Corso di laurea: Data science for management
A.A. 2021/2022
Conoscenza e capacità di comprensione
Il CdS e progettato affinche i suoi laureati conseguano conoscenze e capacita di comprensione teorico-pratiche di livello avanzato nelle aree: informatico, statistico-matematico, economico-manageriale, acquisendo in particolare gli elementi fondamentali dell'approccio scientifico e quantitativo alla soluzione di problemi economico-aziendali che si troveranno ad affrontare nella loro attivita professionale.
Tenendo conto della specificita del CdLM, gli insegnamenti sono, di norma, integrati con attivita di esercitazione pratica e di laboratorio.
Modalita di acquisizione delle conoscenze
L'acquisizione delle conoscenze avviene principalmente attraverso la frequenza delle lezioni tenute dal docente, la frequenza delle attivita di laboratorio, la partecipazione a seminari condotti da esperti esterni (in rappresentanza del mondo professionale di riferimento del corso di studio).
Sono inoltre previste attivita fuori aula basate sullo studio individuale e di gruppo, utilizzo di materiali didattici integrativi messi a disposizione dai docenti per eventuali approfondimenti facoltativi, utilizzo di software dedicato, consultazione di materiale bibliografico.
In questo contesto vanno considerate anche l?esperienza di tirocinio e l?elaborazione della tesi di laurea.
Modalita di verifica delle conoscenze
La verifica dell'acquisizione delle conoscenze e della capacita di applicare le conoscenze e effettuata con le seguenti modalita, diversamente combinate secondo le specificita degli argomenti trattati: prove scritte, prove orali, presentazione di elaborati scritti e, per lo tirocinio, valutazione del tutor aziendale e dell'Universita.
La prova finale fornisce un'ulteriore opportunita di verifica della comprensione dei temi trattati nel CdLM.
Capacità di applicare conoscenza e comprensione
Il CdS e stato progettato affinche i laureati siano in grado di applicare le conoscenze e le competenze informatiche e statistico-matematiche alle analisi di dati inerenti alla implementazione di processi di innovazione tecnologica, utili per il management aziendale, per le azioni di marketing digitale e per la valutazione delle performance e degli effetti ex-ante e ex-post di politiche economiche.
L'apprendimento individuale e costantemente verificato attraverso esercitazioni e altre attivita d?aula, realizzazione di elaborati scritti e successiva discussione in aula, prove scritte e colloqui orali.
La capacita di applicare le conoscenze acquisite nel CdLM si esprime anche nella tesi di laurea; la tesi di laurea costituisce inoltre elemento di verifica della comprensione dei temi trattati nel CdLM.
Autonomia di giudizio
Il CdS e stato progettato affinche i laureati possano acquisire la capacita di formulare sintesi critiche interdisciplinari e giudizi autonomi fondati su solide analisi quantitative e conoscenze tecniche.
Le diverse competenze in ambito quantitativo, tecnologico e economico-manageriale consentono ai laureati di affrontare i diversi problemi fornendo un approccio integrato alla loro soluzione.
L'autonomia di giudizio viene sviluppata attraverso lo svolgimento di simulazioni, analisi di casi studio, discussioni in aula e attivita connessa alla redazione della tesi finale.
La verifica dell'autonomia di giudizio viene effettuata attraverso la valutazione delle prove scritte o orali inerenti ai singoli insegnamenti, valutazione di brevi saggi ed elaborati scritti, presentazione e discussione di casi aziendali, prova finale.
Abilità comunicative
Il CdS e stato progettato affinche i suoi laureati siano in grado di comunicare efficacemente risultati essenziali inerenti alle proprie analisi, argomentare le proprie posizioni, presentare proposte e soluzioni ai problemi affrontati (anche evidenziando diversi scenari), impiegando informazioni ed evidenze empiriche a sostegno delle proprie tesi, ad interlocutori interni ed esterni alle aziende, enti o istituzioni in cui operano, sia in lingua italiana che in lingua inglese.
Le abilita comunicative saranno sviluppate principalmente attraverso attivita in aula, partecipazione a gruppi di lavoro, presentazione di casi studio ed elaborati con particolare riferimento alla prova finale.
Le attivita di tirocinio, inoltre, contribuiscono fortemente allo sviluppo di abilita comunicative.
Le abilita comunicative scritte ed orali saranno verificate sia attraverso le prove d'esame scritte o orali sia in sede di valutazione della prova finale.
Capacità di apprendimento
IIl CdS e stato progettato affinche i suoi laureati sviluppino nel proprio percorso formativo le capacita di apprendimento necessarie sia per intraprendere percorsi autonomi di aggiornamento ed ulteriore sviluppo di conoscenze e competenze relative a data science e business analytics, nonche per proseguire con profitto gli studi (Master di II livello o Dottorato).
La capacita di apprendere e sviluppata soprattutto attraverso la partecipazione alle attivita didattiche in aula, lo studio individuale e la preparazione degli esami, la redazione di elaborati individuali o di gruppo.
La verifica del raggiungimento dell'obiettivo e effettuata attraverso la valutazione dei risultati di profitto nella didattica tradizionale, le valutazioni delle relazioni apposite dei tutor previsti per le attivita di stage e tirocinio, la valutazione della qualita della tesi di laurea.
Requisiti di ammissione
Possono accedere al CdLM in Data Science for Management, i laureati con titolo di studio nelle classi seguenti e nelle equivalenti classi ex D.M.
509/1999:
L-7 Ingegneria Civile e Ambientale
L-8 Ingegneria dell'Informazione
L-9 Ingegneria Industriale
L-18 Scienze dell'Economia e della Gestione Aziendale
L-30 Scienze e Tecnologie Fisiche
L-31 Scienze e Tecnologie Informatiche
L-33 Scienze Economiche
L-35 Scienze Matematiche
L-41 Statistica
Possono, altresi, accedere al CdLM in Data Science for Management i laureati con titolo di studio nelle classi seguenti e nelle equivalenti classi ex D.M.
509/1999:
L-32 Scienze e Tecnologie per l'Ambiente e la Natura
L-34 Scienze Geologiche
Purche abbiamo acquisito, in precedenza, almeno 9 CFU complessivamente nei settori: MAT-06, SECS-S/01 ed almeno 9 CFU complessivamente nei settori: INF/01, ING-INF/05.
Le modalita per l'accesso al corso di studio, per entrambi i gruppi di laureati, sono descritte nel regolamento didattico del Corso di Studio.
Per essere ammessi al corso di Laurea Magistrale in Data Science for Management occorre, inoltre, essere in possesso di adeguati requisiti curriculari nelle discipline della informatica, probabilita e statistica ed altresi, essere in grado di utilizzare fluentemente, in forma scritta e orale, la lingua inglese, con riferimento anche ai lessici disciplinari.
I requisiti curriculari e la verifica della personale preparazione sono disciplinati dal Regolamento didattico del corso di studio.
Prova finale
La prova finale consiste nella discussione, in lingua inglese, di una tesi di laurea, elaborata in modo originale sotto la guida di un relatore su tematiche congruenti con gli obiettivi del Corso di Laurea Magistrale, nella quale lo studente sia in grado di dimostrare piena padronanza dell'argomento trattato, la capacita di metterlo in relazione al contesto di riferimento, la capacita di operare in modo autonomo, e un'adeguata abilita di comunicazione.
Le modalita di svolgimento della prova finale sono disciplinate dal regolamento didattico del corso di studio.
Orientamento in ingresso
Per facilitare la scelta del percorso di studi e approfondire le opportunita offerte dal Corso di Laurea Magistrale, il Dipartimento di Economia e Impresa, di concerto con il Dipartimento di Matematica e Informatica e con il Dipartimento di Ingegneria Elettrica Elettronica e Informatica, prevede di organizzare incontri con studenti laureandi e laureati di primo livello al fine di illustrare il piano di studi, i saperi minimi necessari per accedere, gli obiettivi e gli sbocchi professionali del Corso.
Nel giugno 2020 e stato organizzato un open day dedicato ai corsi di laurea magistrale del dipartimento di Economia e Impresa in modalita telematica.
Il video dell'evento sara visibile su canale Youtube del Dipartimento.
Nella stessa direzione sono stati realizzati, dall'Universita di Catania, gli Open Days 2020 online (18,19 e 20 maggio) in cui veniva illustrata l'offerta formativa di ciascun dipartimento.
In particolare, il CdS in Data Science for Management e stato presentato all'interno dell'offerta formativa dei 3 dipartimenti che hanno istituito il corso: il Dipartimento di Economia e Impresa, il Dipartimento di Ingegneria Elettrica, Elettronica, e Informatica, e il Dipartimento di Matematica e Informatica.
I corrispondenti video di presentazione sono stati registrati e sono visibili rispettivamente alle pagine: https://www.aunpassodate.unict.it/economia, https://www.aunpassodate.unict.it/dmi, https://www.aunpassodate.unict.it/dieei.
Nell'agosto 2019 e stato realizzato, con il supporto dei tecnici della TV di Ateneo, un video di presentazione del CdLM in Data Science for Management a cui hanno partecipato alcuni docenti del CdLM che ne hanno riassunto le caratteristiche e gli sbocchi professionali.
Il video e stato pubblicizzato nel sito dell'Ateneo di Catania, del Dipartimento di Economia e Impresa ed e visibile su canale Youtube al link: https://www.youtube.com/watch?v=b3B4fHlFIng.
Il Corso di Studio in breve
Il corso di natura interdisciplinare intende formare laureati magistrali con elevate conoscenze in riferimento a:
a) raccolta, trattamento, compressione, archiviazione, sicurezza e analisi di dati, sia strutturati che non strutturati;
b) linguaggi di programmazione piu diffusi per l'analisi dati;
c) analisi statistica dei dati e statistical learning;
d) machine learning, neural computing e deep learning;
e) disegno di DB, anche di tipo "Big Data".
I laureati saranno in grado di:
a) dialogare con esperti dei campi applicativi e condurre analisi di dati ben finalizzate nei settori applicativi: scientifico, tecnologico ed economico-aziendale;
b) progettare iniziative di raccolta, pulizia, archiviazione e utilizzo efficiente e sicuro di dati, anche di tipo "Big Data";
c) comunicare correttamente e con precisione le conclusioni cui giungono le analisi dati condotte anche mediante l'utilizzo di strumenti grafici e interattivi avanzati.
I laureati del corso svilupperanno una mentalita prevalentemente quantitativa nell'analisi di fenomeni scientifici, economici e manageriali, sociali e sanitari.
Essi avranno attitudine al lavoro in team interdisciplinari e al dialogo con i destinatari finali delle analisi condotte, dialogo orientato al miglior utilizzo dei dati da raccogliere e studiare.
Essi avranno piena consapevolezza del prezioso e delicato ruolo della raccolta e analisi dati nella gestione di aziende, delle organizzazioni complesse, del monitoraggio e pianificazione di servizi pubblici anche nel campo della salute.
I laureati del corso saranno correttamente formati al rispetto dei principi etici nella raccolta, custodia ed utilizzo delle informazioni personali.
Particolarmente importanti per il raggiungimento degli obiettivi formativi saranno le occasioni di collaborazione e di affiancamento in aziende specializzate nel settore o in organizzazioni per cui la raccolta e analisi dati ha un ruolo cruciale nel monitoraggio e gestione.
Lo studente espliciterà le proprie scelte al momento della presentazione,
tramite il sistema informativo di ateneo, del piano di completamento o del piano di studio individuale,
secondo quanto stabilito dal regolamento didattico del corso di studio.
Data for sciences
Primo anno
Primo semestre
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Insegnamento
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CFU
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SSD
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Ore Lezione
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Ore Eserc.
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Ore Lab
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Ore Altro
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Ore Studio
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Attività
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Lingua
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Gruppo opzionale:
Supplementary course among "Fundamental of economics" and "Basics of computing" - (visualizza)
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9
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9793959 -
FUNDAMENTALS OF ECONOMICS
(obiettivi)
1.The objectives of the module aim at acquiring knowledge about: i) microfoundations of the economic behavior of individuals and firms and the understanding of markets; ii) economy-wide and large-scale phenomena and economic factors.
2.On completion, the student will be able: i) to interpret and address real-world microeconomic and macroeconomic problems; ii) to understand the open questions and issues on real-world economic problems.
3. On completion, students will able to understand the complexity and the trade-offs related to different market institutions and alternative economic policy measures.
4.On completion, students will be able how to present the results from the economic analyses, and which conclusions can be drawn from the analyses.
5.On completion, students will be able to address new questions and more sophisticated frameworks in economics.
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9
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SECS-P/01
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60
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-
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-
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-
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-
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Attività formative affini ed integrative
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ENG |
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9793954 -
BASICS OF COMPUTING
(obiettivi)
nowledge and understanding: the primary objective of the course is the students' acquisition of the "philosophy" of structured programming, as well as the detailed knowledge of syntax and semantics of the Python programming language. The course pays particular attention to the development of well-written and well-structured code using the basic techniques for software development in the imperative paradigm.
Applying knowledge and understanding: it intends to provide the tools to achieve the following practical and professional skills:
- To analyze computational problems and to code algorithmic ideas for their resolution;
- To design, to describe, to implement and debug Python programs with professional tools;
- To use built-in data structures for data management in scientific computing;
- Understanding simple recursive algorithms;
- To use specific libraries for scientific calculation;
- To read, to understand and analyze third-party Python code also in terms of efficiency;
- To be able to read documentation of libraries.
Making judgements: through the examination of code examples and numerous practice exercises, the learner will be able, both independently and in a cooperative manner, to analyze problems and design and implement related software solutions.
Communication skills: the student will acquire the necessary communication skills and expressive appropriateness in the use of technical verbal language in the context of computer programming.
Learning skills: the course aims to provide the learner with the necessary theoretical and practical methodologies to be used in professional contexts and, in particular, the ability to formulate and implement ad-hoc algorithms for solving new computational problems as well as the possibility of easily and quickly acquiring other programming languages.
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9
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INF/01
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60
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-
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-
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-
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-
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Attività formative affini ed integrative
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ENG |
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9793926 -
DATA BASE AND BIG DATA ANALYTICS
(obiettivi)
The course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases.
The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets.
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DATA BASE
(obiettivi)
The course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases.
The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets.
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6
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ING-INF/05
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40
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-
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-
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-
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-
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Attività formative caratterizzanti
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ENG |
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-
BIG DATA ANALYTICS
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Erogato anche in altro semestre o anno
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9793875 -
DATA ANALYSIS AND STATISTICAL LEARNING
(obiettivi)
1. Knowledge and understanding (Conoscenza e capacità di comprensione). The first “Statistical Learning” module mainly concerns the fundamentals of two of the main methods used in unsupervised learning: principal component analysis and cluster analysis.
2. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able: i) to implement the main methods used in unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly.
3. Making judgements (Autonomia di giudizio). On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software.
4. Communication skills (Abilità comunicative). On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn.
5. Learning skills (Capacità di apprendimento). On completion, the student will be able to understand the structure of unsupervised learning.
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DATA ANALYSIS
(obiettivi)
DATA ANALYSIS
1. Knowledge and understanding (Conoscenza e capacità di comprensione). The first “Statistical Learning” module mainly concerns the fundamentals of two of the main methods used in unsupervised learning: principal component analysis and cluster analysis.
2. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able: i) to implement the main methods used in unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly.
3. Making judgements (Autonomia di giudizio). On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software.
4. Communication skills (Abilità comunicative). On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn.
5. Learning skills (Capacità di apprendimento). On completion, the student will be able to understand the structure of unsupervised learning.
STATISTICAL LEARNING
Knowledge and understanding (Conoscenza e capacità di comprensione). The objectives of the module aim at acquiring knowledge about: i) setting of the learning problem and introducing the general model of the risk functional from empirical data; ii) main statistical learning techniques for regression and data classification.
Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, The student will be able: i) to implement main statistical models for supervised and unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly.
