/01 | STAT-01/A | 6 | 12 | I | DIETI – LM Data Science | ||
| Advanced Statistical Learning and Modeling Mod B | SECS-S/01 | STAT-01/A | 6 | I | DIETI – LM Data Science | |||||
| U7202 | Physics Informed Machine Learning | unico | MAT/08 | MATH-05/A | 6 | II | DIETI – LM Data Science | |||
| PdS FSE/MM – Curriculum Fundamental Sciences/Mathematical Methodologies | ||||||||||
| U5430 | Algorithms and Parallel Computing and Computational Complexity | Algorithms and Parallel Computing MOD A | INF/01 | INFO-01/A | 6 | 12 | II | DMRC – LM Ing. Matematica | ||
| Computational Complexity MOD B | INF/01 | INFO-01/A | 6 | I | DMRC – LM Ing. Matematica | |||||
| U1624 | Operational Research | unico | MAT/09 | MATH-06/A | 6 | I | DMRC – LM Ing. Matematica | |||
| PdS FSE/LS – Curriculum Fundamental Sciences/Life Sciences | ||||||||||
| U7216 | Biochemistry and computational biochemistry | Biochemestry | BIO/10 | BIOS-07/A | 6 | 12 | II | DIETI – LM Data Science | ||
| Computational Biochemestry | BIO/10 | BIOS-07/A | 6 | |||||||
| U1581 | Cell and Molecular Biology | unico | BIO/11 | BIOS-08/A | 6 | I | DICMAPI – LM Bioingegneria industriale | |||
| PdS ITE/TS – Curriculum Information Technologies/Text and Speech Processing | ||||||||||
| U7206 | Information Retrieval and Text Mining | Information Retrieval | ING-INF/05 | IINF-05/A | 6 | 12 | II | DIETI – LM Ing. Informatica | ||
| Text Mining | ING-INF/05 | IINF-05/A | 6 | I | DIETI – LM Data Science | |||||
| U6636 | Speech Processing | unico | L-LIN/01 | GLOT-01/A | 6 | II | DIETI – LM Data Science | |||
| PdS ITE/SV – Curriculum Information Technologies/Signal and Video Processing | ||||||||||
| U5444 | Information Theory and Signals Theory | Information Theory | ING-INF/03 | IINF-03/A | 6 | 12 | I | DMRC – LM Ing. Matematica | ||
| Signals Theory | ING-INF/03 | IINF-03/A | 6 | 2 | DMRC – LM Ing. Matematica | |||||
| U3423 | Image and Video Processing for Autonomous Driving | unico | ING-INF/03 | IINF-03/A | 6 | II | DII – LM Autonomous Vehicle Engineering | |||
| PdS ITE/RS – Curriculum Information Technologies/Robotics and Statistics | ||||||||||
| U2563 | Robotics | Robotic Systems | ING-INF/04 | IINF-04/A | 6 | 12 | I | DIETI – LM Ing. Informatica | ||
| Robotic Lab | ING-INF/04 | IINF-04/A | 6 | II | DIETI – LM Ing. Informatica | |||||
| U7207 | Advanced Statistical Learning | unico | SECS-S/01 | STAT-01/A | 6 | I | DIETI – LM Data Science | |||
| PdS ITE/IA – Curriculum Information Technologies/Industrial Applications | ||||||||||
| U5451 | Advanced Statistical Learning and Modeling | Advanced Statistical Learning and Modeling Mod A | SECS-S/01 | STAT-01/A | 6 | 12 | I | DIETI – LM Data Science | ||
| Advanced Statistical Learning and Modeling Mod B | SECS-S/01 | STAT-01/A | 6 | I | DIETI – LM Data Science | |||||
| U2659 | Statistical Methods for Industrial Process Monitoring | unico | SECS-S/02 | STAT-01/B | 6 | I | DMRC – LM Ing. Matematica | |||
| PdS ITE/AI – Curriculum Information Technologies/Design and Security of AI Systems | ||||||||||
| U7208 | Algorithm Design and Methods of AI | Algorithm Design | INF/01 | INFO-01/A | 6 | 12 | II | DIETI – LM Informatica | ||
| Methods for Artificial Intelligence | INF/01 | INFO-01/A | 6 | II | DIETI – LM Informatica | |||||
| U2652 | Data Security | unico | ING-INF/05 | IINF-05/A | 6 | I | DIETI – LM Data Science | |||
| PdS ECO – Curriculum Public Administration, Economy and Management | ||||||||||
| U7213 | Computational Statistical Inference and Categorical Data Analysis | Computational Statistical Inference | SECS-S/01 | STAT-01/A | 6 | 12 | I | DISES – LM Economia e Commercio | ||
| Categorical Data Analysis | SECS-S/01 | STAT-01/A | 6 | I | DISES – LM Economia e Commercio | |||||
| U6373 | Financial Time Series Analysis | unico | SECS-S/01 | STAT-01/A | 6 | I | DISES – LM Economics and Finance | |||
| PdS ISY – Curriculum Intelligent Systems | ||||||||||
| U7211 | Quantum Computation and Computational Intelligence | Mod I Theory | INF/01 | INFO-01/A | 6 | 12 | II | DFEP – LM Quantum Science and Engineering | ||
| Computational Intelligence | INF/01 | INFO-01/A | 6 | II | DIETI – LM Data Science | |||||
