Corso di laurea: Data science for management Programmazione per l'A.A. 2020/2021

Data for sciences

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)

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. - CASERTA Maurizio Giuseppe Giacomo (programma) This course teaches the most important basic concepts in microeconomics. Students will be introduced to a range of economic tools from microeconomics used to study models explicitly involving consumer and firm behavior in monopoly, oligopoly, perfect competition and the role of prices and institutions. For each topic case studies and real-world examples will be analyzed. Hal R. Varian, INTERMEDIATE MICROECONOMICS, Ninth Edition, W. W. Norton & Company, 2020	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. - ORTIS ALESSANDRO	9	INF/01	60	-	-	-	-	Attività formative affini ed integrative	ENG

9793926 - DATA BASE AND BIG DATA ANALYTICS (obiettivi)

- DATA BASE (obiettivi)

- Erogato presso 9793926_1 DATA BASE in Data science for management LM-91 PALAZZO SIMONE

ING-INF/05

Attività formative caratterizzanti

ENG

- BIG DATA ANALYTICS

Erogato anche in altro semestre o anno

9793875 - DATA ANALYSIS AND STATISTICAL LEARNING (obiettivi)

- 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.

- Erogato presso 9793875_1 DATA ANALYSIS in Data science for management LM-91 PUNZO ANTONIO

SECS-S/01

Attività formative caratterizzanti

ENG

- STATISTICAL LEARNING

Erogato anche in altro semestre o anno

9793879 - OPTIMIZATION (obiettivi)

- DANIELE Patrizia

MAT/09

Attività formative caratterizzanti

ENG

9793940 - STATISTICAL LABORATORY (obiettivi)

- FURNARI ANTONINO

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. - Erogato presso 9793926_2 BIG DATA ANALYTICS in Data science for management LM-91 TOMARCHIO Orazio	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. - Erogato presso 9793875_2 STATISTICAL LEARNING in Data science for management LM-91 INGRASSIA Salvatore	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. - Erogato presso 9793878 BEHAVIORAL ECONOMICS AND COMPLEXITY in Data science for management LM-91 BIONDO ALESSIO EMANUELE	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. - Erogato presso 9793876 DIGITAL INNOVATION AND TRANSFORMATION MANAGEMENT in Data science for management LM-91 ROMANO Marco	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

- CARCHIOLO Vincenza (programma)

INF/01

Attività formative caratterizzanti

ENG

- KNOWLEDGE DISCOVERY

Erogato anche in altro semestre o anno

9793880 - COMPUTER SECURITY AND DATA PROTECTION

- CATALANO Dario (programma)

INF/01

Attività formative caratterizzanti

ENG

9793881 - NEURAL COMPUTING

- BATTIATO SEBASTIANO (programma)

INF/01

Attività formative caratterizzanti

ENG

Gruppo opzionale: SUPPLEMENTARY COURSES - (visualizza)

