Corso di laurea: Data science for management
A.A. 2019/2020
Conoscenza e capacità di comprensione
Il CdS è progettato affinché i suoi laureati conseguano conoscenze e capacità 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 attività professionale.
Tenendo conto della specificità del CdLM, gli insegnamenti sono, di norma, integrati con attività di esercitazione pratica e di laboratorio.
Modalità di acquisizione delle conoscenze
L'acquisizione delle conoscenze avviene principalmente attraverso la frequenza delle lezioni tenute dal docente, la frequenza delle attività di laboratorio, la partecipazione a seminari condotti da esperti esterni (in rappresentanza del mondo professionale di riferimento del corso di studio).
Sono inoltre previste attività 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.
Modalità di verifica delle conoscenze
La verifica dell'acquisizione delle conoscenze e della capacità di applicare le conoscenze è effettuata con le seguenti modalità, diversamente combinate secondo le specificità degli argomenti trattati: prove scritte, prove orali, presentazione di elaborati scritti e, per lo tirocinio, valutazione del tutor aziendale e dell'Università.
La prova finale fornisce un'ulteriore opportunità di verifica della comprensione dei temi trattati nel CdLM.
Capacità di applicare conoscenza e comprensione
Il CdS è stato progettato affinché 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 è costantemente verificato attraverso esercitazioni e altre attività d’aula, realizzazione di elaborati scritti e successiva discussione in aula, prove scritte e colloqui orali.
La capacità 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 è stato progettato affinché i laureati possano acquisire la capacità 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 attività 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 è stato progettato affinché 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 abilità comunicative saranno sviluppate principalmente attraverso attività in aula, partecipazione a gruppi di lavoro, presentazione di casi studio ed elaborati con particolare riferimento alla prova finale.
Le attività di tirocinio, inoltre, contribuiscono fortemente allo sviluppo di abilità comunicative.
Le abilità 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 è stato progettato affinché i suoi laureati sviluppino nel proprio percorso formativo le capacità di apprendimento necessarie sia per intraprendere percorsi autonomi di aggiornamento ed ulteriore sviluppo di conoscenze e competenze relative a data science e business analytics, nonché per proseguire con profitto gli studi (Master di II livello o Dottorato).
La capacità di apprendere è sviluppata soprattutto attraverso la partecipazione alle attività 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 è effettuata attraverso la valutazione dei risultati di profitto nella didattica tradizionale, le valutazioni delle relazioni apposite dei tutor previsti per le attività di stage e tirocinio, la valutazione della qualità 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, altresì, 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
Purchè 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 modalità 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, probabilità e statistica ed altresì, 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 capacità di metterlo in relazione al contesto di riferimento, la capacità di operare in modo autonomo, e un'adeguata abilità di comunicazione.
Le modalità 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 opportunità 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 organizza incontri con studenti delle scuole medie superiori, studenti universitari e loro genitori durante i quali, previa distribuzione di materiale informativo, viene illustrato il Corso di Studio, con particolare riguardo al piano di studi e ai saperi minimi necessari per accedere al Corso.
In particolare, si prevede di partecipare a:
Incontri di Area, pensati per far conoscere l'offerta didattica, i docenti, i servizi offerti dall'Università e i luoghi in cui si svolgeranno le attività didattiche.
Open Day: giornata di orientamento dedicata a tutti i corsi di laurea e tutti i servizi dell'Ateneo
Descrizione link: Orientamento nel Dipartimento Economia e Impresa
Link inserito: http://www.dei.unict.it/didattica/orientamentoIl Corso di Studio in breve
Il corso di natura interdisciplinare intende formare laureati magistrali con elevate conoscenze in:
a) raccolta, trattamento, compressione, archiviazione, sicurezza ed analisi di dati, sia strutturati che non strutturati;
b) linguaggi di programmazione più diffusi per l'analisi dati;
c) statistical learning e analisi statistica dei dati;
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 in ogni settore applicativo scientifico, tencologico ed economico-aziendale;
b) progettare iniziative di raccolta, pulizia, archiviazione ed 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 mentalità 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 orientato al miglior utilizzo dei dati da raccogliere e studiare.