Making judgements (Autonomia di giudizio). On completion, students will able how to choose a suitable statistical model, apply sound statistical methods, and perform the analyses using statistical software
Communication skills (Abilità comunicative). On completion, students will be able how to present the results from the statistical analyses, and which conclusions can be drawn from the analyses.
Learning skills (Capacità di apprendimento). On completion, students will be able to understand the structure of the statistical learning.
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6
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SECS-S/01
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40
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-
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-
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-
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-
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Attività formative caratterizzanti
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ENG |
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-
STATISTICAL LEARNING
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Erogato anche in altro semestre o anno
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9793879 -
OPTIMIZATION
(obiettivi)
This graduated-level course introduces analytic tools and optimization methods that are suitable for large-scale problems arising in data science applications. The course presents both basic and advanced concepts of optimization and explores several algorithms that are efficient for network problems.
The student will acquire the ability to formulate, in mathematical terms, problems related to profit maximization and cost minimization, optimization of resources, and traffic network equilibria.
The goals of the course are:
Knowledge and understanding: the aim of the course is to acquire advanced knowledge that allows students to study optimization problems and model techniques of large-scale decision-making problems. The students will be able to use algorithms for both linear and nonlinear programming problems.
Applying knowledge and understanding: students will acquire knowledge useful to identify and model real-life decision-making problems. In addition, through real examples, the student will be able to implement correct solutions for complex problems.
Making judgments: students will be able to choose and solve autonomously complex decision-making problems and to interpret the solutions.
Communication skills: students will acquire base communication and reading skills using technical language.
Learning skills: the course provides students with theoretical and practical methodologies and skills to deal with large-scale optimization problems.
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6
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MAT/09
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40
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-
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-
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-
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-
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Attività formative caratterizzanti
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ENG |
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9793940 -
STATISTICAL LABORATORY
(obiettivi)
1.The objectives aim at introducing the knowledge of the R language for statistical data analysis with special focus on descriptive statistics, probability distributions and statistical inference.
2.On completion. Students will be able to utilize the R language for: i) providing basic statistical analyses of data; ii) simulating data according to given probability distributions; iii) applying main methods of statistical inference.
3.On completion, students will able to extract knowledge from data through statistical analyses in R.
4.On completion, students will be able how to present the results from the statistical analyses, based on the use of the statistical software R.
5.On completion, students will able how to utilize the statistical software R for basic data analysis and modeling.
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3
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-
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-
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36
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-
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-
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Ulteriori attività formative (art.10, comma 5, lettera d)
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ENG |
Secondo semestre
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Insegnamento
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CFU
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SSD
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Ore Lezione
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Ore Eserc.
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Ore Lab
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Ore Altro
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Ore Studio
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Attività
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Lingua
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9793926 -
DATA BASE AND BIG DATA ANALYTICS
(obiettivi)
The course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases.
The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets.
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-
DATA BASE
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Erogato anche in altro semestre o anno
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-
BIG DATA ANALYTICS
(obiettivi)
he course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases.
The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets.
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6
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ING-INF/05
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40
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-
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-
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-
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-
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Attività formative caratterizzanti
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ENG |
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9793875 -
DATA ANALYSIS AND STATISTICAL LEARNING
(obiettivi)
1. Knowledge and understanding (Conoscenza e capacità di comprensione). The first “Statistical Learning” module mainly concerns the fundamentals of two of the main methods used in unsupervised learning: principal component analysis and cluster analysis.
2. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able: i) to implement the main methods used in unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly.
3. Making judgements (Autonomia di giudizio). On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software.
4. Communication skills (Abilità comunicative). On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn.
5. Learning skills (Capacità di apprendimento). On completion, the student will be able to understand the structure of unsupervised learning.
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-
DATA ANALYSIS
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Erogato anche in altro semestre o anno
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-
STATISTICAL LEARNING
(obiettivi)
DATA ANALYSIS
1. Knowledge and understanding (Conoscenza e capacità di comprensione). The first “Statistical Learning” module mainly concerns the fundamentals of two of the main methods used in unsupervised learning: principal component analysis and cluster analysis.
2. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able: i) to implement the main methods used in unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly.
3. Making judgements (Autonomia di giudizio). On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software.
4. Communication skills (Abilità comunicative). On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn.
5. Learning skills (Capacità di apprendimento). On completion, the student will be able to understand the structure of unsupervised learning.
STATISTICAL LEARNING
Knowledge and understanding (Conoscenza e capacità di comprensione). The objectives of the module aim at acquiring knowledge about: i) setting of the learning problem and introducing the general model of the risk functional from empirical data; ii) main statistical learning techniques for regression and data classification.
Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, The student will be able: i) to implement main statistical models for supervised and unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly.
Making judgements (Autonomia di giudizio). On completion, students will able how to choose a suitable statistical model, apply sound statistical methods, and perform the analyses using statistical software
Communication skills (Abilità comunicative). On completion, students will be able how to present the results from the statistical analyses, and which conclusions can be drawn from the analyses.
Learning skills (Capacità di apprendimento). On completion, students will be able to understand the structure of the statistical learning.
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6
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SECS-S/01
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40
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-
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-
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-
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-
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Attività formative caratterizzanti
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ENG |
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9793878 -
BEHAVIORAL ECONOMICS AND COMPLEXITY
(obiettivi)
1.The course aims to build consciousness on the roots of complexity in human behaviors, with reference to their consequences on dynamic perspectives of economic relations. Main contributions of related literature will provide the methodological approach to understand socio-economic relations, by comparing theoretical models and empirical data.
2.The course will analyze both the micro- and the macro-economic perspective, by underlining, respectively, the behavioral approach in the individual choice paradigm and the emergent dynamics in collective phenomena. An essential introduction to agent-based modelling will be given, as one of the most adequate tools of analysis in the field.
3.The course will provide students with adequate abilities to distinguish complex phenomena and to reconcile correct modeling structures with socio-economic problems at hands.
4.The course has an experimental nature. It deals with borderline topics and non-standard approaches for economic analysis. Therefore, a specific effort will be done to help students learning the appropriate terminology and the ability to discuss actual aspects of studied concepts.
5.The course will be a starting point more than a consolidated set of results. All teaching materials, references and presented topics will create a toolbox for many possible future developments, for both further studies and professional applications.
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9
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SECS-P/02
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60
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-
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-
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-
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-
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Attività formative caratterizzanti
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ENG |
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9793876 -
DIGITAL INNOVATION AND TRANSFORMATION MANAGEMENT
(obiettivi)
Digital Innovation and Transformation Management provides a comprehensive suite of strategy concepts, tools, methods and perspectives to understand and manage your way through a digital transformation and to develop a strategic response to the emerging digital revolution and to then align your organization for effective strategy execution.
Digital Innovation and Transformation Management course is designed to lead and execute digital innovation initiatives and develop new business models for existing and nascent companies in a wide range of industries.
Future-proof your organization by leveraging human-centred innovation and the power of digital technologies, to tap into customer insights and to deliver sustained competitive advantage by developing new products and services, entrepreneurial initiatives, innovative start-ups, consulting in strategic, marketing and R&D management.
This course serves as the capstone course for the Data science for management program. As a capstone, the goal is to integrate technological and managerial perspectives.
The course provides a comprehensive suite of strategy concepts, tools, methods and perspectives to understand and manage your way through a digital transformation and to develop a strategic response to the emerging digital revolution and to then align your organization for effective strategy execution. The course is designed to lead and execute digital innovation initiatives and develop new business models for existing and nascent companies in entrepreneurial ecosystem.
Students will be able to learn the key theoretical and conceptual categories (knowledge and understanding) that show an entrepreneurial approach to digital innovation and transformation management.
Students will be able to apply professional scheme, models and tools learned using cases studies, also making judgements and contributing to lecture interactions during presentations and discussions in class (communication skills).
Students will be able to learn how to learn, since digital innovation generates changes and evolves over time.
|
9
|
SECS-P/08
|
60
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
Secondo anno
Primo semestre
|
Insegnamento
|
CFU
|
SSD
|
Ore Lezione
|
Ore Eserc.
|
Ore Lab
|
Ore Altro
|
Ore Studio
|
Attività
|
Lingua
|
|
9793877 -
ADVANCED MACHINE LEARNING AND KNOWLEDGE DISCOVERY
(obiettivi)
ADVANCED MACHINE LEARNING
The module will focus on the implementations of various machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries.
KNOWLEDGE DISCOVERY
This module covers the fundamental concepts of deep learning methods and how to use them for extracting, modelling and visualizing the learned knowledge.
Topics include: neural networks with backpropagation, convolutional neural networks, recurrent neural networks, methods for representation learning, and how to use them under different learning regimes (supervised, unsupervised and reinforcement learning) and in variety of real-world applications ranging from computer vision, machine translation and medical image analysis.
The learning objectives are:
to understand and use the main methodologies and techniques for learning from data
to understand the main methodologies to design and implement neural networks for real-world applications
to understand how to extract and learn knowledge in scenarios when supervision cannot be provided
to understand and foresee the reliability of machine learning methods in operational scenarios.
Knowledge and understanding
To understand the main concepts of learning from data
To understand concepts and tools for building intelligent systems using supervision and no supervision
To understand the most important machine learning and artificial intelligence methodologies and techniques used by industries to make sense of data in order to support the decision process
To understand what are the most appropriate techniques to be used in different real-world applications
Applying knowledge and understanding
To be able to effectively understand and use the main tools for creating, loading and manipulating datasets.
To design and implement from scratch a machine learning system following application-derived constraints in terms of modelling and data
To understand proper benchmarks and baselines and analysing achieved results and their generalization in real-world applications
To be able to apply methodologies and techniques to analyse data.
|
|
|
-
ADVANCED MACHINE LEARNING
(obiettivi)
ADVANCED MACHINE LEARNING
The module will focus on the implementations of various machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries.
KNOWLEDGE DISCOVERY
This module covers the fundamental concepts of deep learning methods and how to use them for extracting, modelling and visualizing the learned knowledge.
Topics include: neural networks with backpropagation, convolutional neural networks, recurrent neural networks, methods for representation learning, and how to use them under different learning regimes (supervised, unsupervised and reinforcement learning) and in variety of real-world applications ranging from computer vision, machine translation and medical image analysis.
The learning objectives are:
to understand and use the main methodologies and techniques for learning from data
to understand the main methodologies to design and implement neural networks for real-world applications
to understand how to extract and learn knowledge in scenarios when supervision cannot be provided
to understand and foresee the reliability of machine learning methods in operational scenarios.
Knowledge and understanding
To understand the main concepts of learning from data
To understand concepts and tools for building intelligent systems using supervision and no supervision
To understand the most important machine learning and artificial intelligence methodologies and techniques used by industries to make sense of data in order to support the decision process
To understand what are the most appropriate techniques to be used in different real-world applications
Applying knowledge and understanding
To be able to effectively understand and use the main tools for creating, loading and manipulating datasets.
To design and implement from scratch a machine learning system following application-derived constraints in terms of modelling and data
To understand proper benchmarks and baselines and analysing achieved results and their generalization in real-world applications
To be able to apply methodologies and techniques to analyse data.
|
6
|
INF/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
-
KNOWLEDGE DISCOVERY
|
Erogato anche in altro semestre o anno
|
|
9793880 -
COMPUTER SECURITY AND DATA PROTECTION
(obiettivi)
Nowadays data controllers must design information systems that provide the highest possible privacy guarantees. A fundamental enabler to achieve this is cryptography.
This class is intended to provide an introduction to the main concepts of modern cryptography and their usage to protect data e build secure systems. The main focus will be on constructions of various building blocks, such as encryption schemes, message authentication codes and digital signatures. We will try to understand what properties we expect from these objects, how to define these properties and how to construct schemes that realize them. We will also focus on schemes that are widely used in practice. These include, for instance, AES, SHA, HMAC and RSA. However, rather than using these tools as black box, we will show how they are built and the security level they provide. No programming will be required for this class.
The goals of this course, in terms of expected results, are
Knowledge and understanding (Conoscenza e capacità di comprensione). Students will learn the fundamental ideas and principles underlying modern cryptography and modern secure systems.
Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able to securely use cryptographic tools like encryption schema and digital signatures and to understand their exact role in secure systems.
Making judgements (Autonomia di giudizio). By studying concrete examples and common mistakes students will learn how to use solutions that providee high security guarantees.
Communication skills (Abilità comunicative). On completion, students will acquire communication skills that will allow them to fluently communicate using the technical language of computer security.
Learning skills (Capacità di apprendimento). On completion, students will acquire methodologies that will allow them to securely deal with problems that require the usage of secure solutions.
|
6
|
INF/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
9793881 -
NEURAL COMPUTING
(obiettivi)
The course covers the theory and practice of artificial neural networks, highlighting their relevance both for artificial intelligence applications and for modeling human cognition and brain function. Theoretical discussion of various types of neural networks and learning algorithms is complemented by hands-on practices in the computer lab. Models for classification and regression, as well as neural network architectures (e.g., Deep Learning) will be discussed. The course will present the techniques to design and optimize learning algorithms, and those useful to assess the performance of Machine Learning systems.
|
6
|
INF/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
Gruppo opzionale:
SUPPLEMENTARY COURSES - (visualizza)
|
12
|
|
|
|
|
|
|
|
|
9793955 -
CLOUD COMPUTING AND BIG DATA
(obiettivi)
Knowledge and understanding (Conoscenza e capacità di comprensione). Students will acquire a precise knowledge and understanding of fundamental concepts in the field of cloud computing, chiefly through a guided exploration of the main technological solutions available from the public Cloud, focusing on resources and services oriented to data storage, analysis, visualization and machine learning.
Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). Based on the operating knowledge acquired, students will develop an effective "toolset" of practical, application-oriented skills in leveraging the Cloud to cater for the typical needs of a data scientist: i.e. processing large datasets with a view to revealing meaningful patterns and relationships. Cloud implementations of state-of-the-art tools and frameworks like, e.g., MapReduce/Hadoop or TensorFlow, will be employed
Making judgements (Autonomia di giudizio). The student will develop the ability to choose the suitable Cloud-based resource for the Data Science scenario of interest, properly estimating the ensuing costs and performance gains, as well as consciously assessing the tradeoffs involved.
Communication skills (Abilità comunicative). The student will acquire the communication skills required to express and discuss, at a rigorous technical level, the benefits and (mostly cost-related) downsides of the Cloud for Data Science applications. In addition, the student will gain the ability, for presentation purposes, to effectively highlight the features of very large datasets by means of cloud-based visualization services.
Learning skills (Capacità di apprendimento). Students will become capable of profitably consulting technical documentation concerning Data Science-oriented Cloud services, in order to concretely put them to effective use
|
6
|
INF/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793943 -
BIG DATA SENSING, COMPRESSION AND COMMUNICATION
(obiettivi)
Data is growing and has grown very fast in the last years.”Big Data” analytics is challenging today because of the unprecedented large data volumes. In this course, we will describe the structure of data generated in big data sensing applications, by distinguishing the type and structure of data. Then we will discuss SoA methodologies which can be used to compress this data based on its intrinsic features; finally, communication protocols for remotely delivering this data will be described and detailed. In this way students will be provided with communication engineering competences allowing them to actively communicate with experts in various fields by providing focused and competent data analysis for every application, such as in scientific, technological or business fields. Students will also be able to exploit the competences gained for design processes of collection, compression and communication of heterogeneous big data.
This course will be of interest for students attending all paths for the following reasons:
For “Business and economics data scientists”, this course will allow to complement their business competences being able also to communicate with technical experts in the field.
For students in “Data driven applications for the IoT” and “Data for sciences”, this is a fundamental course to well understand the intrinsic nature of IoT big data.
|
6
|
ING-INF/03
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793930 -
MODELLING AND ANALYSIS FOR COMPLEX SYSTEMS
(obiettivi)
Knowledge and understanding. Students will learn the fundamental concepts of stationary processes and time series, how to estimate the features of a process, the main structures of prediction models, how to identify models starting from time series and how to validate models.
Applying knowledge and understanding. Students will be able to identify linear and non-linear models starting from time series by using popular software tools, such as MATLAB toolboxes, and validate their performances. Case studies will be proposed by using various kinds of dataset.