| U7215 | Generative Artificial Intelligence | unico | INF/01 | INFO-01/A | 6 | I | DIETI – LM Data Science | |||
LEGENDA:
DFEP: Dipartimento di Fisica Ettore Pancini; DIETI: Dipartimento di Ingegneria Elettrica e Tecnologie dell’Informazione; DII: Dipartimento di Ingegneria Industriale; DISES: Dipartimento di Scienze Economiche e Statistiche; DMRC: Dipartimento di Matematica e applicazioni Renato Caccioppoli; DICMAPI: Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale;
NI: Nuova Istituzione
| TABELLA 2 – Elenco degli insegnamenti a SCELTA AUTONOMA di automatica approvazione II ANNO (6 CFU + 6 CFU oppure 12 CFU) – TAF D | ||||||
| Codice esame | Denominazione insegnamento | SSD | CFU | Sem | CdS di riferimento | |
| Già insegnamenti o moduli di insegnamenti curriculari se non facenti parte del PdS scelto | ||||||
| U7207 | Advanced Statistical Learning | SECS-S/01 | STAT-01/A | 6 | I | DIETI – LM Data Science |
| U7218 | Advanced Statistical Modeling | SECS-S/01 | STAT-01/A | 6 | I | DIETI – LM Data Science |
| U1643 | Algorithms and Parallel Computing | INF/01 | INFO-01/A | 6 | II | DMRC – LM Ing. Matematica |
| U7219 | Computational Intelligence | INF/01 | INFO-01/A | 6 | II | DIETI – LM Data Science |
| U2653 | Data Security | ING-INF/05 | IINF-05/A | 6 | I | DIETI – LM Data Science |
| U7215 | Generative Artificial Intelligence | INF/01 | INFO-01/A | 6 | I | DIETI – LM Data Science |
| NI | Financial Time Series Analysis | SECS-S/01 | STAT-01/A | 6 | I | DISES – LM Econ. and Finance |
| U3423 | Image and Video Processing for Autonomous Driving | ING-INF/03 | IINF-03/A | 6 | II | DII – LM Autonomous Vehicle Engineering |
| U1644 | Information Theory | ING-INF/03 | IINF-03/A | 6 | I | DMRC – LM Ing. Matematica |
| U3522 | Methods for Artificial Intelligence | INF/01 | INFO-01/A | 6 | II | DIETI – LM Informatica |
| U5902 | Text Mining | ING-INF/05 | IINF-05/A | 6 | I | DIETI – LM Data Science |
| Altri insegnamenti | ||||||
| U5494 | AI Systems Engineering | ING-INF/05 | IINF-05/A | 6 | I | DIETI – LM Ing. Informatica |
| U4763 | Applied Quantum Systems | FIS/03 | PHYS-03/A | 6 | II | DFEP – LM Quantum Science and Engineering |
| U1205 | Astroinformatics | FIS/05 | PHYS-05/A | 6 | II | DFEP – LM Fisica |
| U3525 | Biometric Systems | INF/01 | INFO-01/A | 6 | II | DIETI – LM Informatica |
| U3523 | Computer Vision | INF/01 | INFO-01/A | 6 | I | DIETI – LM Informatica |
| U2658 | Data visualization | ING-INF/05 | IINF-05/A | 6 | II | DIETI – LM Data Science |
| U3536 | Human robot interaction | INF/01 | INFO-01/A | 6 | I | DIETI – LM Informatica |
| U3546 | Information Systems and Business Intelligence | ING-INF/05 | IINF-05/A | 6 | I | DIETI – LM Ing. Informatica |
| U3539 | Natural Language Processing | INF/01 | INFO-01/A | 6 | II | DIETI – LM Informatica |
| U1437 | Real and Functional Analysis | MAT/05 | MATH-03/A | 6 | I | DMRC – LM Ing. Matematica |
| U3835 | Reliability and Risk in Aerospace Engineering | SECS-S/02 | STAT-01/B | 6 | II | DII – LM Ing. Aerospaziale |
| U5937 | Software Architecture Design | ING-INF/05 | IINF-05/A | 6 | I | DIETI – LM Ing. Informatica |
| U6635 | Techniques of Text Analysis and Computational Linguistic | L-LIN/01 | GLOT-01/A | 6 | I | DIETI – LM Data Science |
| 25880 | Advanced Microeconomics | SECS-P/01 | ECON-01/A | 12 | I | DISES – LM Economics and Finance |
| 25881 | Advanced Macroeconomics | SECS-P/01 | ECON-01/A | 12 | II | DISES – LM Economics and Finance |
| 27381 | Economics of Regulation | SECS-P/03 | ECON-03/A | 6 | II | DISES – LM Economics and Finance |
| 27382 | Financial Econometrics | SECS-P/05 | ECON-05/A | 6 | II | DISES – LM Economics and Finance |
| 25884 | Mathematics for Economics and Finance | SECS-S/06 | STAT-04/A | 12 | I | DISES – LM Economics and Finance |
| NI | Metodi statistici per la ricerca sociale | SECS-S/05 | STAT-03/B | 6 | I | DISES – LM Economia e Commercio |
| NI | Preference learning | SECS-S/01 | STAT-01/A | 6 | I | DISES – LM Economia e Commercio |
| U6640 | SW and methods for statistical analysis of economic data | SECS-S/01 | STAT-01/A | 6 | II | DIETI – LM Data Science |