Essi avranno piena consapevolezza del prezioso e delicato ruolo della raccolta ed analisi dati nella gestione di aziende, delle organizzazioni complesse, del monitoraggio e pianificazione di servizi pubblici anche nel campo della salute.
Essi saranno correttamente formati al rispetto dei principi etici nella raccolta, custodia ed utilizzo delle informazioni personali.
Particolarmente importante 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 monotoraggio 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
|
Insegnamento
|
CFU
|
SSD
|
Ore Lezione
|
Ore Eserc.
|
Ore Lab
|
Ore Altro
|
Ore Studio
|
Attività
|
Lingua
|
|
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 |
|
9793926 -
DATA BASE AND BIG DATA ANALYTICS
(obiettivi)
1.To understand and use the main technologies for database management;
To use SQL language for performing efficient queries in cases of large datasets;
To understand how to index and query multimedia datasets
To become aware of the existing benchmarks and their liminations for training and comparing data analysis techniques.
Knowledge and understanding
2.To understand the main concepts of management database systems
To understand concepts and tools for generating and querying datasets at different scales
To understand techniques for indexing and searching multimedia datasets
To understand how potential biases in data collection may affect analytics methods
Applying knowledge and understanding
3.To be able to effectively understand and use the main tools for creating and querying SQL and NoSQL datasets.
To query and analysis multimedia at large scale
To understand proper benchmarks and analysing achieved results also in terms of potential biases
Big Data Analytics
This module covers the fundamental concepts of management and design of a business intelligence system. Topics include data models for building a data warehouse; ETL (extract, transform and load) functionalities; OLAP analysis; basic data mining; reporting and interactive dashboards, evolution of BI architectures on large datasets. The module covers techniques and algorithms for data visualization and exploratory analysis based on principles and techniques from graphic design, perceptual psychology and cognitive science. It is targeted to using visualization in their data analytics work.
4.To understand and use the main methodologies and techniques for data analysis
to understand the main methodologies to design a data warehouse
to understand the main methodologies to transform data into sources of knowledge through visual representation
Knowledge and understanding
5.To understand the most important methodologies and techniques used by industries to analyse data in order to support the decision process
To understand the main methodologies to design a data warehouse
To understand the main methodologies to transform data into sources of knowledge through visual representation
Applying knowledge and understanding
To be able to apply methodologies and techniques to analyse data.
To be able to design a data warehouse.
To be able to build report and data analysis and organize them into interactive dashboards
|
|
|
-
DATA BASE
|
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
|
|
|
-
DATA ANALYSIS
|
6
|
SECS-S/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
-
STATISTICAL LEARNING
|
Erogato anche in altro semestre o anno
|
|
9793879 -
OPTIMIZATION
|
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)
1.To understand and use the main technologies for database management;
To use SQL language for performing efficient queries in cases of large datasets;
To understand how to index and query multimedia datasets
To become aware of the existing benchmarks and their liminations for training and comparing data analysis techniques.
Knowledge and understanding
2.To understand the main concepts of management database systems
To understand concepts and tools for generating and querying datasets at different scales
To understand techniques for indexing and searching multimedia datasets
To understand how potential biases in data collection may affect analytics methods
Applying knowledge and understanding
3.To be able to effectively understand and use the main tools for creating and querying SQL and NoSQL datasets.
To query and analysis multimedia at large scale
To understand proper benchmarks and analysing achieved results also in terms of potential biases
Big Data Analytics
This module covers the fundamental concepts of management and design of a business intelligence system. Topics include data models for building a data warehouse; ETL (extract, transform and load) functionalities; OLAP analysis; basic data mining; reporting and interactive dashboards, evolution of BI architectures on large datasets. The module covers techniques and algorithms for data visualization and exploratory analysis based on principles and techniques from graphic design, perceptual psychology and cognitive science. It is targeted to using visualization in their data analytics work.
4.To understand and use the main methodologies and techniques for data analysis
to understand the main methodologies to design a data warehouse
to understand the main methodologies to transform data into sources of knowledge through visual representation
Knowledge and understanding
5.To understand the most important methodologies and techniques used by industries to analyse data in order to support the decision process
To understand the main methodologies to design a data warehouse
To understand the main methodologies to transform data into sources of knowledge through visual representation
Applying knowledge and understanding
To be able to apply methodologies and techniques to analyse data.