Making judgements. Students will be able to judge on the potential and limits of the model identification theory proposed in the course.
Communication skills. Students will be able to illustrate the basic aspects of model identification theory, interact and collaborate in teams with other experts.
Learning skills. Students will be able to autonomously extend their knowledge, drawing on the vast literature available in the field of time series model identification.
|
6
|
ING-INF/04
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793931 -
DATA AND METHODS FOR PUBLIC POLICIES EVALUATION
(obiettivi)
This course aims to present the theory and practice of
empirical research in public economics with particular
emphasis on the assessment of public programs in the
market economies. The course will develop analytical
knowledge of the main tools of quantitative
evaluations which underpin public interventions
efficiency and outcomes. Public policy applications
include the main programs in welfare (i.e. health,
education, and social sector).
|
6
|
SECS-P/03
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793932 -
CREDIT RISK MANAGEMENT
(obiettivi)
Knowledge and understanding (Conoscenza e capacità di comprensione). Basic knowledge related to the recognition and management of financial risks from the core of the management processes of financial intermediaries.
Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able to apply the measurement models of the main risks analyzed (credit risk, operational risk, interest rate risk on the banking book, liquidity risk)
Making judgements (Autonomia di giudizio). The ability to research, analyze and process public data and information also collected through scientific researches, with specific reference to the strategies up to the study of the problems concerning the assessment processes of the specific risks that banks incur in the activity of lending.
Communication skills (Abilità comunicative). On completion, the student will be able to communicate the results obtained, the problems encountered and the lessons learned, also based on independent judgment.
Learning skills (Capacità di apprendimento). On completion, the student will be able to individuate the components of credit risk, the organizational aspects of credit risk management and to choose the assessment tools for the diagnosis of credit risk.
|
6
|
SECS-P/11
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793937 -
DATA ANALYSIS FOR PUBLIC HEALTH
(obiettivi)
Il corso fornisce conoscenze specifiche sui metodi e tecniche per affrontare i principali temi di Sanità Pubblica. In particolare, al termine del corso, gli studenti dovranno possedere: i) buona conoscenza degli strumenti e dei metodi necessari alla formulazione di quesiti e obiettivi della propria ricerca; ii) conoscenza approfondita dei metodi statistici e degli strumenti informatici per l'elaborazione del piano di analisi statistica e per l'interpretazione dei risultati; iii) capacità di comunicazione utilizzando il linguaggio proprio della Sanità Pubblica e conoscenza del contesto biomedico. Queste abilità vengono acquisite attraverso lezioni attive e interattive, seminari, laboratori ed esercitazioni e attraverso lo studio del materiale didattico.
|
6
|
MED/42
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793970 -
PRIVATE LAW FOR INFORMATION TECHNOLOGY
(obiettivi)
Knowledge and understanding
The course of Private Law for Information Technology aims to analyze the impact of the Internet on legal rules and to identify the regulation of Internet behavior, with particular reference to the relationships between private subjects and to the complexity of the diffusion of artificial intelligence. The course aims also to give the keys to an adequate knowledge and understanding, as well as the normative sources and the relative interpretative principles, of the fundamental institutions of private law strictly connected to the Web in terms of subject regulation and protection of personal data, of the goods and of the circulation of internet rights, of the contract in general and of contracts on line.
2. Applying knowledge and understanding
At the end of the course, the student will be able to understand the legal issues raised by the technological context and to identify the solutions, both by reconstructing and interpreting the different situations and the legally relevant interests in the network on the private level, both by applying in practice the knowledge and tools acquired during the course of lessons and developed with a careful study of the subject.
3. Making judgements
The rapid development of information technology has exacerbated the need for robust personal data protection, the right to which is safeguarded by both European Union (EU) and Council of Europe (CoE) instruments. Safeguarding this important right entails new and significant challenges as technological advances expand the frontiers of areas such as surveillance, communication interception and data storage. This course is designed to familiarise students not specialised in data protection with this emerging area of the law. In particular, the student will be placed in a position to distinguish between positive and negative aspects, advantages and disadvantages associated with data protection regulation and the possible options between alternative forms of protection ready by the legislature. The teaching tends to let the student's ability to understand and explain key case law, summarising major rulings of both the Court of Justice of the European Union and the European Court of Human Rights.
4. Communication skills
The course must put the student in a position to transfer and apply the knowledge gained outside, using an exposure mode logical argument conforms to the main principles of matter and also suits a technically appropriate legal language. In particular, he must be able to provide opinions, advice and assistance on issues object of study.
5. Learning skills
Exceeding the matter must be based on a rigorous assessment in the examination of the achievement by the student of a level of preparation and competence that allows him to approach the study of the other teachings, also not legal, but which may present significant implications with them.
|
6
|
IUS/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9796145 -
POLITICAL SCIENCE RESEARCH DESIGN AND METHODS
(obiettivi)
The goal of this course is to familiarize the students
with research in political science with quantitative
data. They will learn how to formulate (scientifically)
questions about the political world finding answers
using the logic of the scientific method.
On completion, students will learn how to measure political
concepts, how to define a research design and methods of data
collection; how to use statistical and graphical techniques for
describing data; and the principles of statistical inference
|
6
|
SPS/04
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9796144 -
SURVEY DESIGN AND QUESTIONNAIRE DATA ANALYSIS
(obiettivi)
The course will give the main concepts and techniques
for the design of questionnaires and data analysis of
collected data. On completion, students will acquire
knowledge about: i) design of a statistical survey; ii)
techniques for questionnaire design; iii) methods for
statistical analysis of collected data and for providing
statistical reports.
|
6
|
SECS-S/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9795551 -
PREFERENCE MODELING AND CHOICE THEORY
|
6
|
SECS-S/06
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ITA |
|
Secondo semestre
|
Insegnamento
|
CFU
|
SSD
|
Ore Lezione
|
Ore Eserc.
|
Ore Lab
|
Ore Altro
|
Ore Studio
|
Attività
|
Lingua
|
|
9793877 -
ADVANCED MACHINE LEARNING AND KNOWLEDGE DISCOVERY
(obiettivi)
ADVANCED MACHINE LEARNING
The module will focus on the implementations of various machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries.
KNOWLEDGE DISCOVERY
This module covers the fundamental concepts of deep learning methods and how to use them for extracting, modelling and visualizing the learned knowledge.
Topics include: neural networks with backpropagation, convolutional neural networks, recurrent neural networks, methods for representation learning, and how to use them under different learning regimes (supervised, unsupervised and reinforcement learning) and in variety of real-world applications ranging from computer vision, machine translation and medical image analysis.
The learning objectives are:
to understand and use the main methodologies and techniques for learning from data
to understand the main methodologies to design and implement neural networks for real-world applications
to understand how to extract and learn knowledge in scenarios when supervision cannot be provided
to understand and foresee the reliability of machine learning methods in operational scenarios.
Knowledge and understanding
To understand the main concepts of learning from data
To understand concepts and tools for building intelligent systems using supervision and no supervision
To understand the most important machine learning and artificial intelligence methodologies and techniques used by industries to make sense of data in order to support the decision process
To understand what are the most appropriate techniques to be used in different real-world applications
Applying knowledge and understanding
To be able to effectively understand and use the main tools for creating, loading and manipulating datasets.
To design and implement from scratch a machine learning system following application-derived constraints in terms of modelling and data
To understand proper benchmarks and baselines and analysing achieved results and their generalization in real-world applications
To be able to apply methodologies and techniques to analyse data.
|
|
|
-
ADVANCED MACHINE LEARNING
|
Erogato anche in altro semestre o anno
|
|
-
KNOWLEDGE DISCOVERY
(obiettivi)
ADVANCED MACHINE LEARNING
The module will focus on the implementations of various machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries.
KNOWLEDGE DISCOVERY
This module covers the fundamental concepts of deep learning methods and how to use them for extracting, modelling and visualizing the learned knowledge.
Topics include: neural networks with backpropagation, convolutional neural networks, recurrent neural networks, methods for representation learning, and how to use them under different learning regimes (supervised, unsupervised and reinforcement learning) and in variety of real-world applications ranging from computer vision, machine translation and medical image analysis.
The learning objectives are:
to understand and use the main methodologies and techniques for learning from data
to understand the main methodologies to design and implement neural networks for real-world applications
to understand how to extract and learn knowledge in scenarios when supervision cannot be provided
to understand and foresee the reliability of machine learning methods in operational scenarios.
Knowledge and understanding
To understand the main concepts of learning from data
To understand concepts and tools for building intelligent systems using supervision and no supervision
To understand the most important machine learning and artificial intelligence methodologies and techniques used by industries to make sense of data in order to support the decision process
To understand what are the most appropriate techniques to be used in different real-world applications
Applying knowledge and understanding
To be able to effectively understand and use the main tools for creating, loading and manipulating datasets.
To design and implement from scratch a machine learning system following application-derived constraints in terms of modelling and data
To understand proper benchmarks and baselines and analysing achieved results and their generalization in real-world applications
To be able to apply methodologies and techniques to analyse data.
|
6
|
ING-INF/05
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
Gruppo opzionale:
SUPPLEMENTARY COURSES - (visualizza)
|
12
|
|
|
|
|
|
|
|
|
9793955 -
CLOUD COMPUTING AND BIG DATA
(obiettivi)
Knowledge and understanding (Conoscenza e capacità di comprensione). Students will acquire a precise knowledge and understanding of fundamental concepts in the field of cloud computing, chiefly through a guided exploration of the main technological solutions available from the public Cloud, focusing on resources and services oriented to data storage, analysis, visualization and machine learning.
Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). Based on the operating knowledge acquired, students will develop an effective "toolset" of practical, application-oriented skills in leveraging the Cloud to cater for the typical needs of a data scientist: i.e. processing large datasets with a view to revealing meaningful patterns and relationships. Cloud implementations of state-of-the-art tools and frameworks like, e.g., MapReduce/Hadoop or TensorFlow, will be employed
Making judgements (Autonomia di giudizio). The student will develop the ability to choose the suitable Cloud-based resource for the Data Science scenario of interest, properly estimating the ensuing costs and performance gains, as well as consciously assessing the tradeoffs involved.
Communication skills (Abilità comunicative). The student will acquire the communication skills required to express and discuss, at a rigorous technical level, the benefits and (mostly cost-related) downsides of the Cloud for Data Science applications. In addition, the student will gain the ability, for presentation purposes, to effectively highlight the features of very large datasets by means of cloud-based visualization services.
Learning skills (Capacità di apprendimento). Students will become capable of profitably consulting technical documentation concerning Data Science-oriented Cloud services, in order to concretely put them to effective use
|
6
|
INF/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793943 -
BIG DATA SENSING, COMPRESSION AND COMMUNICATION
(obiettivi)
Data is growing and has grown very fast in the last years.”Big Data” analytics is challenging today because of the unprecedented large data volumes. In this course, we will describe the structure of data generated in big data sensing applications, by distinguishing the type and structure of data. Then we will discuss SoA methodologies which can be used to compress this data based on its intrinsic features; finally, communication protocols for remotely delivering this data will be described and detailed. In this way students will be provided with communication engineering competences allowing them to actively communicate with experts in various fields by providing focused and competent data analysis for every application, such as in scientific, technological or business fields. Students will also be able to exploit the competences gained for design processes of collection, compression and communication of heterogeneous big data.
This course will be of interest for students attending all paths for the following reasons:
For “Business and economics data scientists”, this course will allow to complement their business competences being able also to communicate with technical experts in the field.
For students in “Data driven applications for the IoT” and “Data for sciences”, this is a fundamental course to well understand the intrinsic nature of IoT big data.
|
6
|
ING-INF/03
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793930 -
MODELLING AND ANALYSIS FOR COMPLEX SYSTEMS
(obiettivi)
Knowledge and understanding. Students will learn the fundamental concepts of stationary processes and time series, how to estimate the features of a process, the main structures of prediction models, how to identify models starting from time series and how to validate models.
Applying knowledge and understanding. Students will be able to identify linear and non-linear models starting from time series by using popular software tools, such as MATLAB toolboxes, and validate their performances. Case studies will be proposed by using various kinds of dataset.
Making judgements. Students will be able to judge on the potential and limits of the model identification theory proposed in the course.
Communication skills. Students will be able to illustrate the basic aspects of model identification theory, interact and collaborate in teams with other experts.
Learning skills. Students will be able to autonomously extend their knowledge, drawing on the vast literature available in the field of time series model identification.
|
6
|
ING-INF/04
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793931 -
DATA AND METHODS FOR PUBLIC POLICIES EVALUATION
(obiettivi)
This course aims to present the theory and practice of
empirical research in public economics with particular
emphasis on the assessment of public programs in the
market economies. The course will develop analytical
knowledge of the main tools of quantitative
evaluations which underpin public interventions
efficiency and outcomes. Public policy applications
include the main programs in welfare (i.e. health,
education, and social sector).
|
6
|
SECS-P/03
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793932 -
CREDIT RISK MANAGEMENT
(obiettivi)
Knowledge and understanding (Conoscenza e capacità di comprensione). Basic knowledge related to the recognition and management of financial risks from the core of the management processes of financial intermediaries.
Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able to apply the measurement models of the main risks analyzed (credit risk, operational risk, interest rate risk on the banking book, liquidity risk)
Making judgements (Autonomia di giudizio). The ability to research, analyze and process public data and information also collected through scientific researches, with specific reference to the strategies up to the study of the problems concerning the assessment processes of the specific risks that banks incur in the activity of lending.
Communication skills (Abilità comunicative). On completion, the student will be able to communicate the results obtained, the problems encountered and the lessons learned, also based on independent judgment.
Learning skills (Capacità di apprendimento). On completion, the student will be able to individuate the components of credit risk, the organizational aspects of credit risk management and to choose the assessment tools for the diagnosis of credit risk.
|
6
|
SECS-P/11
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793937 -
DATA ANALYSIS FOR PUBLIC HEALTH
(obiettivi)
Il corso fornisce conoscenze specifiche sui metodi e tecniche per affrontare i principali temi di Sanità Pubblica. In particolare, al termine del corso, gli studenti dovranno possedere: i) buona conoscenza degli strumenti e dei metodi necessari alla formulazione di quesiti e obiettivi della propria ricerca; ii) conoscenza approfondita dei metodi statistici e degli strumenti informatici per l'elaborazione del piano di analisi statistica e per l'interpretazione dei risultati; iii) capacità di comunicazione utilizzando il linguaggio proprio della Sanità Pubblica e conoscenza del contesto biomedico. Queste abilità vengono acquisite attraverso lezioni attive e interattive, seminari, laboratori ed esercitazioni e attraverso lo studio del materiale didattico.
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6
|
MED/42
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40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793970 -
PRIVATE LAW FOR INFORMATION TECHNOLOGY
(obiettivi)
Knowledge and understanding
The course of Private Law for Information Technology aims to analyze the impact of the Internet on legal rules and to identify the regulation of Internet behavior, with particular reference to the relationships between private subjects and to the complexity of the diffusion of artificial intelligence. The course aims also to give the keys to an adequate knowledge and understanding, as well as the normative sources and the relative interpretative principles, of the fundamental institutions of private law strictly connected to the Web in terms of subject regulation and protection of personal data, of the goods and of the circulation of internet rights, of the contract in general and of contracts on line.
2. Applying knowledge and understanding
At the end of the course, the student will be able to understand the legal issues raised by the technological context and to identify the solutions, both by reconstructing and interpreting the different situations and the legally relevant interests in the network on the private level, both by applying in practice the knowledge and tools acquired during the course of lessons and developed with a careful study of the subject.
3. Making judgements
The rapid development of information technology has exacerbated the need for robust personal data protection, the right to which is safeguarded by both European Union (EU) and Council of Europe (CoE) instruments. Safeguarding this important right entails new and significant challenges as technological advances expand the frontiers of areas such as surveillance, communication interception and data storage. This course is designed to familiarise students not specialised in data protection with this emerging area of the law. In particular, the student will be placed in a position to distinguish between positive and negative aspects, advantages and disadvantages associated with data protection regulation and the possible options between alternative forms of protection ready by the legislature. The teaching tends to let the student's ability to understand and explain key case law, summarising major rulings of both the Court of Justice of the European Union and the European Court of Human Rights.