To be able to design a data warehouse.
To be able to build report and data analysis and organize them into interactive dashboards
|
|
|
-
DATA BASE
|
Erogato anche in altro semestre o anno
|
|
-
BIG DATA ANALYTICS
|
6
|
ING-INF/05
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
9793875 -
DATA ANALYSIS AND STATISTICAL LEARNING
|
|
|
-
DATA ANALYSIS
|
Erogato anche in altro semestre o anno
|
|
-
STATISTICAL LEARNING
(obiettivi)
1.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.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.On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software.
4.On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn.
5.On completion, the student will be able to understand the structure of unsupervised 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
|
|
|
-
ADVANCED MACHINE LEARNING
|
6
|
INF/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
-
KNOWLEDGE DISCOVERY
|
Erogato anche in altro semestre o anno
|
|
9793880 -
COMPUTER SECURITY AND DATA PROTECTION
|
6
|
INF/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
9793881 -
NEURAL COMPUTING
|
6
|
INF/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
Gruppo opzionale:
SUPPLEMENTARY COURSES - (visualizza)
|
12
|
|
|
|
|
|
|
|
|
9793955 -
CLOUD COMPUTING AND BIG DATA
|
Erogato anche in altro semestre o anno
|
|
9793943 -
BIG DATA SENSING, COMPRESSION AND COMMUNICATION
|
Erogato anche in altro semestre o anno
|
|
9793930 -
MODELLING AND ANALYSIS FOR COMPLEX SYSTEMS
|
Erogato anche in altro semestre o anno
|
|
9793931 -
DATA AND METHODS FOR PUBLIC POLICIES EVALUATION
|
Erogato anche in altro semestre o anno
|
|
9793932 -
CREDIT RISK MANAGEMENT
|
Erogato anche in altro semestre o anno
|
|
9793934 -
ANALYSIS OF QUESTIONNAIRE DATA
|
6
|
SECS-S/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793937 -
DATA ANALYSIS FOR PUBLIC HEALTH
|
Erogato anche in altro semestre o anno
|
|
9793936 -
DECISION SCIENCES
|
Erogato anche in altro semestre o anno
|
|
9793939 -
METHODOLOGY OF POLITICAL SCIENCE
|
6
|
SPS/04
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793970 -
PRIVATE LAW FOR INFORMATION TECHNOLOGY
|
Erogato anche in altro semestre o anno
|
|
Secondo semestre
|
Insegnamento
|
CFU
|
SSD
|
Ore Lezione
|
Ore Eserc.
|
Ore Lab
|
Ore Altro
|
Ore Studio
|
Attività
|
Lingua
|
|
9793877 -
ADVANCED MACHINE LEARNING AND KNOWLEDGE DISCOVERY
|
|
|
-
ADVANCED MACHINE LEARNING
|
Erogato anche in altro semestre o anno
|
|
-
KNOWLEDGE DISCOVERY
|
6
|
ING-INF/05
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
Gruppo opzionale:
SUPPLEMENTARY COURSES - (visualizza)
|
12
|
|
|
|
|
|
|
|
|
9793955 -
CLOUD COMPUTING AND BIG DATA
|
6
|
INF/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793943 -
BIG DATA SENSING, COMPRESSION AND COMMUNICATION
|
6
|
ING-INF/03
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793930 -
MODELLING AND ANALYSIS FOR COMPLEX SYSTEMS
|
6
|
ING-INF/04
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793931 -
DATA AND METHODS FOR PUBLIC POLICIES EVALUATION
|
6
|
SECS-P/03
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793932 -
CREDIT RISK MANAGEMENT
|
6
|
SECS-P/11
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793934 -
ANALYSIS OF QUESTIONNAIRE DATA
|
Erogato anche in altro semestre o anno
|
|
9793937 -
DATA ANALYSIS FOR PUBLIC HEALTH
|
6
|
MED/42
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793936 -
DECISION SCIENCES
|
6
|
SECS-S/06
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793939 -
METHODOLOGY OF POLITICAL SCIENCE
|
Erogato anche in altro semestre o anno
|
|
9793970 -
PRIVATE LAW FOR INFORMATION TECHNOLOGY
|
6
|
IUS/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
|
9793962 -
THESIS
|
12
|
|
-
|
-
|
-
|
300
|
-
|
Per la prova finale e la lingua straniera (art.10, comma 5, lettera c)
|
ENG |
|
9793963 -
ELECTIVE COURSE
|
12
|
|
80
|
-
|
-
|
-
|
-
|
Attività formative a scelta dello studente (art.10, comma 5, lettera a)
|
ENG |
Data driven applications for IoT
Primo anno
Primo semestre
|
Insegnamento
|
CFU
|
SSD
|
Ore Lezione
|
Ore Eserc.