4. Communication skills
The course must put the student in a position to transfer and apply the knowledge gained outside, using an exposure mode logical argument conforms to the main principles of matter and also suits a technically appropriate legal language. In particular, he must be able to provide opinions, advice and assistance on issues object of study.
5. Learning skills
Exceeding the matter must be based on a rigorous assessment in the examination of the achievement by the student of a level of preparation and competence that allows him to approach the study of the other teachings, also not legal, but which may present significant implications with them.
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6
|
IUS/01
|
40
|
-
|
-
|
-
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-
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Attività formative affini ed integrative
|
ENG |
|
9796145 -
POLITICAL SCIENCE RESEARCH DESIGN AND METHODS
(obiettivi)
The goal of this course is to familiarize the students
with research in political science with quantitative
data. They will learn how to formulate (scientifically)
questions about the political world finding answers
using the logic of the scientific method.
On completion, students will learn how to measure political
concepts, how to define a research design and methods of data
collection; how to use statistical and graphical techniques for
describing data; and the principles of statistical inference
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6
|
SPS/04
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40
|
-
|
-
|
-
|
-
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Attività formative affini ed integrative
|
ENG |
|
9796144 -
SURVEY DESIGN AND QUESTIONNAIRE DATA ANALYSIS
(obiettivi)
The course will give the main concepts and techniques
for the design of questionnaires and data analysis of
collected data. On completion, students will acquire
knowledge about: i) design of a statistical survey; ii)
techniques for questionnaire design; iii) methods for
statistical analysis of collected data and for providing
statistical reports.
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6
|
SECS-S/01
|
40
|
-
|
-
|
-
|
-
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Attività formative affini ed integrative
|
ENG |
|
9795551 -
PREFERENCE MODELING AND CHOICE THEORY
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6
|
SECS-S/06
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40
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-
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-
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-
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-
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Attività formative affini ed integrative
|
ITA |
|
|
9793963 -
ELECTIVE COURSE
(obiettivi)
...................................
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12
|
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80
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-
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-
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-
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-
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Attività formative a scelta dello studente (art.10, comma 5, lettera a)
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ENG |
|
9795513 -
Final dissertation
(obiettivi)
.........................
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|
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-
Research for and writing of the final dissertation
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10
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-
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-
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-
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250
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-
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Per la prova finale e la lingua straniera (art.10, comma 5, lettera c)
|
ITA |
|
-
Presentation and discussion of the final dissertation
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2
|
|
-
|
-
|
-
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50
|
-
|
Per la prova finale e la lingua straniera (art.10, comma 5, lettera c)
|
ITA |
Data driven applications for IoT
Primo anno
Primo semestre
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Insegnamento
|
CFU
|
SSD
|
Ore Lezione
|
Ore Eserc.
|
Ore Lab
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Ore Altro
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Ore Studio
|
Attività
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Lingua
|
|
Gruppo opzionale:
Supplementary course among "Fundamental of economics" and "Basics of computing" - (visualizza)
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9
|
|
|
|
|
|
|
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9793959 -
FUNDAMENTALS OF ECONOMICS
(obiettivi)
1.The objectives of the module aim at acquiring knowledge about: i) microfoundations of the economic behavior of individuals and firms and the understanding of markets; ii) economy-wide and large-scale phenomena and economic factors.
2.On completion, the student will be able: i) to interpret and address real-world microeconomic and macroeconomic problems; ii) to understand the open questions and issues on real-world economic problems.
3. On completion, students will able to understand the complexity and the trade-offs related to different market institutions and alternative economic policy measures.
4.On completion, students will be able how to present the results from the economic analyses, and which conclusions can be drawn from the analyses.
5.On completion, students will be able to address new questions and more sophisticated frameworks in economics.
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9
|
SECS-P/01
|
60
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793954 -
BASICS OF COMPUTING
(obiettivi)
nowledge and understanding: the primary objective of the course is the students' acquisition of the "philosophy" of structured programming, as well as the detailed knowledge of syntax and semantics of the Python programming language. The course pays particular attention to the development of well-written and well-structured code using the basic techniques for software development in the imperative paradigm.
Applying knowledge and understanding: it intends to provide the tools to achieve the following practical and professional skills:
- To analyze computational problems and to code algorithmic ideas for their resolution;
- To design, to describe, to implement and debug Python programs with professional tools;
- To use built-in data structures for data management in scientific computing;
- Understanding simple recursive algorithms;
- To use specific libraries for scientific calculation;
- To read, to understand and analyze third-party Python code also in terms of efficiency;
- To be able to read documentation of libraries.
Making judgements: through the examination of code examples and numerous practice exercises, the learner will be able, both independently and in a cooperative manner, to analyze problems and design and implement related software solutions.
Communication skills: the student will acquire the necessary communication skills and expressive appropriateness in the use of technical verbal language in the context of computer programming.
Learning skills: the course aims to provide the learner with the necessary theoretical and practical methodologies to be used in professional contexts and, in particular, the ability to formulate and implement ad-hoc algorithms for solving new computational problems as well as the possibility of easily and quickly acquiring other programming languages.
|
9
|
INF/01
|
60
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
|
9793926 -
DATA BASE AND BIG DATA ANALYTICS
(obiettivi)
The course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases.
The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets.
|
|
|
-
DATA BASE
(obiettivi)
The course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases.
The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets.
|
6
|
ING-INF/05
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
-
BIG DATA ANALYTICS
|
Erogato anche in altro semestre o anno
|
|
9793875 -
DATA ANALYSIS AND STATISTICAL LEARNING
(obiettivi)
1. Knowledge and understanding (Conoscenza e capacità di comprensione). The first “Statistical Learning” module mainly concerns the fundamentals of two of the main methods used in unsupervised learning: principal component analysis and cluster analysis.
2. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able: i) to implement the main methods used in unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly.
3. Making judgements (Autonomia di giudizio). On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software.
4. Communication skills (Abilità comunicative). On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn.
5. Learning skills (Capacità di apprendimento). On completion, the student will be able to understand the structure of unsupervised learning.
|
|
|
-
DATA ANALYSIS
(obiettivi)
DATA ANALYSIS
1. Knowledge and understanding (Conoscenza e capacità di comprensione). The first “Statistical Learning” module mainly concerns the fundamentals of two of the main methods used in unsupervised learning: principal component analysis and cluster analysis.
2. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able: i) to implement the main methods used in unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly.
3. Making judgements (Autonomia di giudizio). On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software.
4. Communication skills (Abilità comunicative). On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn.
5. Learning skills (Capacità di apprendimento). On completion, the student will be able to understand the structure of unsupervised learning.
STATISTICAL LEARNING
Knowledge and understanding (Conoscenza e capacità di comprensione). The objectives of the module aim at acquiring knowledge about: i) setting of the learning problem and introducing the general model of the risk functional from empirical data; ii) main statistical learning techniques for regression and data classification.
Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, The student will be able: i) to implement main statistical models for supervised and unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly.
Making judgements (Autonomia di giudizio). On completion, students will able how to choose a suitable statistical model, apply sound statistical methods, and perform the analyses using statistical software
Communication skills (Abilità comunicative). On completion, students will be able how to present the results from the statistical analyses, and which conclusions can be drawn from the analyses.
Learning skills (Capacità di apprendimento). On completion, students will be able to understand the structure of the statistical learning.
|
6
|
SECS-S/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
-
STATISTICAL LEARNING
|
Erogato anche in altro semestre o anno
|
|
9793879 -
OPTIMIZATION
(obiettivi)
This graduated-level course introduces analytic tools and optimization methods that are suitable for large-scale problems arising in data science applications. The course presents both basic and advanced concepts of optimization and explores several algorithms that are efficient for network problems.
The student will acquire the ability to formulate, in mathematical terms, problems related to profit maximization and cost minimization, optimization of resources, and traffic network equilibria.
The goals of the course are:
Knowledge and understanding: the aim of the course is to acquire advanced knowledge that allows students to study optimization problems and model techniques of large-scale decision-making problems. The students will be able to use algorithms for both linear and nonlinear programming problems.
Applying knowledge and understanding: students will acquire knowledge useful to identify and model real-life decision-making problems. In addition, through real examples, the student will be able to implement correct solutions for complex problems.
Making judgments: students will be able to choose and solve autonomously complex decision-making problems and to interpret the solutions.
Communication skills: students will acquire base communication and reading skills using technical language.
Learning skills: the course provides students with theoretical and practical methodologies and skills to deal with large-scale optimization problems.
|
6
|
MAT/09
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
9793940 -
STATISTICAL LABORATORY
(obiettivi)
1.The objectives aim at introducing the knowledge of the R language for statistical data analysis with special focus on descriptive statistics, probability distributions and statistical inference.
2.On completion. Students will be able to utilize the R language for: i) providing basic statistical analyses of data; ii) simulating data according to given probability distributions; iii) applying main methods of statistical inference.
3.On completion, students will able to extract knowledge from data through statistical analyses in R.
4.On completion, students will be able how to present the results from the statistical analyses, based on the use of the statistical software R.
5.On completion, students will able how to utilize the statistical software R for basic data analysis and modeling.
|
3
|
|
-
|
-
|
36
|
-
|
-
|
Ulteriori attività formative (art.10, comma 5, lettera d)
|
ENG |
Secondo semestre
|
Insegnamento
|
CFU
|
SSD
|
Ore Lezione
|
Ore Eserc.
|
Ore Lab
|
Ore Altro
|
Ore Studio
|
Attività
|
Lingua
|
|
9793926 -
DATA BASE AND BIG DATA ANALYTICS
(obiettivi)
The course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases.
The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets.
|
|
|
-
DATA BASE
|
Erogato anche in altro semestre o anno
|
|
-
BIG DATA ANALYTICS
(obiettivi)
he course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases.
The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets.
|
6
|
ING-INF/05
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
9793875 -
DATA ANALYSIS AND STATISTICAL LEARNING
(obiettivi)
1. Knowledge and understanding (Conoscenza e capacità di comprensione). The first “Statistical Learning” module mainly concerns the fundamentals of two of the main methods used in unsupervised learning: principal component analysis and cluster analysis.
2. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able: i) to implement the main methods used in unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly.
3. Making judgements (Autonomia di giudizio). On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software.
4. Communication skills (Abilità comunicative). On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn.
5. Learning skills (Capacità di apprendimento). On completion, the student will be able to understand the structure of unsupervised learning.
|
|
|
-
DATA ANALYSIS
|
Erogato anche in altro semestre o anno
|
|
-
STATISTICAL LEARNING
(obiettivi)
DATA ANALYSIS
1. Knowledge and understanding (Conoscenza e capacità di comprensione). The first “Statistical Learning” module mainly concerns the fundamentals of two of the main methods used in unsupervised learning: principal component analysis and cluster analysis.
2. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able: i) to implement the main methods used in unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly.
3. Making judgements (Autonomia di giudizio). On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software.
4. Communication skills (Abilità comunicative). On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn.
5. Learning skills (Capacità di apprendimento). On completion, the student will be able to understand the structure of unsupervised learning.
STATISTICAL LEARNING
Knowledge and understanding (Conoscenza e capacità di comprensione). The objectives of the module aim at acquiring knowledge about: i) setting of the learning problem and introducing the general model of the risk functional from empirical data; ii) main statistical learning techniques for regression and data classification.
Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, The student will be able: i) to implement main statistical models for supervised and unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly.
Making judgements (Autonomia di giudizio). On completion, students will able how to choose a suitable statistical model, apply sound statistical methods, and perform the analyses using statistical software
Communication skills (Abilità comunicative). On completion, students will be able how to present the results from the statistical analyses, and which conclusions can be drawn from the analyses.
Learning skills (Capacità di apprendimento). On completion, students will be able to understand the structure of the statistical learning.
|
6
|
SECS-S/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
9793878 -
BEHAVIORAL ECONOMICS AND COMPLEXITY
(obiettivi)
1.The course aims to build consciousness on the roots of complexity in human behaviors, with reference to their consequences on dynamic perspectives of economic relations. Main contributions of related literature will provide the methodological approach to understand socio-economic relations, by comparing theoretical models and empirical data.
2.The course will analyze both the micro- and the macro-economic perspective, by underlining, respectively, the behavioral approach in the individual choice paradigm and the emergent dynamics in collective phenomena. An essential introduction to agent-based modelling will be given, as one of the most adequate tools of analysis in the field.
3.The course will provide students with adequate abilities to distinguish complex phenomena and to reconcile correct modeling structures with socio-economic problems at hands.
4.The course has an experimental nature. It deals with borderline topics and non-standard approaches for economic analysis. Therefore, a specific effort will be done to help students learning the appropriate terminology and the ability to discuss actual aspects of studied concepts.
5.The course will be a starting point more than a consolidated set of results. All teaching materials, references and presented topics will create a toolbox for many possible future developments, for both further studies and professional applications.
|
9
|
SECS-P/02
|
60
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
9793876 -
DIGITAL INNOVATION AND TRANSFORMATION MANAGEMENT
(obiettivi)
Digital Innovation and Transformation Management provides a comprehensive suite of strategy concepts, tools, methods and perspectives to understand and manage your way through a digital transformation and to develop a strategic response to the emerging digital revolution and to then align your organization for effective strategy execution.
Digital Innovation and Transformation Management course is designed to lead and execute digital innovation initiatives and develop new business models for existing and nascent companies in a wide range of industries.
Future-proof your organization by leveraging human-centred innovation and the power of digital technologies, to tap into customer insights and to deliver sustained competitive advantage by developing new products and services, entrepreneurial initiatives, innovative start-ups, consulting in strategic, marketing and R&D management.
This course serves as the capstone course for the Data science for management program. As a capstone, the goal is to integrate technological and managerial perspectives.
The course provides a comprehensive suite of strategy concepts, tools, methods and perspectives to understand and manage your way through a digital transformation and to develop a strategic response to the emerging digital revolution and to then align your organization for effective strategy execution. The course is designed to lead and execute digital innovation initiatives and develop new business models for existing and nascent companies in entrepreneurial ecosystem.
Students will be able to learn the key theoretical and conceptual categories (knowledge and understanding) that show an entrepreneurial approach to digital innovation and transformation management.
Students will be able to apply professional scheme, models and tools learned using cases studies, also making judgements and contributing to lecture interactions during presentations and discussions in class (communication skills).
Students will be able to learn how to learn, since digital innovation generates changes and evolves over time.
|
9
|
SECS-P/08
|
60
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
Secondo anno
Primo semestre
|
Insegnamento
|
CFU
|
SSD
|
Ore Lezione
|
Ore Eserc.
|
Ore Lab
|
Ore Altro
|
Ore Studio
|
Attività
|
Lingua
|
|
9793877 -
ADVANCED MACHINE LEARNING AND KNOWLEDGE DISCOVERY
(obiettivi)
ADVANCED MACHINE LEARNING
The module will focus on the implementations of various machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries.
KNOWLEDGE DISCOVERY
This module covers the fundamental concepts of deep learning methods and how to use them for extracting, modelling and visualizing the learned knowledge.
Topics include: neural networks with backpropagation, convolutional neural networks, recurrent neural networks, methods for representation learning, and how to use them under different learning regimes (supervised, unsupervised and reinforcement learning) and in variety of real-world applications ranging from computer vision, machine translation and medical image analysis.
The learning objectives are:
to understand and use the main methodologies and techniques for learning from data
to understand the main methodologies to design and implement neural networks for real-world applications
to understand how to extract and learn knowledge in scenarios when supervision cannot be provided
to understand and foresee the reliability of machine learning methods in operational scenarios.
Knowledge and understanding
To understand the main concepts of learning from data
To understand concepts and tools for building intelligent systems using supervision and no supervision
To understand the most important machine learning and artificial intelligence methodologies and techniques used by industries to make sense of data in order to support the decision process
To understand what are the most appropriate techniques to be used in different real-world applications
Applying knowledge and understanding
To be able to effectively understand and use the main tools for creating, loading and manipulating datasets.
To design and implement from scratch a machine learning system following application-derived constraints in terms of modelling and data
To understand proper benchmarks and baselines and analysing achieved results and their generalization in real-world applications
To be able to apply methodologies and techniques to analyse data.
|
|
|
-
ADVANCED MACHINE LEARNING
(obiettivi)
ADVANCED MACHINE LEARNING
The module will focus on the implementations of various machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries.
KNOWLEDGE DISCOVERY
This module covers the fundamental concepts of deep learning methods and how to use them for extracting, modelling and visualizing the learned knowledge.
Topics include: neural networks with backpropagation, convolutional neural networks, recurrent neural networks, methods for representation learning, and how to use them under different learning regimes (supervised, unsupervised and reinforcement learning) and in variety of real-world applications ranging from computer vision, machine translation and medical image analysis.
The learning objectives are:
to understand and use the main methodologies and techniques for learning from data
to understand the main methodologies to design and implement neural networks for real-world applications
to understand how to extract and learn knowledge in scenarios when supervision cannot be provided
to understand and foresee the reliability of machine learning methods in operational scenarios.
Knowledge and understanding
To understand the main concepts of learning from data
To understand concepts and tools for building intelligent systems using supervision and no supervision
To understand the most important machine learning and artificial intelligence methodologies and techniques used by industries to make sense of data in order to support the decision process
To understand what are the most appropriate techniques to be used in different real-world applications
Applying knowledge and understanding
To be able to effectively understand and use the main tools for creating, loading and manipulating datasets.
To design and implement from scratch a machine learning system following application-derived constraints in terms of modelling and data
To understand proper benchmarks and baselines and analysing achieved results and their generalization in real-world applications
To be able to apply methodologies and techniques to analyse data.
|
6
|
INF/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
-
KNOWLEDGE DISCOVERY
|
Erogato anche in altro semestre o anno
|
|
9793927 -
IoT AND BIG DATA FOR SMART SPACES
(obiettivi)
Knowledge and Understanding: On completion of the course, the student shall 1) Know the key technological components underpinning IoT, 2) Understand IoT Architectures and the application of IoT in various domains, 3) Know the difference among networking protocols in the context of resource-constrained IoT devices, and 4) Know how Big Data can be exploited in the context of Smart Spaces.
Applying Knowledge and Understanding: On completion of the course, the student shall be able to analyze and select the appropriate technological solutions for Smart Spaces enabled by IoT and Big Data collection and analysis.
Making Judgements: Completing the course, the student will be able to judge the suitability, the capabilities, and the limitations of IoT based applications in the context of Smart Spaces. Further, the student will be able to identify issues, problems, or misleading results.
Communication Skills: On completion of the course, the student will be able to illustrate the theoretical and technical properties which characterize IoT based Smart Environments. The student will be able to interact and collaborate with peers and experts in the realization of a project or research.
Learning Skills: On completion of the course, the student will be able to autonomously extend the knowledge acquired during the study course by reading and understanding scientific and technical documentation.
|
6
|
ING-INF/05
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
9793928 -
BIG DATA FOR SMART MANUFACTURING
(obiettivi)
Knowledge and understanding. On completion of the course, the student shall 1) know the basic principles of the smart manufacturing according to the novel IC technologies adopted in the modern industry, and 2) understand methodologies and techniques used in industries to realise the “Failure Prediction and Predictive Maintenance” .
Applying knowledge and understanding. On completion of the course, the student will be able to select the appropriate technological solutions in predictive maintenance.
Making judgements. On completion of the course, the student will be able to choose a suitable data science model for each of the subjects treated inside the course.
Communication skills. On completion of the course, the student can communicate his conclusions and recommendations about data science applications in smart manufacturing with the argumentation of the knowledge and rationale underpinning these, to both specialist and non-specialist audiences clearly and unambiguously.
Learning skills. On completion, the student will be able to continue to study in a manner that may be largely selfdirected or autonomous.
|
6
|
ING-INF/05
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
Gruppo opzionale:
SUPPLEMENTARY COURSES - (visualizza)
|
12
|
|
|
|
|
|
|
|
|
9793955 -
CLOUD COMPUTING AND BIG DATA
(obiettivi)
Knowledge and understanding (Conoscenza e capacità di comprensione). Students will acquire a precise knowledge and understanding of fundamental concepts in the field of cloud computing, chiefly through a guided exploration of the main technological solutions available from the public Cloud, focusing on resources and services oriented to data storage, analysis, visualization and machine learning.
Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). Based on the operating knowledge acquired, students will develop an effective "toolset" of practical, application-oriented skills in leveraging the Cloud to cater for the typical needs of a data scientist: i.e. processing large datasets with a view to revealing meaningful patterns and relationships. Cloud implementations of state-of-the-art tools and frameworks like, e.g., MapReduce/Hadoop or TensorFlow, will be employed
Making judgements (Autonomia di giudizio). The student will develop the ability to choose the suitable Cloud-based resource for the Data Science scenario of interest, properly estimating the ensuing costs and performance gains, as well as consciously assessing the tradeoffs involved.
Communication skills (Abilità comunicative). The student will acquire the communication skills required to express and discuss, at a rigorous technical level, the benefits and (mostly cost-related) downsides of the Cloud for Data Science applications. In addition, the student will gain the ability, for presentation purposes, to effectively highlight the features of very large datasets by means of cloud-based visualization services.
Learning skills (Capacità di apprendimento). Students will become capable of profitably consulting technical documentation concerning Data Science-oriented Cloud services, in order to concretely put them to effective use
|
6
|
INF/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793943 -
BIG DATA SENSING, COMPRESSION AND COMMUNICATION
(obiettivi)
Data is growing and has grown very fast in the last years.”Big Data” analytics is challenging today because of the unprecedented large data volumes. In this course, we will describe the structure of data generated in big data sensing applications, by distinguishing the type and structure of data. Then we will discuss SoA methodologies which can be used to compress this data based on its intrinsic features; finally, communication protocols for remotely delivering this data will be described and detailed. In this way students will be provided with communication engineering competences allowing them to actively communicate with experts in various fields by providing focused and competent data analysis for every application, such as in scientific, technological or business fields. Students will also be able to exploit the competences gained for design processes of collection, compression and communication of heterogeneous big data.
This course will be of interest for students attending all paths for the following reasons:
For “Business and economics data scientists”, this course will allow to complement their business competences being able also to communicate with technical experts in the field.
For students in “Data driven applications for the IoT” and “Data for sciences”, this is a fundamental course to well understand the intrinsic nature of IoT big data.
|
6
|
ING-INF/03
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793930 -
MODELLING AND ANALYSIS FOR COMPLEX SYSTEMS
(obiettivi)
Knowledge and understanding. Students will learn the fundamental concepts of stationary processes and time series, how to estimate the features of a process, the main structures of prediction models, how to identify models starting from time series and how to validate models.
Applying knowledge and understanding. Students will be able to identify linear and non-linear models starting from time series by using popular software tools, such as MATLAB toolboxes, and validate their performances. Case studies will be proposed by using various kinds of dataset.
Making judgements. Students will be able to judge on the potential and limits of the model identification theory proposed in the course.
Communication skills. Students will be able to illustrate the basic aspects of model identification theory, interact and collaborate in teams with other experts.
Learning skills. Students will be able to autonomously extend their knowledge, drawing on the vast literature available in the field of time series model identification.
|
6
|
ING-INF/04
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793931 -
DATA AND METHODS FOR PUBLIC POLICIES EVALUATION
(obiettivi)
This course aims to present the theory and practice of
empirical research in public economics with particular
emphasis on the assessment of public programs in the
market economies. The course will develop analytical
knowledge of the main tools of quantitative
evaluations which underpin public interventions
efficiency and outcomes. Public policy applications
include the main programs in welfare (i.e. health,
education, and social sector).
|
6
|
SECS-P/03
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793932 -
CREDIT RISK MANAGEMENT
(obiettivi)
Knowledge and understanding (Conoscenza e capacità di comprensione). Basic knowledge related to the recognition and management of financial risks from the core of the management processes of financial intermediaries.
Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able to apply the measurement models of the main risks analyzed (credit risk, operational risk, interest rate risk on the banking book, liquidity risk)
Making judgements (Autonomia di giudizio). The ability to research, analyze and process public data and information also collected through scientific researches, with specific reference to the strategies up to the study of the problems concerning the assessment processes of the specific risks that banks incur in the activity of lending.
Communication skills (Abilità comunicative). On completion, the student will be able to communicate the results obtained, the problems encountered and the lessons learned, also based on independent judgment.
Learning skills (Capacità di apprendimento). On completion, the student will be able to individuate the components of credit risk, the organizational aspects of credit risk management and to choose the assessment tools for the diagnosis of credit risk.
|
6
|
SECS-P/11
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793937 -
DATA ANALYSIS FOR PUBLIC HEALTH
(obiettivi)
Il corso fornisce conoscenze specifiche sui metodi e tecniche per affrontare i principali temi di Sanità Pubblica. In particolare, al termine del corso, gli studenti dovranno possedere: i) buona conoscenza degli strumenti e dei metodi necessari alla formulazione di quesiti e obiettivi della propria ricerca; ii) conoscenza approfondita dei metodi statistici e degli strumenti informatici per l'elaborazione del piano di analisi statistica e per l'interpretazione dei risultati; iii) capacità di comunicazione utilizzando il linguaggio proprio della Sanità Pubblica e conoscenza del contesto biomedico. Queste abilità vengono acquisite attraverso lezioni attive e interattive, seminari, laboratori ed esercitazioni e attraverso lo studio del materiale didattico.
|
6
|
MED/42
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793970 -
PRIVATE LAW FOR INFORMATION TECHNOLOGY
(obiettivi)
Knowledge and understanding
The course of Private Law for Information Technology aims to analyze the impact of the Internet on legal rules and to identify the regulation of Internet behavior, with particular reference to the relationships between private subjects and to the complexity of the diffusion of artificial intelligence. The course aims also to give the keys to an adequate knowledge and understanding, as well as the normative sources and the relative interpretative principles, of the fundamental institutions of private law strictly connected to the Web in terms of subject regulation and protection of personal data, of the goods and of the circulation of internet rights, of the contract in general and of contracts on line.
2. Applying knowledge and understanding
At the end of the course, the student will be able to understand the legal issues raised by the technological context and to identify the solutions, both by reconstructing and interpreting the different situations and the legally relevant interests in the network on the private level, both by applying in practice the knowledge and tools acquired during the course of lessons and developed with a careful study of the subject.
3. Making judgements
The rapid development of information technology has exacerbated the need for robust personal data protection, the right to which is safeguarded by both European Union (EU) and Council of Europe (CoE) instruments. Safeguarding this important right entails new and significant challenges as technological advances expand the frontiers of areas such as surveillance, communication interception and data storage. This course is designed to familiarise students not specialised in data protection with this emerging area of the law. In particular, the student will be placed in a position to distinguish between positive and negative aspects, advantages and disadvantages associated with data protection regulation and the possible options between alternative forms of protection ready by the legislature. The teaching tends to let the student's ability to understand and explain key case law, summarising major rulings of both the Court of Justice of the European Union and the European Court of Human Rights.
4. Communication skills
The course must put the student in a position to transfer and apply the knowledge gained outside, using an exposure mode logical argument conforms to the main principles of matter and also suits a technically appropriate legal language. In particular, he must be able to provide opinions, advice and assistance on issues object of study.
5. Learning skills
Exceeding the matter must be based on a rigorous assessment in the examination of the achievement by the student of a level of preparation and competence that allows him to approach the study of the other teachings, also not legal, but which may present significant implications with them.
|
6
|
IUS/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9796145 -
POLITICAL SCIENCE RESEARCH DESIGN AND METHODS
(obiettivi)
The goal of this course is to familiarize the students
with research in political science with quantitative
data. They will learn how to formulate (scientifically)
questions about the political world finding answers
using the logic of the scientific method.
On completion, students will learn how to measure political
concepts, how to define a research design and methods of data
collection; how to use statistical and graphical techniques for
describing data; and the principles of statistical inference
|
6
|
SPS/04
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9796144 -
SURVEY DESIGN AND QUESTIONNAIRE DATA ANALYSIS
(obiettivi)
The course will give the main concepts and techniques
for the design of questionnaires and data analysis of
collected data. On completion, students will acquire
knowledge about: i) design of a statistical survey; ii)
techniques for questionnaire design; iii) methods for
statistical analysis of collected data and for providing
statistical reports.
|
6
|
SECS-S/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9795551 -
PREFERENCE MODELING AND CHOICE THEORY
|
6
|
SECS-S/06
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ITA |
|
Secondo semestre
|
Insegnamento
|
CFU
|
SSD
|
Ore Lezione
|
Ore Eserc.
|
Ore Lab
|
Ore Altro
|
Ore Studio
|
Attività
|
Lingua
|
|
9793877 -
ADVANCED MACHINE LEARNING AND KNOWLEDGE DISCOVERY
(obiettivi)
ADVANCED MACHINE LEARNING
The module will focus on the implementations of various machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries.
KNOWLEDGE DISCOVERY
This module covers the fundamental concepts of deep learning methods and how to use them for extracting, modelling and visualizing the learned knowledge.
Topics include: neural networks with backpropagation, convolutional neural networks, recurrent neural networks, methods for representation learning, and how to use them under different learning regimes (supervised, unsupervised and reinforcement learning) and in variety of real-world applications ranging from computer vision, machine translation and medical image analysis.
The learning objectives are:
to understand and use the main methodologies and techniques for learning from data
to understand the main methodologies to design and implement neural networks for real-world applications
to understand how to extract and learn knowledge in scenarios when supervision cannot be provided
to understand and foresee the reliability of machine learning methods in operational scenarios.
Knowledge and understanding
To understand the main concepts of learning from data
To understand concepts and tools for building intelligent systems using supervision and no supervision
To understand the most important machine learning and artificial intelligence methodologies and techniques used by industries to make sense of data in order to support the decision process
To understand what are the most appropriate techniques to be used in different real-world applications
Applying knowledge and understanding
To be able to effectively understand and use the main tools for creating, loading and manipulating datasets.
To design and implement from scratch a machine learning system following application-derived constraints in terms of modelling and data
To understand proper benchmarks and baselines and analysing achieved results and their generalization in real-world applications
To be able to apply methodologies and techniques to analyse data.
|
|
|
-
ADVANCED MACHINE LEARNING
|
Erogato anche in altro semestre o anno
|
|
-
KNOWLEDGE DISCOVERY
(obiettivi)
ADVANCED MACHINE LEARNING
The module will focus on the implementations of various machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries.
KNOWLEDGE DISCOVERY
This module covers the fundamental concepts of deep learning methods and how to use them for extracting, modelling and visualizing the learned knowledge.
Topics include: neural networks with backpropagation, convolutional neural networks, recurrent neural networks, methods for representation learning, and how to use them under different learning regimes (supervised, unsupervised and reinforcement learning) and in variety of real-world applications ranging from computer vision, machine translation and medical image analysis.
The learning objectives are:
to understand and use the main methodologies and techniques for learning from data
to understand the main methodologies to design and implement neural networks for real-world applications
to understand how to extract and learn knowledge in scenarios when supervision cannot be provided
to understand and foresee the reliability of machine learning methods in operational scenarios.
Knowledge and understanding
To understand the main concepts of learning from data
To understand concepts and tools for building intelligent systems using supervision and no supervision
To understand the most important machine learning and artificial intelligence methodologies and techniques used by industries to make sense of data in order to support the decision process
To understand what are the most appropriate techniques to be used in different real-world applications
Applying knowledge and understanding
To be able to effectively understand and use the main tools for creating, loading and manipulating datasets.