|
Ore Lab
|
Ore Altro
|
Ore Studio
|
Attività
|
Lingua
|
|
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 |
|
9793926 -
DATA BASE AND BIG DATA ANALYTICS
(obiettivi)
1.To understand and use the main technologies for database management;
To use SQL language for performing efficient queries in cases of large datasets;
To understand how to index and query multimedia datasets
To become aware of the existing benchmarks and their liminations for training and comparing data analysis techniques.
Knowledge and understanding
2.To understand the main concepts of management database systems
To understand concepts and tools for generating and querying datasets at different scales
To understand techniques for indexing and searching multimedia datasets
To understand how potential biases in data collection may affect analytics methods
Applying knowledge and understanding
3.To be able to effectively understand and use the main tools for creating and querying SQL and NoSQL datasets.
To query and analysis multimedia at large scale
To understand proper benchmarks and analysing achieved results also in terms of potential biases
Big Data Analytics
This module covers the fundamental concepts of management and design of a business intelligence system. Topics include data models for building a data warehouse; ETL (extract, transform and load) functionalities; OLAP analysis; basic data mining; reporting and interactive dashboards, evolution of BI architectures on large datasets. The module covers techniques and algorithms for data visualization and exploratory analysis based on principles and techniques from graphic design, perceptual psychology and cognitive science. It is targeted to using visualization in their data analytics work.
4.To understand and use the main methodologies and techniques for data analysis
to understand the main methodologies to design a data warehouse
to understand the main methodologies to transform data into sources of knowledge through visual representation
Knowledge and understanding
5.To understand the most important methodologies and techniques used by industries to analyse data in order to support the decision process
To understand the main methodologies to design a data warehouse
To understand the main methodologies to transform data into sources of knowledge through visual representation
Applying knowledge and understanding
To be able to apply methodologies and techniques to analyse data.
To be able to design a data warehouse.
To be able to build report and data analysis and organize them into interactive dashboards
|
|
|
-
DATA BASE
|
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
|
|
|
-
DATA ANALYSIS
|
6
|
SECS-S/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
-
STATISTICAL LEARNING
|
Erogato anche in altro semestre o anno
|
|
9793879 -
OPTIMIZATION
|
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)
1.To understand and use the main technologies for database management;
To use SQL language for performing efficient queries in cases of large datasets;
To understand how to index and query multimedia datasets
To become aware of the existing benchmarks and their liminations for training and comparing data analysis techniques.
Knowledge and understanding
2.To understand the main concepts of management database systems
To understand concepts and tools for generating and querying datasets at different scales
To understand techniques for indexing and searching multimedia datasets
To understand how potential biases in data collection may affect analytics methods
Applying knowledge and understanding
3.To be able to effectively understand and use the main tools for creating and querying SQL and NoSQL datasets.
To query and analysis multimedia at large scale
To understand proper benchmarks and analysing achieved results also in terms of potential biases
Big Data Analytics
This module covers the fundamental concepts of management and design of a business intelligence system. Topics include data models for building a data warehouse; ETL (extract, transform and load) functionalities; OLAP analysis; basic data mining; reporting and interactive dashboards, evolution of BI architectures on large datasets. The module covers techniques and algorithms for data visualization and exploratory analysis based on principles and techniques from graphic design, perceptual psychology and cognitive science. It is targeted to using visualization in their data analytics work.
4.To understand and use the main methodologies and techniques for data analysis
to understand the main methodologies to design a data warehouse
to understand the main methodologies to transform data into sources of knowledge through visual representation
Knowledge and understanding
5.To understand the most important methodologies and techniques used by industries to analyse data in order to support the decision process
To understand the main methodologies to design a data warehouse
To understand the main methodologies to transform data into sources of knowledge through visual representation
Applying knowledge and understanding
To be able to apply methodologies and techniques to analyse data.
To be able to design a data warehouse.
To be able to build report and data analysis and organize them into interactive dashboards
|
|
|
-
DATA BASE
|
Erogato anche in altro semestre o anno
|
|
-
BIG DATA ANALYTICS
|
6
|
ING-INF/05
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
9793875 -
DATA ANALYSIS AND STATISTICAL LEARNING
|
|
|
-
DATA ANALYSIS
|
Erogato anche in altro semestre o anno
|
|
-
STATISTICAL LEARNING
(obiettivi)
1.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.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.On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software.
4.On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn.
5.On completion, the student will be able to understand the structure of unsupervised 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
|
|
|
-
ADVANCED MACHINE LEARNING
|
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
|
6
|
ING-INF/05
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
9793928 -
BIG DATA FOR SMART MANUFACTURING
|
6
|
ING-INF/05
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
Gruppo opzionale:
SUPPLEMENTARY COURSES - (visualizza)
|
12
|
|
|
|
|
|
|
|
|
9793955 -
CLOUD COMPUTING AND BIG DATA
|
Erogato anche in altro semestre o anno
|
|
9793943 -
BIG DATA SENSING, COMPRESSION AND COMMUNICATION
|
Erogato anche in altro semestre o anno
|
|
9793930 -
MODELLING AND ANALYSIS FOR COMPLEX SYSTEMS
|
Erogato anche in altro semestre o anno
|
|
9793931 -
DATA AND METHODS FOR PUBLIC POLICIES EVALUATION
|
Erogato anche in altro semestre o anno
|
|
9793932 -
CREDIT RISK MANAGEMENT
|
Erogato anche in altro semestre o anno
|
|
9793934 -
ANALYSIS OF QUESTIONNAIRE DATA
|
6
|
SECS-S/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793937 -
DATA ANALYSIS FOR PUBLIC HEALTH
|
Erogato anche in altro semestre o anno
|
|
9793936 -
DECISION SCIENCES
|
Erogato anche in altro semestre o anno
|
|
9793939 -
METHODOLOGY OF POLITICAL SCIENCE
|
Erogato anche in altro semestre o anno
|
|
9793970 -
PRIVATE LAW FOR INFORMATION TECHNOLOGY
|
Erogato anche in altro semestre o anno
|
|
Secondo semestre
|
Insegnamento
|
CFU
|
SSD
|
Ore Lezione
|
Ore Eserc.
|
Ore Lab
|
Ore Altro
|
Ore Studio
|
Attività
|
Lingua
|
|
9793877 -
ADVANCED MACHINE LEARNING AND KNOWLEDGE DISCOVERY
|
|
|
-
ADVANCED MACHINE LEARNING
|
Erogato anche in altro semestre o anno
|
|
-
KNOWLEDGE DISCOVERY
|
6
|
ING-INF/05
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
9793962 -
THESIS
|
12
|
|
-
|
-
|
-
|
300
|
-
|
Per la prova finale e la lingua straniera (art.10, comma 5, lettera c)
|
ENG |
|
9793963 -
ELECTIVE COURSE
|
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
|
6
|
INF/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793943 -
BIG DATA SENSING, COMPRESSION AND COMMUNICATION
|
6
|
ING-INF/03
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793930 -
MODELLING AND ANALYSIS FOR COMPLEX SYSTEMS
|
6
|
ING-INF/04
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793931 -
DATA AND METHODS FOR PUBLIC POLICIES EVALUATION
|
6
|
SECS-P/03
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793932 -
CREDIT RISK MANAGEMENT
|
6
|
SECS-P/11
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793934 -
ANALYSIS OF QUESTIONNAIRE DATA
|
Erogato anche in altro semestre o anno
|
|
9793937 -
DATA ANALYSIS FOR PUBLIC HEALTH
|
6
|
MED/42
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793936 -
DECISION SCIENCES
|
6
|
SECS-S/06
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793939 -
METHODOLOGY OF POLITICAL SCIENCE
|
6
|
SPS/04
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793970 -
PRIVATE LAW FOR INFORMATION TECHNOLOGY
|
6
|
IUS/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
Business and economics data scientist
Primo anno
Primo semestre
|
Insegnamento
|
CFU
|
SSD
|
Ore Lezione
|
Ore Eserc.
|
Ore Lab
|
Ore Altro
|
Ore Studio
|
Attività
|
Lingua
|
|
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)
1.To understand and use the main technologies for database management;
To use SQL language for performing efficient queries in cases of large datasets;
To understand how to index and query multimedia datasets
To become aware of the existing benchmarks and their liminations for training and comparing data analysis techniques.