To design and implement from scratch a machine learning system following application-derived constraints in terms of modelling and data
To understand proper benchmarks and baselines and analysing achieved results and their generalization in real-world applications
To be able to apply methodologies and techniques to analyse data.
|
6
|
ING-INF/05
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
9793963 -
ELECTIVE COURSE
(obiettivi)
...................................
|
12
|
|
80
|
-
|
-
|
-
|
-
|
Attività formative a scelta dello studente (art.10, comma 5, lettera a)
|
ENG |
|
Gruppo opzionale:
SUPPLEMENTARY COURSES - (visualizza)
|
12
|
|
|
|
|
|
|
|
|
9793955 -
CLOUD COMPUTING AND BIG DATA
(obiettivi)
Knowledge and understanding (Conoscenza e capacità di comprensione). Students will acquire a precise knowledge and understanding of fundamental concepts in the field of cloud computing, chiefly through a guided exploration of the main technological solutions available from the public Cloud, focusing on resources and services oriented to data storage, analysis, visualization and machine learning.
Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). Based on the operating knowledge acquired, students will develop an effective "toolset" of practical, application-oriented skills in leveraging the Cloud to cater for the typical needs of a data scientist: i.e. processing large datasets with a view to revealing meaningful patterns and relationships. Cloud implementations of state-of-the-art tools and frameworks like, e.g., MapReduce/Hadoop or TensorFlow, will be employed
Making judgements (Autonomia di giudizio). The student will develop the ability to choose the suitable Cloud-based resource for the Data Science scenario of interest, properly estimating the ensuing costs and performance gains, as well as consciously assessing the tradeoffs involved.
Communication skills (Abilità comunicative). The student will acquire the communication skills required to express and discuss, at a rigorous technical level, the benefits and (mostly cost-related) downsides of the Cloud for Data Science applications. In addition, the student will gain the ability, for presentation purposes, to effectively highlight the features of very large datasets by means of cloud-based visualization services.
Learning skills (Capacità di apprendimento). Students will become capable of profitably consulting technical documentation concerning Data Science-oriented Cloud services, in order to concretely put them to effective use
|
6
|
INF/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793943 -
BIG DATA SENSING, COMPRESSION AND COMMUNICATION
(obiettivi)
Data is growing and has grown very fast in the last years.”Big Data” analytics is challenging today because of the unprecedented large data volumes. In this course, we will describe the structure of data generated in big data sensing applications, by distinguishing the type and structure of data. Then we will discuss SoA methodologies which can be used to compress this data based on its intrinsic features; finally, communication protocols for remotely delivering this data will be described and detailed. In this way students will be provided with communication engineering competences allowing them to actively communicate with experts in various fields by providing focused and competent data analysis for every application, such as in scientific, technological or business fields. Students will also be able to exploit the competences gained for design processes of collection, compression and communication of heterogeneous big data.
This course will be of interest for students attending all paths for the following reasons:
For “Business and economics data scientists”, this course will allow to complement their business competences being able also to communicate with technical experts in the field.
For students in “Data driven applications for the IoT” and “Data for sciences”, this is a fundamental course to well understand the intrinsic nature of IoT big data.
|
6
|
ING-INF/03
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793930 -
MODELLING AND ANALYSIS FOR COMPLEX SYSTEMS
(obiettivi)
Knowledge and understanding. Students will learn the fundamental concepts of stationary processes and time series, how to estimate the features of a process, the main structures of prediction models, how to identify models starting from time series and how to validate models.
Applying knowledge and understanding. Students will be able to identify linear and non-linear models starting from time series by using popular software tools, such as MATLAB toolboxes, and validate their performances. Case studies will be proposed by using various kinds of dataset.
Making judgements. Students will be able to judge on the potential and limits of the model identification theory proposed in the course.
Communication skills. Students will be able to illustrate the basic aspects of model identification theory, interact and collaborate in teams with other experts.
Learning skills. Students will be able to autonomously extend their knowledge, drawing on the vast literature available in the field of time series model identification.
|
6
|
ING-INF/04
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793931 -
DATA AND METHODS FOR PUBLIC POLICIES EVALUATION
(obiettivi)
This course aims to present the theory and practice of
empirical research in public economics with particular
emphasis on the assessment of public programs in the
market economies. The course will develop analytical
knowledge of the main tools of quantitative
evaluations which underpin public interventions
efficiency and outcomes. Public policy applications
include the main programs in welfare (i.e. health,
education, and social sector).
|
6
|
SECS-P/03
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793932 -
CREDIT RISK MANAGEMENT
(obiettivi)
Knowledge and understanding (Conoscenza e capacità di comprensione). Basic knowledge related to the recognition and management of financial risks from the core of the management processes of financial intermediaries.
Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able to apply the measurement models of the main risks analyzed (credit risk, operational risk, interest rate risk on the banking book, liquidity risk)
Making judgements (Autonomia di giudizio). The ability to research, analyze and process public data and information also collected through scientific researches, with specific reference to the strategies up to the study of the problems concerning the assessment processes of the specific risks that banks incur in the activity of lending.
Communication skills (Abilità comunicative). On completion, the student will be able to communicate the results obtained, the problems encountered and the lessons learned, also based on independent judgment.
Learning skills (Capacità di apprendimento). On completion, the student will be able to individuate the components of credit risk, the organizational aspects of credit risk management and to choose the assessment tools for the diagnosis of credit risk.
|
6
|
SECS-P/11
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793937 -
DATA ANALYSIS FOR PUBLIC HEALTH
(obiettivi)
Il corso fornisce conoscenze specifiche sui metodi e tecniche per affrontare i principali temi di Sanità Pubblica. In particolare, al termine del corso, gli studenti dovranno possedere: i) buona conoscenza degli strumenti e dei metodi necessari alla formulazione di quesiti e obiettivi della propria ricerca; ii) conoscenza approfondita dei metodi statistici e degli strumenti informatici per l'elaborazione del piano di analisi statistica e per l'interpretazione dei risultati; iii) capacità di comunicazione utilizzando il linguaggio proprio della Sanità Pubblica e conoscenza del contesto biomedico. Queste abilità vengono acquisite attraverso lezioni attive e interattive, seminari, laboratori ed esercitazioni e attraverso lo studio del materiale didattico.
|
6
|
MED/42
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793970 -
PRIVATE LAW FOR INFORMATION TECHNOLOGY
(obiettivi)
Knowledge and understanding
The course of Private Law for Information Technology aims to analyze the impact of the Internet on legal rules and to identify the regulation of Internet behavior, with particular reference to the relationships between private subjects and to the complexity of the diffusion of artificial intelligence. The course aims also to give the keys to an adequate knowledge and understanding, as well as the normative sources and the relative interpretative principles, of the fundamental institutions of private law strictly connected to the Web in terms of subject regulation and protection of personal data, of the goods and of the circulation of internet rights, of the contract in general and of contracts on line.
2. Applying knowledge and understanding
At the end of the course, the student will be able to understand the legal issues raised by the technological context and to identify the solutions, both by reconstructing and interpreting the different situations and the legally relevant interests in the network on the private level, both by applying in practice the knowledge and tools acquired during the course of lessons and developed with a careful study of the subject.
3. Making judgements
The rapid development of information technology has exacerbated the need for robust personal data protection, the right to which is safeguarded by both European Union (EU) and Council of Europe (CoE) instruments. Safeguarding this important right entails new and significant challenges as technological advances expand the frontiers of areas such as surveillance, communication interception and data storage. This course is designed to familiarise students not specialised in data protection with this emerging area of the law. In particular, the student will be placed in a position to distinguish between positive and negative aspects, advantages and disadvantages associated with data protection regulation and the possible options between alternative forms of protection ready by the legislature. The teaching tends to let the student's ability to understand and explain key case law, summarising major rulings of both the Court of Justice of the European Union and the European Court of Human Rights.
4. Communication skills
The course must put the student in a position to transfer and apply the knowledge gained outside, using an exposure mode logical argument conforms to the main principles of matter and also suits a technically appropriate legal language. In particular, he must be able to provide opinions, advice and assistance on issues object of study.
5. Learning skills
Exceeding the matter must be based on a rigorous assessment in the examination of the achievement by the student of a level of preparation and competence that allows him to approach the study of the other teachings, also not legal, but which may present significant implications with them.
|
6
|
IUS/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9796145 -
POLITICAL SCIENCE RESEARCH DESIGN AND METHODS
(obiettivi)
The goal of this course is to familiarize the students
with research in political science with quantitative
data. They will learn how to formulate (scientifically)
questions about the political world finding answers
using the logic of the scientific method.
On completion, students will learn how to measure political
concepts, how to define a research design and methods of data
collection; how to use statistical and graphical techniques for
describing data; and the principles of statistical inference
|
6
|
SPS/04
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9796144 -
SURVEY DESIGN AND QUESTIONNAIRE DATA ANALYSIS
(obiettivi)
The course will give the main concepts and techniques
for the design of questionnaires and data analysis of
collected data. On completion, students will acquire
knowledge about: i) design of a statistical survey; ii)
techniques for questionnaire design; iii) methods for
statistical analysis of collected data and for providing
statistical reports.
|
6
|
SECS-S/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9795551 -
PREFERENCE MODELING AND CHOICE THEORY
|
6
|
SECS-S/06
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ITA |
|
|
9795513 -
Final dissertation
(obiettivi)
.........................
|
|
|
-
Research for and writing of the final dissertation
|
10
|
|
-
|
-
|
-
|
250
|
-
|
Per la prova finale e la lingua straniera (art.10, comma 5, lettera c)
|
ITA |
|
-
Presentation and discussion of the final dissertation
|
2
|
|
-
|
-
|
-
|
50
|
-
|
Per la prova finale e la lingua straniera (art.10, comma 5, lettera c)
|
ITA |
Business and economics data scientist
Primo anno
Primo semestre
|
Insegnamento
|
CFU
|
SSD
|
Ore Lezione
|
Ore Eserc.
|
Ore Lab
|
Ore Altro
|
Ore Studio
|
Attività
|
Lingua
|
|
Gruppo opzionale:
Supplementary course among "Fundamental of economics" and "Basics of computing" - (visualizza)
|
9
|
|
|
|
|
|
|
|
|
9793959 -
FUNDAMENTALS OF ECONOMICS
(obiettivi)
1.The objectives of the module aim at acquiring knowledge about: i) microfoundations of the economic behavior of individuals and firms and the understanding of markets; ii) economy-wide and large-scale phenomena and economic factors.
2.On completion, the student will be able: i) to interpret and address real-world microeconomic and macroeconomic problems; ii) to understand the open questions and issues on real-world economic problems.
3. On completion, students will able to understand the complexity and the trade-offs related to different market institutions and alternative economic policy measures.
4.On completion, students will be able how to present the results from the economic analyses, and which conclusions can be drawn from the analyses.
5.On completion, students will be able to address new questions and more sophisticated frameworks in economics.
|
9
|
SECS-P/01
|
60
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793954 -
BASICS OF COMPUTING
(obiettivi)
nowledge and understanding: the primary objective of the course is the students' acquisition of the "philosophy" of structured programming, as well as the detailed knowledge of syntax and semantics of the Python programming language. The course pays particular attention to the development of well-written and well-structured code using the basic techniques for software development in the imperative paradigm.
Applying knowledge and understanding: it intends to provide the tools to achieve the following practical and professional skills:
- To analyze computational problems and to code algorithmic ideas for their resolution;
- To design, to describe, to implement and debug Python programs with professional tools;
- To use built-in data structures for data management in scientific computing;
- Understanding simple recursive algorithms;
- To use specific libraries for scientific calculation;
- To read, to understand and analyze third-party Python code also in terms of efficiency;
- To be able to read documentation of libraries.
Making judgements: through the examination of code examples and numerous practice exercises, the learner will be able, both independently and in a cooperative manner, to analyze problems and design and implement related software solutions.
Communication skills: the student will acquire the necessary communication skills and expressive appropriateness in the use of technical verbal language in the context of computer programming.
Learning skills: the course aims to provide the learner with the necessary theoretical and practical methodologies to be used in professional contexts and, in particular, the ability to formulate and implement ad-hoc algorithms for solving new computational problems as well as the possibility of easily and quickly acquiring other programming languages.
|
9
|
INF/01
|
60
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
|
9793926 -
DATA BASE AND BIG DATA ANALYTICS
(obiettivi)
The course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases.
The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets.
|
|
|
-
DATA BASE
(obiettivi)
The course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases.
The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets.
|
6
|
ING-INF/05
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
-
BIG DATA ANALYTICS
|
Erogato anche in altro semestre o anno
|
|
9793875 -
DATA ANALYSIS AND STATISTICAL LEARNING
(obiettivi)
1. Knowledge and understanding (Conoscenza e capacità di comprensione). The first “Statistical Learning” module mainly concerns the fundamentals of two of the main methods used in unsupervised learning: principal component analysis and cluster analysis.
2. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able: i) to implement the main methods used in unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly.
3. Making judgements (Autonomia di giudizio). On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software.
4. Communication skills (Abilità comunicative). On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn.
5. Learning skills (Capacità di apprendimento). On completion, the student will be able to understand the structure of unsupervised learning.
|
|
|
-
DATA ANALYSIS
(obiettivi)
DATA ANALYSIS
1. Knowledge and understanding (Conoscenza e capacità di comprensione). The first “Statistical Learning” module mainly concerns the fundamentals of two of the main methods used in unsupervised learning: principal component analysis and cluster analysis.
2. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able: i) to implement the main methods used in unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly.
3. Making judgements (Autonomia di giudizio). On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software.
4. Communication skills (Abilità comunicative). On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn.
5. Learning skills (Capacità di apprendimento). On completion, the student will be able to understand the structure of unsupervised learning.
STATISTICAL LEARNING
Knowledge and understanding (Conoscenza e capacità di comprensione). The objectives of the module aim at acquiring knowledge about: i) setting of the learning problem and introducing the general model of the risk functional from empirical data; ii) main statistical learning techniques for regression and data classification.
Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, The student will be able: i) to implement main statistical models for supervised and unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly.
Making judgements (Autonomia di giudizio). On completion, students will able how to choose a suitable statistical model, apply sound statistical methods, and perform the analyses using statistical software
Communication skills (Abilità comunicative). On completion, students will be able how to present the results from the statistical analyses, and which conclusions can be drawn from the analyses.
Learning skills (Capacità di apprendimento). On completion, students will be able to understand the structure of the statistical learning.
|
6
|
SECS-S/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
-
STATISTICAL LEARNING
|
Erogato anche in altro semestre o anno
|
|
9793879 -
OPTIMIZATION
(obiettivi)
This graduated-level course introduces analytic tools and optimization methods that are suitable for large-scale problems arising in data science applications. The course presents both basic and advanced concepts of optimization and explores several algorithms that are efficient for network problems.
The student will acquire the ability to formulate, in mathematical terms, problems related to profit maximization and cost minimization, optimization of resources, and traffic network equilibria.
The goals of the course are:
Knowledge and understanding: the aim of the course is to acquire advanced knowledge that allows students to study optimization problems and model techniques of large-scale decision-making problems. The students will be able to use algorithms for both linear and nonlinear programming problems.
Applying knowledge and understanding: students will acquire knowledge useful to identify and model real-life decision-making problems. In addition, through real examples, the student will be able to implement correct solutions for complex problems.
Making judgments: students will be able to choose and solve autonomously complex decision-making problems and to interpret the solutions.
Communication skills: students will acquire base communication and reading skills using technical language.
Learning skills: the course provides students with theoretical and practical methodologies and skills to deal with large-scale optimization problems.
|
6
|
MAT/09
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
9793940 -
STATISTICAL LABORATORY
(obiettivi)
1.The objectives aim at introducing the knowledge of the R language for statistical data analysis with special focus on descriptive statistics, probability distributions and statistical inference.
2.On completion. Students will be able to utilize the R language for: i) providing basic statistical analyses of data; ii) simulating data according to given probability distributions; iii) applying main methods of statistical inference.
3.On completion, students will able to extract knowledge from data through statistical analyses in R.
4.On completion, students will be able how to present the results from the statistical analyses, based on the use of the statistical software R.