Knowledge and understanding
2.To understand the main concepts of management database systems
To understand concepts and tools for generating and querying datasets at different scales
To understand techniques for indexing and searching multimedia datasets
To understand how potential biases in data collection may affect analytics methods
Applying knowledge and understanding
3.To be able to effectively understand and use the main tools for creating and querying SQL and NoSQL datasets.
To query and analysis multimedia at large scale
To understand proper benchmarks and analysing achieved results also in terms of potential biases
Big Data Analytics
This module covers the fundamental concepts of management and design of a business intelligence system. Topics include data models for building a data warehouse; ETL (extract, transform and load) functionalities; OLAP analysis; basic data mining; reporting and interactive dashboards, evolution of BI architectures on large datasets. The module covers techniques and algorithms for data visualization and exploratory analysis based on principles and techniques from graphic design, perceptual psychology and cognitive science. It is targeted to using visualization in their data analytics work.
4.To understand and use the main methodologies and techniques for data analysis
to understand the main methodologies to design a data warehouse
to understand the main methodologies to transform data into sources of knowledge through visual representation
Knowledge and understanding
5.To understand the most important methodologies and techniques used by industries to analyse data in order to support the decision process
To understand the main methodologies to design a data warehouse
To understand the main methodologies to transform data into sources of knowledge through visual representation
Applying knowledge and understanding
To be able to apply methodologies and techniques to analyse data.
To be able to design a data warehouse.
To be able to build report and data analysis and organize them into interactive dashboards
|
|
|
-
DATA BASE
|
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
|
|
|
-
DATA ANALYSIS
|
6
|
SECS-S/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
-
STATISTICAL LEARNING
|
Erogato anche in altro semestre o anno
|
|
9793879 -
OPTIMIZATION
|
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)
1.To understand and use the main technologies for database management;
To use SQL language for performing efficient queries in cases of large datasets;
To understand how to index and query multimedia datasets
To become aware of the existing benchmarks and their liminations for training and comparing data analysis techniques.
Knowledge and understanding
2.To understand the main concepts of management database systems
To understand concepts and tools for generating and querying datasets at different scales
To understand techniques for indexing and searching multimedia datasets
To understand how potential biases in data collection may affect analytics methods
Applying knowledge and understanding
3.To be able to effectively understand and use the main tools for creating and querying SQL and NoSQL datasets.
To query and analysis multimedia at large scale
To understand proper benchmarks and analysing achieved results also in terms of potential biases
Big Data Analytics
This module covers the fundamental concepts of management and design of a business intelligence system. Topics include data models for building a data warehouse; ETL (extract, transform and load) functionalities; OLAP analysis; basic data mining; reporting and interactive dashboards, evolution of BI architectures on large datasets. The module covers techniques and algorithms for data visualization and exploratory analysis based on principles and techniques from graphic design, perceptual psychology and cognitive science. It is targeted to using visualization in their data analytics work.
4.To understand and use the main methodologies and techniques for data analysis
to understand the main methodologies to design a data warehouse
to understand the main methodologies to transform data into sources of knowledge through visual representation
Knowledge and understanding
5.To understand the most important methodologies and techniques used by industries to analyse data in order to support the decision process
To understand the main methodologies to design a data warehouse
To understand the main methodologies to transform data into sources of knowledge through visual representation
Applying knowledge and understanding
To be able to apply methodologies and techniques to analyse data.
To be able to design a data warehouse.
To be able to build report and data analysis and organize them into interactive dashboards
|
|
|
-
DATA BASE
|
Erogato anche in altro semestre o anno
|
|
-
BIG DATA ANALYTICS
|
6
|
ING-INF/05
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
9793875 -
DATA ANALYSIS AND STATISTICAL LEARNING
|
|
|
-
DATA ANALYSIS
|
Erogato anche in altro semestre o anno
|
|
-
STATISTICAL LEARNING
(obiettivi)
1.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.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.On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software.