5.On completion, students will able how to utilize the statistical software R for basic data analysis and modeling.
|
3
|
|
-
|
-
|
36
|
-
|
-
|
Ulteriori attività formative (art.10, comma 5, lettera d)
|
ENG |
Secondo semestre
|
Insegnamento
|
CFU
|
SSD
|
Ore Lezione
|
Ore Eserc.
|
Ore Lab
|
Ore Altro
|
Ore Studio
|
Attività
|
Lingua
|
|
9793926 -
DATA BASE AND BIG DATA ANALYTICS
(obiettivi)
The course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases.
The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets.
|
|
|
-
DATA BASE
|
Erogato anche in altro semestre o anno
|
|
-
BIG DATA ANALYTICS
(obiettivi)
he course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases.
The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets.
|
6
|
ING-INF/05
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
9793875 -
DATA ANALYSIS AND STATISTICAL LEARNING
(obiettivi)
1. Knowledge and understanding (Conoscenza e capacità di comprensione). The first “Statistical Learning” module mainly concerns the fundamentals of two of the main methods used in unsupervised learning: principal component analysis and cluster analysis.
2. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able: i) to implement the main methods used in unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly.
3. Making judgements (Autonomia di giudizio). On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software.
4. Communication skills (Abilità comunicative). On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn.
5. Learning skills (Capacità di apprendimento). On completion, the student will be able to understand the structure of unsupervised learning.
|
|
|
-
DATA ANALYSIS
|
Erogato anche in altro semestre o anno
|
|
-
STATISTICAL LEARNING
(obiettivi)
DATA ANALYSIS
1. Knowledge and understanding (Conoscenza e capacità di comprensione). The first “Statistical Learning” module mainly concerns the fundamentals of two of the main methods used in unsupervised learning: principal component analysis and cluster analysis.
2. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able: i) to implement the main methods used in unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly.
3. Making judgements (Autonomia di giudizio). On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software.
4. Communication skills (Abilità comunicative). On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn.
5. Learning skills (Capacità di apprendimento). On completion, the student will be able to understand the structure of unsupervised learning.
STATISTICAL LEARNING
Knowledge and understanding (Conoscenza e capacità di comprensione). The objectives of the module aim at acquiring knowledge about: i) setting of the learning problem and introducing the general model of the risk functional from empirical data; ii) main statistical learning techniques for regression and data classification.
Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, The student will be able: i) to implement main statistical models for supervised and unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly.
Making judgements (Autonomia di giudizio). On completion, students will able how to choose a suitable statistical model, apply sound statistical methods, and perform the analyses using statistical software
Communication skills (Abilità comunicative). On completion, students will be able how to present the results from the statistical analyses, and which conclusions can be drawn from the analyses.
Learning skills (Capacità di apprendimento). On completion, students will be able to understand the structure of the statistical learning.
|
6
|
SECS-S/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
9793876 -
DIGITAL INNOVATION AND TRANSFORMATION MANAGEMENT
(obiettivi)
Digital Innovation and Transformation Management provides a comprehensive suite of strategy concepts, tools, methods and perspectives to understand and manage your way through a digital transformation and to develop a strategic response to the emerging digital revolution and to then align your organization for effective strategy execution.
Digital Innovation and Transformation Management course is designed to lead and execute digital innovation initiatives and develop new business models for existing and nascent companies in a wide range of industries.
Future-proof your organization by leveraging human-centred innovation and the power of digital technologies, to tap into customer insights and to deliver sustained competitive advantage by developing new products and services, entrepreneurial initiatives, innovative start-ups, consulting in strategic, marketing and R&D management.
This course serves as the capstone course for the Data science for management program. As a capstone, the goal is to integrate technological and managerial perspectives.
The course provides a comprehensive suite of strategy concepts, tools, methods and perspectives to understand and manage your way through a digital transformation and to develop a strategic response to the emerging digital revolution and to then align your organization for effective strategy execution. The course is designed to lead and execute digital innovation initiatives and develop new business models for existing and nascent companies in entrepreneurial ecosystem.
Students will be able to learn the key theoretical and conceptual categories (knowledge and understanding) that show an entrepreneurial approach to digital innovation and transformation management.
Students will be able to apply professional scheme, models and tools learned using cases studies, also making judgements and contributing to lecture interactions during presentations and discussions in class (communication skills).
Students will be able to learn how to learn, since digital innovation generates changes and evolves over time.
|
9
|
SECS-P/08
|
60
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
9793878 -
BEHAVIORAL ECONOMICS AND COMPLEXITY
(obiettivi)
1.The course aims to build consciousness on the roots of complexity in human behaviors, with reference to their consequences on dynamic perspectives of economic relations. Main contributions of related literature will provide the methodological approach to understand socio-economic relations, by comparing theoretical models and empirical data.
2.The course will analyze both the micro- and the macro-economic perspective, by underlining, respectively, the behavioral approach in the individual choice paradigm and the emergent dynamics in collective phenomena. An essential introduction to agent-based modelling will be given, as one of the most adequate tools of analysis in the field.
3.The course will provide students with adequate abilities to distinguish complex phenomena and to reconcile correct modeling structures with socio-economic problems at hands.
4.The course has an experimental nature. It deals with borderline topics and non-standard approaches for economic analysis. Therefore, a specific effort will be done to help students learning the appropriate terminology and the ability to discuss actual aspects of studied concepts.
5.The course will be a starting point more than a consolidated set of results. All teaching materials, references and presented topics will create a toolbox for many possible future developments, for both further studies and professional applications.
|
9
|
SECS-P/02
|
60
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
Secondo anno
Primo semestre
|
Insegnamento
|
CFU
|
SSD
|
Ore Lezione
|
Ore Eserc.
|
Ore Lab
|
Ore Altro
|
Ore Studio
|
Attività
|
Lingua
|
|
9793877 -
ADVANCED MACHINE LEARNING AND KNOWLEDGE DISCOVERY
(obiettivi)
ADVANCED MACHINE LEARNING
The module will focus on the implementations of various machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries.
KNOWLEDGE DISCOVERY
This module covers the fundamental concepts of deep learning methods and how to use them for extracting, modelling and visualizing the learned knowledge.
Topics include: neural networks with backpropagation, convolutional neural networks, recurrent neural networks, methods for representation learning, and how to use them under different learning regimes (supervised, unsupervised and reinforcement learning) and in variety of real-world applications ranging from computer vision, machine translation and medical image analysis.
The learning objectives are:
to understand and use the main methodologies and techniques for learning from data
to understand the main methodologies to design and implement neural networks for real-world applications
to understand how to extract and learn knowledge in scenarios when supervision cannot be provided
to understand and foresee the reliability of machine learning methods in operational scenarios.
Knowledge and understanding
To understand the main concepts of learning from data
To understand concepts and tools for building intelligent systems using supervision and no supervision
To understand the most important machine learning and artificial intelligence methodologies and techniques used by industries to make sense of data in order to support the decision process
To understand what are the most appropriate techniques to be used in different real-world applications
Applying knowledge and understanding
To be able to effectively understand and use the main tools for creating, loading and manipulating datasets.
To design and implement from scratch a machine learning system following application-derived constraints in terms of modelling and data
To understand proper benchmarks and baselines and analysing achieved results and their generalization in real-world applications
To be able to apply methodologies and techniques to analyse data.
|
|
|
-
ADVANCED MACHINE LEARNING
(obiettivi)
ADVANCED MACHINE LEARNING
The module will focus on the implementations of various machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries.
KNOWLEDGE DISCOVERY
This module covers the fundamental concepts of deep learning methods and how to use them for extracting, modelling and visualizing the learned knowledge.
Topics include: neural networks with backpropagation, convolutional neural networks, recurrent neural networks, methods for representation learning, and how to use them under different learning regimes (supervised, unsupervised and reinforcement learning) and in variety of real-world applications ranging from computer vision, machine translation and medical image analysis.
The learning objectives are:
to understand and use the main methodologies and techniques for learning from data
to understand the main methodologies to design and implement neural networks for real-world applications
to understand how to extract and learn knowledge in scenarios when supervision cannot be provided
to understand and foresee the reliability of machine learning methods in operational scenarios.
Knowledge and understanding
To understand the main concepts of learning from data
To understand concepts and tools for building intelligent systems using supervision and no supervision
To understand the most important machine learning and artificial intelligence methodologies and techniques used by industries to make sense of data in order to support the decision process
To understand what are the most appropriate techniques to be used in different real-world applications
Applying knowledge and understanding
To be able to effectively understand and use the main tools for creating, loading and manipulating datasets.
To design and implement from scratch a machine learning system following application-derived constraints in terms of modelling and data
To understand proper benchmarks and baselines and analysing achieved results and their generalization in real-world applications
To be able to apply methodologies and techniques to analyse data.
|
6
|
INF/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
-
KNOWLEDGE DISCOVERY
|
Erogato anche in altro semestre o anno
|
|
9793884 -
HIGH TECH MARKETS, INDUSTRIAL ORGANIZATION AND GROWTH
(obiettivi)
Knowledge and understanding (Conoscenza e capacità di comprensione). The unit aims to provide knowledge of the main economic aspects related to Information & Communication Technologies (ICT) along with both their link to some Industrial Organisation (IO) issues and their implications for economic growth.
Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). The unit aims to develop skills in applying information, using appropriate methods, concepts and theories regarding the ITC, IO, and growth to the analysis of real-world cases.
Making judgments (Autonomia di giudizio). Successful students will be able to select a proper economic model in order to analyse both theoretical and real-world cases.
Communication skills (Abilità comunicative). Successful students will be familiar with both the terms and the narrative related to High Technology, Industrial Organisation and Growth. Furthermore, they will be able to communicate to a variety of audiences including experts, practitioners, and the general public.
Learning skills (Capacità di apprendimento). Successful students will be able to understand which theoretical concept is appropriate to deal with specific problems in the field of high technology, industrial organisation, and its link to growth.
|
6
|
SECS-P/06
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
9793885 -
ACCOUNTING INFORMATION SYSTEMS
(obiettivi)
The course aims at providing the methodology
necessary to manage company information flows. In
particular, the relationships between internal control
organization and information systems will be
explored. In this sense, the fundamental knowledge
useful for the classification and qualification of
company information will be provided, analyzing the
different aspects that characterize the information
needs both at operational levels and at different levels
of managerial responsibility. Possibilities of
supporting problem solving related to business
decisions and the perspective of decision-making
automation will be illustrated. Interactions among
accounting system, internal control, managerial
control and corporate communication will therefore be
explored
|
6
|
SECS-P/07
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
Gruppo opzionale:
SUPPLEMENTARY COURSES - (visualizza)
|
12
|
|
|
|
|
|
|
|
|
9793955 -
CLOUD COMPUTING AND BIG DATA
(obiettivi)
Knowledge and understanding (Conoscenza e capacità di comprensione). Students will acquire a precise knowledge and understanding of fundamental concepts in the field of cloud computing, chiefly through a guided exploration of the main technological solutions available from the public Cloud, focusing on resources and services oriented to data storage, analysis, visualization and machine learning.
Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). Based on the operating knowledge acquired, students will develop an effective "toolset" of practical, application-oriented skills in leveraging the Cloud to cater for the typical needs of a data scientist: i.e. processing large datasets with a view to revealing meaningful patterns and relationships. Cloud implementations of state-of-the-art tools and frameworks like, e.g., MapReduce/Hadoop or TensorFlow, will be employed
Making judgements (Autonomia di giudizio). The student will develop the ability to choose the suitable Cloud-based resource for the Data Science scenario of interest, properly estimating the ensuing costs and performance gains, as well as consciously assessing the tradeoffs involved.
Communication skills (Abilità comunicative). The student will acquire the communication skills required to express and discuss, at a rigorous technical level, the benefits and (mostly cost-related) downsides of the Cloud for Data Science applications. In addition, the student will gain the ability, for presentation purposes, to effectively highlight the features of very large datasets by means of cloud-based visualization services.
Learning skills (Capacità di apprendimento). Students will become capable of profitably consulting technical documentation concerning Data Science-oriented Cloud services, in order to concretely put them to effective use
|
6
|
INF/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793943 -
BIG DATA SENSING, COMPRESSION AND COMMUNICATION
(obiettivi)
Data is growing and has grown very fast in the last years.”Big Data” analytics is challenging today because of the unprecedented large data volumes. In this course, we will describe the structure of data generated in big data sensing applications, by distinguishing the type and structure of data. Then we will discuss SoA methodologies which can be used to compress this data based on its intrinsic features; finally, communication protocols for remotely delivering this data will be described and detailed. In this way students will be provided with communication engineering competences allowing them to actively communicate with experts in various fields by providing focused and competent data analysis for every application, such as in scientific, technological or business fields. Students will also be able to exploit the competences gained for design processes of collection, compression and communication of heterogeneous big data.
This course will be of interest for students attending all paths for the following reasons:
For “Business and economics data scientists”, this course will allow to complement their business competences being able also to communicate with technical experts in the field.
For students in “Data driven applications for the IoT” and “Data for sciences”, this is a fundamental course to well understand the intrinsic nature of IoT big data.
|
6
|
ING-INF/03
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793930 -
MODELLING AND ANALYSIS FOR COMPLEX SYSTEMS
(obiettivi)
Knowledge and understanding. Students will learn the fundamental concepts of stationary processes and time series, how to estimate the features of a process, the main structures of prediction models, how to identify models starting from time series and how to validate models.
Applying knowledge and understanding. Students will be able to identify linear and non-linear models starting from time series by using popular software tools, such as MATLAB toolboxes, and validate their performances. Case studies will be proposed by using various kinds of dataset.
Making judgements. Students will be able to judge on the potential and limits of the model identification theory proposed in the course.
Communication skills. Students will be able to illustrate the basic aspects of model identification theory, interact and collaborate in teams with other experts.
Learning skills. Students will be able to autonomously extend their knowledge, drawing on the vast literature available in the field of time series model identification.
|
6
|
ING-INF/04
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793931 -
DATA AND METHODS FOR PUBLIC POLICIES EVALUATION
(obiettivi)
This course aims to present the theory and practice of
empirical research in public economics with particular
emphasis on the assessment of public programs in the
market economies. The course will develop analytical
knowledge of the main tools of quantitative
evaluations which underpin public interventions
efficiency and outcomes. Public policy applications
include the main programs in welfare (i.e. health,
education, and social sector).
|
6
|
SECS-P/03
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793932 -
CREDIT RISK MANAGEMENT
(obiettivi)
Knowledge and understanding (Conoscenza e capacità di comprensione). Basic knowledge related to the recognition and management of financial risks from the core of the management processes of financial intermediaries.
Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able to apply the measurement models of the main risks analyzed (credit risk, operational risk, interest rate risk on the banking book, liquidity risk)
Making judgements (Autonomia di giudizio). The ability to research, analyze and process public data and information also collected through scientific researches, with specific reference to the strategies up to the study of the problems concerning the assessment processes of the specific risks that banks incur in the activity of lending.
Communication skills (Abilità comunicative). On completion, the student will be able to communicate the results obtained, the problems encountered and the lessons learned, also based on independent judgment.
Learning skills (Capacità di apprendimento). On completion, the student will be able to individuate the components of credit risk, the organizational aspects of credit risk management and to choose the assessment tools for the diagnosis of credit risk.
|
6
|
SECS-P/11
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793937 -
DATA ANALYSIS FOR PUBLIC HEALTH
(obiettivi)
Il corso fornisce conoscenze specifiche sui metodi e tecniche per affrontare i principali temi di Sanità Pubblica. In particolare, al termine del corso, gli studenti dovranno possedere: i) buona conoscenza degli strumenti e dei metodi necessari alla formulazione di quesiti e obiettivi della propria ricerca; ii) conoscenza approfondita dei metodi statistici e degli strumenti informatici per l'elaborazione del piano di analisi statistica e per l'interpretazione dei risultati; iii) capacità di comunicazione utilizzando il linguaggio proprio della Sanità Pubblica e conoscenza del contesto biomedico. Queste abilità vengono acquisite attraverso lezioni attive e interattive, seminari, laboratori ed esercitazioni e attraverso lo studio del materiale didattico.
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6
|
MED/42
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793970 -
PRIVATE LAW FOR INFORMATION TECHNOLOGY
(obiettivi)
Knowledge and understanding
The course of Private Law for Information Technology aims to analyze the impact of the Internet on legal rules and to identify the regulation of Internet behavior, with particular reference to the relationships between private subjects and to the complexity of the diffusion of artificial intelligence. The course aims also to give the keys to an adequate knowledge and understanding, as well as the normative sources and the relative interpretative principles, of the fundamental institutions of private law strictly connected to the Web in terms of subject regulation and protection of personal data, of the goods and of the circulation of internet rights, of the contract in general and of contracts on line.
2. Applying knowledge and understanding
At the end of the course, the student will be able to understand the legal issues raised by the technological context and to identify the solutions, both by reconstructing and interpreting the different situations and the legally relevant interests in the network on the private level, both by applying in practice the knowledge and tools acquired during the course of lessons and developed with a careful study of the subject.
3. Making judgements
The rapid development of information technology has exacerbated the need for robust personal data protection, the right to which is safeguarded by both European Union (EU) and Council of Europe (CoE) instruments. Safeguarding this important right entails new and significant challenges as technological advances expand the frontiers of areas such as surveillance, communication interception and data storage. This course is designed to familiarise students not specialised in data protection with this emerging area of the law. In particular, the student will be placed in a position to distinguish between positive and negative aspects, advantages and disadvantages associated with data protection regulation and the possible options between alternative forms of protection ready by the legislature. The teaching tends to let the student's ability to understand and explain key case law, summarising major rulings of both the Court of Justice of the European Union and the European Court of Human Rights.
4. Communication skills
The course must put the student in a position to transfer and apply the knowledge gained outside, using an exposure mode logical argument conforms to the main principles of matter and also suits a technically appropriate legal language. In particular, he must be able to provide opinions, advice and assistance on issues object of study.
5. Learning skills
Exceeding the matter must be based on a rigorous assessment in the examination of the achievement by the student of a level of preparation and competence that allows him to approach the study of the other teachings, also not legal, but which may present significant implications with them.
|
6
|
IUS/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9796145 -
POLITICAL SCIENCE RESEARCH DESIGN AND METHODS
(obiettivi)
The goal of this course is to familiarize the students
with research in political science with quantitative
data. They will learn how to formulate (scientifically)
questions about the political world finding answers
using the logic of the scientific method.
On completion, students will learn how to measure political
concepts, how to define a research design and methods of data
collection; how to use statistical and graphical techniques for
describing data; and the principles of statistical inference
|
6
|
SPS/04
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9796144 -
SURVEY DESIGN AND QUESTIONNAIRE DATA ANALYSIS
(obiettivi)
The course will give the main concepts and techniques
for the design of questionnaires and data analysis of
collected data. On completion, students will acquire
knowledge about: i) design of a statistical survey; ii)
techniques for questionnaire design; iii) methods for
statistical analysis of collected data and for providing
statistical reports.
|
6
|
SECS-S/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9795551 -
PREFERENCE MODELING AND CHOICE THEORY
|
6
|
SECS-S/06
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40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ITA |
|
Secondo semestre
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Insegnamento
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CFU
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SSD
|
Ore Lezione
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Ore Eserc.
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Ore Lab
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Ore Altro
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Ore Studio
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Attività
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Lingua
|
|
9793877 -
ADVANCED MACHINE LEARNING AND KNOWLEDGE DISCOVERY
(obiettivi)
ADVANCED MACHINE LEARNING
The module will focus on the implementations of various machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries.
KNOWLEDGE DISCOVERY
This module covers the fundamental concepts of deep learning methods and how to use them for extracting, modelling and visualizing the learned knowledge.
Topics include: neural networks with backpropagation, convolutional neural networks, recurrent neural networks, methods for representation learning, and how to use them under different learning regimes (supervised, unsupervised and reinforcement learning) and in variety of real-world applications ranging from computer vision, machine translation and medical image analysis.
The learning objectives are:
to understand and use the main methodologies and techniques for learning from data
to understand the main methodologies to design and implement neural networks for real-world applications
to understand how to extract and learn knowledge in scenarios when supervision cannot be provided
to understand and foresee the reliability of machine learning methods in operational scenarios.
Knowledge and understanding
To understand the main concepts of learning from data
To understand concepts and tools for building intelligent systems using supervision and no supervision
To understand the most important machine learning and artificial intelligence methodologies and techniques used by industries to make sense of data in order to support the decision process
To understand what are the most appropriate techniques to be used in different real-world applications
Applying knowledge and understanding
To be able to effectively understand and use the main tools for creating, loading and manipulating datasets.
To design and implement from scratch a machine learning system following application-derived constraints in terms of modelling and data
To understand proper benchmarks and baselines and analysing achieved results and their generalization in real-world applications
To be able to apply methodologies and techniques to analyse data.
|
|
|
-
ADVANCED MACHINE LEARNING
|
Erogato anche in altro semestre o anno
|
|
-
KNOWLEDGE DISCOVERY
(obiettivi)
ADVANCED MACHINE LEARNING
The module will focus on the implementations of various machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries.
KNOWLEDGE DISCOVERY
This module covers the fundamental concepts of deep learning methods and how to use them for extracting, modelling and visualizing the learned knowledge.
Topics include: neural networks with backpropagation, convolutional neural networks, recurrent neural networks, methods for representation learning, and how to use them under different learning regimes (supervised, unsupervised and reinforcement learning) and in variety of real-world applications ranging from computer vision, machine translation and medical image analysis.
The learning objectives are:
to understand and use the main methodologies and techniques for learning from data
to understand the main methodologies to design and implement neural networks for real-world applications
to understand how to extract and learn knowledge in scenarios when supervision cannot be provided
to understand and foresee the reliability of machine learning methods in operational scenarios.
Knowledge and understanding
To understand the main concepts of learning from data
To understand concepts and tools for building intelligent systems using supervision and no supervision
To understand the most important machine learning and artificial intelligence methodologies and techniques used by industries to make sense of data in order to support the decision process
To understand what are the most appropriate techniques to be used in different real-world applications
Applying knowledge and understanding
To be able to effectively understand and use the main tools for creating, loading and manipulating datasets.
To design and implement from scratch a machine learning system following application-derived constraints in terms of modelling and data
To understand proper benchmarks and baselines and analysing achieved results and their generalization in real-world applications
To be able to apply methodologies and techniques to analyse data.
|
6
|
ING-INF/05
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
Gruppo opzionale:
SUPPLEMENTARY COURSES - (visualizza)
|
12
|
|
|
|
|
|
|
|
|
9793955 -
CLOUD COMPUTING AND BIG DATA
(obiettivi)
Knowledge and understanding (Conoscenza e capacità di comprensione). Students will acquire a precise knowledge and understanding of fundamental concepts in the field of cloud computing, chiefly through a guided exploration of the main technological solutions available from the public Cloud, focusing on resources and services oriented to data storage, analysis, visualization and machine learning.
Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). Based on the operating knowledge acquired, students will develop an effective "toolset" of practical, application-oriented skills in leveraging the Cloud to cater for the typical needs of a data scientist: i.e. processing large datasets with a view to revealing meaningful patterns and relationships. Cloud implementations of state-of-the-art tools and frameworks like, e.g., MapReduce/Hadoop or TensorFlow, will be employed
Making judgements (Autonomia di giudizio). The student will develop the ability to choose the suitable Cloud-based resource for the Data Science scenario of interest, properly estimating the ensuing costs and performance gains, as well as consciously assessing the tradeoffs involved.
Communication skills (Abilità comunicative). The student will acquire the communication skills required to express and discuss, at a rigorous technical level, the benefits and (mostly cost-related) downsides of the Cloud for Data Science applications. In addition, the student will gain the ability, for presentation purposes, to effectively highlight the features of very large datasets by means of cloud-based visualization services.
Learning skills (Capacità di apprendimento). Students will become capable of profitably consulting technical documentation concerning Data Science-oriented Cloud services, in order to concretely put them to effective use
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6
|
INF/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793943 -
BIG DATA SENSING, COMPRESSION AND COMMUNICATION
(obiettivi)
Data is growing and has grown very fast in the last years.”Big Data” analytics is challenging today because of the unprecedented large data volumes. In this course, we will describe the structure of data generated in big data sensing applications, by distinguishing the type and structure of data. Then we will discuss SoA methodologies which can be used to compress this data based on its intrinsic features; finally, communication protocols for remotely delivering this data will be described and detailed. In this way students will be provided with communication engineering competences allowing them to actively communicate with experts in various fields by providing focused and competent data analysis for every application, such as in scientific, technological or business fields. Students will also be able to exploit the competences gained for design processes of collection, compression and communication of heterogeneous big data.
This course will be of interest for students attending all paths for the following reasons:
For “Business and economics data scientists”, this course will allow to complement their business competences being able also to communicate with technical experts in the field.
For students in “Data driven applications for the IoT” and “Data for sciences”, this is a fundamental course to well understand the intrinsic nature of IoT big data.
|
6
|
ING-INF/03
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793930 -
MODELLING AND ANALYSIS FOR COMPLEX SYSTEMS
(obiettivi)
Knowledge and understanding. Students will learn the fundamental concepts of stationary processes and time series, how to estimate the features of a process, the main structures of prediction models, how to identify models starting from time series and how to validate models.
Applying knowledge and understanding. Students will be able to identify linear and non-linear models starting from time series by using popular software tools, such as MATLAB toolboxes, and validate their performances. Case studies will be proposed by using various kinds of dataset.
Making judgements. Students will be able to judge on the potential and limits of the model identification theory proposed in the course.
Communication skills. Students will be able to illustrate the basic aspects of model identification theory, interact and collaborate in teams with other experts.
Learning skills. Students will be able to autonomously extend their knowledge, drawing on the vast literature available in the field of time series model identification.
|
6
|
ING-INF/04
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793931 -
DATA AND METHODS FOR PUBLIC POLICIES EVALUATION
(obiettivi)
This course aims to present the theory and practice of
empirical research in public economics with particular
emphasis on the assessment of public programs in the
market economies. The course will develop analytical
knowledge of the main tools of quantitative
evaluations which underpin public interventions
efficiency and outcomes. Public policy applications
include the main programs in welfare (i.e. health,
education, and social sector).
|
6
|
SECS-P/03
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793932 -
CREDIT RISK MANAGEMENT
(obiettivi)
Knowledge and understanding (Conoscenza e capacità di comprensione). Basic knowledge related to the recognition and management of financial risks from the core of the management processes of financial intermediaries.
Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able to apply the measurement models of the main risks analyzed (credit risk, operational risk, interest rate risk on the banking book, liquidity risk)
Making judgements (Autonomia di giudizio). The ability to research, analyze and process public data and information also collected through scientific researches, with specific reference to the strategies up to the study of the problems concerning the assessment processes of the specific risks that banks incur in the activity of lending.
Communication skills (Abilità comunicative). On completion, the student will be able to communicate the results obtained, the problems encountered and the lessons learned, also based on independent judgment.
Learning skills (Capacità di apprendimento). On completion, the student will be able to individuate the components of credit risk, the organizational aspects of credit risk management and to choose the assessment tools for the diagnosis of credit risk.
|
6
|
SECS-P/11
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793937 -
DATA ANALYSIS FOR PUBLIC HEALTH
(obiettivi)
Il corso fornisce conoscenze specifiche sui metodi e tecniche per affrontare i principali temi di Sanità Pubblica. In particolare, al termine del corso, gli studenti dovranno possedere: i) buona conoscenza degli strumenti e dei metodi necessari alla formulazione di quesiti e obiettivi della propria ricerca; ii) conoscenza approfondita dei metodi statistici e degli strumenti informatici per l'elaborazione del piano di analisi statistica e per l'interpretazione dei risultati; iii) capacità di comunicazione utilizzando il linguaggio proprio della Sanità Pubblica e conoscenza del contesto biomedico. Queste abilità vengono acquisite attraverso lezioni attive e interattive, seminari, laboratori ed esercitazioni e attraverso lo studio del materiale didattico.
|
6
|
MED/42
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793970 -
PRIVATE LAW FOR INFORMATION TECHNOLOGY
(obiettivi)
Knowledge and understanding
The course of Private Law for Information Technology aims to analyze the impact of the Internet on legal rules and to identify the regulation of Internet behavior, with particular reference to the relationships between private subjects and to the complexity of the diffusion of artificial intelligence. The course aims also to give the keys to an adequate knowledge and understanding, as well as the normative sources and the relative interpretative principles, of the fundamental institutions of private law strictly connected to the Web in terms of subject regulation and protection of personal data, of the goods and of the circulation of internet rights, of the contract in general and of contracts on line.
2. Applying knowledge and understanding
At the end of the course, the student will be able to understand the legal issues raised by the technological context and to identify the solutions, both by reconstructing and interpreting the different situations and the legally relevant interests in the network on the private level, both by applying in practice the knowledge and tools acquired during the course of lessons and developed with a careful study of the subject.
3. Making judgements
The rapid development of information technology has exacerbated the need for robust personal data protection, the right to which is safeguarded by both European Union (EU) and Council of Europe (CoE) instruments. Safeguarding this important right entails new and significant challenges as technological advances expand the frontiers of areas such as surveillance, communication interception and data storage. This course is designed to familiarise students not specialised in data protection with this emerging area of the law. In particular, the student will be placed in a position to distinguish between positive and negative aspects, advantages and disadvantages associated with data protection regulation and the possible options between alternative forms of protection ready by the legislature. The teaching tends to let the student's ability to understand and explain key case law, summarising major rulings of both the Court of Justice of the European Union and the European Court of Human Rights.
4. Communication skills
The course must put the student in a position to transfer and apply the knowledge gained outside, using an exposure mode logical argument conforms to the main principles of matter and also suits a technically appropriate legal language. In particular, he must be able to provide opinions, advice and assistance on issues object of study.
5. Learning skills
Exceeding the matter must be based on a rigorous assessment in the examination of the achievement by the student of a level of preparation and competence that allows him to approach the study of the other teachings, also not legal, but which may present significant implications with them.
|
6
|
IUS/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9796145 -
POLITICAL SCIENCE RESEARCH DESIGN AND METHODS
(obiettivi)
The goal of this course is to familiarize the students
with research in political science with quantitative
data. They will learn how to formulate (scientifically)
questions about the political world finding answers
using the logic of the scientific method.
On completion, students will learn how to measure political
concepts, how to define a research design and methods of data
collection; how to use statistical and graphical techniques for
describing data; and the principles of statistical inference
|
6
|
SPS/04
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9796144 -
SURVEY DESIGN AND QUESTIONNAIRE DATA ANALYSIS
(obiettivi)
The course will give the main concepts and techniques
for the design of questionnaires and data analysis of
collected data. On completion, students will acquire
knowledge about: i) design of a statistical survey; ii)
techniques for questionnaire design; iii) methods for
statistical analysis of collected data and for providing
statistical reports.
|
6
|
SECS-S/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9795551 -
PREFERENCE MODELING AND CHOICE THEORY
|
6
|
SECS-S/06
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ITA |
|
|
9793963 -
ELECTIVE COURSE
(obiettivi)
...................................
|
12
|
|
80
|
-
|
-
|
-
|
-
|
Attività formative a scelta dello studente (art.10, comma 5, lettera a)
|
ENG |
|
9795513 -
Final dissertation
(obiettivi)
.........................
|
|
|
-
Research for and writing of the final dissertation
|
10
|
|
-
|
-
|
-
|
250
|
-
|
Per la prova finale e la lingua straniera (art.10, comma 5, lettera c)
|
ITA |
|
-
Presentation and discussion of the final dissertation
|
2
|
|
-
|
-
|
-
|
50
|
-
|
Per la prova finale e la lingua straniera (art.10, comma 5, lettera c)
|
ITA |