4.On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn.
5.On completion, the student will be able to understand the structure of unsupervised 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
|
|
|
-
ADVANCED MACHINE LEARNING
|
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
|
6
|
SECS-P/06
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
9793885 -
ACCOUNTING INFORMATION SYSTEMS
|
6
|
SECS-P/07
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
Gruppo opzionale:
SUPPLEMENTARY COURSES - (visualizza)
|
12
|
|
|
|
|
|
|
|
|
9793955 -
CLOUD COMPUTING AND BIG DATA
|
Erogato anche in altro semestre o anno
|
|
9793943 -
BIG DATA SENSING, COMPRESSION AND COMMUNICATION
|
Erogato anche in altro semestre o anno
|
|
9793930 -
MODELLING AND ANALYSIS FOR COMPLEX SYSTEMS
|
Erogato anche in altro semestre o anno
|
|
9793931 -
DATA AND METHODS FOR PUBLIC POLICIES EVALUATION
|
Erogato anche in altro semestre o anno
|
|
9793932 -
CREDIT RISK MANAGEMENT
|
Erogato anche in altro semestre o anno
|
|
9793934 -
ANALYSIS OF QUESTIONNAIRE DATA
|
6
|
SECS-S/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793937 -
DATA ANALYSIS FOR PUBLIC HEALTH
|
Erogato anche in altro semestre o anno
|
|
9793936 -
DECISION SCIENCES
|
Erogato anche in altro semestre o anno
|
|
9793939 -
METHODOLOGY OF POLITICAL SCIENCE
|
Erogato anche in altro semestre o anno
|
|
9793970 -
PRIVATE LAW FOR INFORMATION TECHNOLOGY
|
Erogato anche in altro semestre o anno
|
|
Secondo semestre
|
Insegnamento
|
CFU
|
SSD
|
Ore Lezione
|
Ore Eserc.
|
Ore Lab
|
Ore Altro
|
Ore Studio
|
Attività
|
Lingua
|
|
9793877 -
ADVANCED MACHINE LEARNING AND KNOWLEDGE DISCOVERY
|
|
|
-
ADVANCED MACHINE LEARNING
|
Erogato anche in altro semestre o anno
|
|
-
KNOWLEDGE DISCOVERY
|
6
|
ING-INF/05
|
40
|
-
|
-
|
-
|
-
|
Attività formative caratterizzanti
|
ENG |
|
Gruppo opzionale:
SUPPLEMENTARY COURSES - (visualizza)
|
12
|
|
|
|
|
|
|
|
|
9793955 -
CLOUD COMPUTING AND BIG DATA
|
6
|
INF/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793943 -
BIG DATA SENSING, COMPRESSION AND COMMUNICATION
|
6
|
ING-INF/03
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793930 -
MODELLING AND ANALYSIS FOR COMPLEX SYSTEMS
|
6
|
ING-INF/04
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793931 -
DATA AND METHODS FOR PUBLIC POLICIES EVALUATION
|
6
|
SECS-P/03
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793932 -
CREDIT RISK MANAGEMENT
|
6
|
SECS-P/11
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793934 -
ANALYSIS OF QUESTIONNAIRE DATA
|
Erogato anche in altro semestre o anno
|
|
9793937 -
DATA ANALYSIS FOR PUBLIC HEALTH
|
6
|
MED/42
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793936 -
DECISION SCIENCES
|
6
|
SECS-S/06
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793939 -
METHODOLOGY OF POLITICAL SCIENCE
|
6
|
SPS/04
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
9793970 -
PRIVATE LAW FOR INFORMATION TECHNOLOGY
|
6
|
IUS/01
|
40
|
-
|
-
|
-
|
-
|
Attività formative affini ed integrative
|
ENG |
|
|
9793962 -
THESIS
|
12
|
|
-
|
-
|
-
|
300
|
-
|
Per la prova finale e la lingua straniera (art.10, comma 5, lettera c)
|
ENG |
|
9793963 -
ELECTIVE COURSE
|
12
|
|
80
|
-
|
-
|
-
|
-
|
Attività formative a scelta dello studente (art.10, comma 5, lettera a)
|
ENG |