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 - COMMIS GIULIA (programma) Modalità di raccolta dei dati. Introduzione ai metodi di ricerca del sondaggio. Componente dei sondaggi. Approcci alla consegna dei sondaggi. Tecniche di campionamento. Progettazione di questionari. Definire gli obiettivi della ricerca. Identificare la popolazione e il campione. Domande di progettazione. Tipi di misure e Domande. Tipi di errore nei sondaggi. Valutazione delle domande del sondaggio. Preparazione dei dati del sondaggio per l'analisi. Formattazione di un file di dati. Codifica e inserimento dati. Pulizia dei dati. Laboratorio in R e/o in SAS. Analisi statistiche dei dati del questionario. Descrizione delle risposte. Descrizione delle Relazioni tra variabili. Analisi di classi latenti. Teoria della risposta all'oggetto. Laboratorio in R e/o in SAS. alissard B. (2012), Analysis of Questionnaire Data with R, CRC Press, Boca Raton Bartolucci F., Bacci S., Gnaldi M. (2016), Statistical Analysis of Questionnaires, CRC Press, Boca Raton Fowler F. J. (2009), Survey Research Methods, SAGE Publications, Thousand Oaks, California Lecture notes and slides	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 - CARAMMIA MARCELLO (programma) The large-scale, high-frequency and real-timeliness of Big Data (BD) provide unprecedented opportunities for understanding, monitoring and forecasting social and political phenomenons. Just to provide some examples, real-time tweets can be used to monitor moods and opinions as well as to forecast election results; online polls and surveys can be used to measure political preferences; political speeches can be used to trace the relevance of policy issues over time and space; geotagged event data can be used for monitoring and early warning of social uprisings or conflicts. Similarly, the potential of BD for the evaluation of policy programmes is enormous. Big Data, and the Data Science (DS) needed to analyse them, represent the largest transformation of social -science for decades – so much so that a ‘new’ field of studies emerged that is referred to as Computational Social Science. This course focuses on the methods for designing political (and social) science research, with a special emphasis on innovative (big) data sources and DS methods. We will discuss how to design a political science research project, from asking good research questions to selecting data sources, formulating and testing hypotheses, and evaluating findings. Throughout, we will highlight the key differences between designing research for exploring problems, for developing theories, and for making forecasts – and we will attempt to build bridges between them. While you will be expected to pick up your topic and develop your own research project throughout the course, this is not a primarily technical course. Rather than on analytical techniques or statistical modelling, the emphasis will be on developing the ability to design, carry out and evaluate research that exploits the potential of big data and data science to understand political and social problems. NOTA BENE: Students should not be intimidated by the amount of readings! Before each seminar, you will normally be expected to read one chapter on the ‘general’ topic under discussion. One additional reading will normally be a piece of research (see research in focus below) that we will use to make sense of how ‘general’ questions are addressed ‘in practice’ in applied research. In addition to the single topics enumerated below, we will also have 'research in focus' sessions to discuss particular applications of computational political science. RESEARCH IN FOCUS Computational political science: applications of big data science to political science research/I. • Big data in surveys for the study of elections, public opinion and representation (Warshaw in Alvarez 2016). • Political event real time data (Beieler et al in Alvarez 2016). • Network analysis (Sinclair in Alvarez 2016). • Social media and protests (Tucker et al in Alvarez 2016). • Social marketing for smart government (Griepentrog in Alvarez 2016). • Machine learning algorithms for election fraud detection (Levin et al in Alvarez 2016). • Social media for nowcasting and forecasting elections (Ceron/Curini/Iacus 2017). Computational political science: applications of big data science to political science research/II. • International Trade with Big Data C. A. Hidalgo, B. Klinger, A.-L. Barab´asi, R. Hausmann. “The Product Space Conditions the Development of Nations.” Science 317.5837 (2007): 482-487 • Lobbying and Campaign Contribution. In Song Kim. “Political Cleavages within Industry: Firm-level Lobbying for Trade Liberalization.” American Political Science Review, 111.1: 1-20. • Stephen Ansolabehere, John M. de Figueiredo, and James M. Snyder. “Why is There so Little Money in U.S. Politics?” Journal of Economic Perspectives, 17.1 (2003): 105-130 Identifying Behavioral Patterns using Massive Data Reading • Gary King, Jennifer Pan, and Margaret E Roberts. “How Censorship in China Allows Government Criticism but Silences Collective Expression.” American Political Science Review, 107.2: 326-343. • Pierson, E., Simoiu, C., Overgoor, J., Corbett-Davies, S., Ramachandran, V., Phillips, C., and Goel, S. (2017). “A large-scale Analysis of Racial Disparities in Police Stops across the United States.” arXiv preprint arXiv:1706.05678. Measuring Ideological and Political Preferences using Social Network Data • Robert Bond and Solomon Messing. “Quantifying Social Media’s Political Space: Estimating Ideology from Publicly Revealed Preferences on Facebook.” American Political Science Review 109.1 (2015): 62-78. • Pablo Barbera “Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data.” Political Analysis 23.1 (2014): 76-91 What do Politicians Do? • Justin Grimmer, Solomon Messing, and Sean Westwood. “How Words and Money Cultivate a Personal Vote: The Effect of Legislator Credit Claiming on Constituent Credit Allocation.” American Political Science Review, 106.4 (2012), 703-719 Big Administrative Data: Promises and Pitfalls • Connelly, R., Playford, C.J., Gayle, V., Dibben, C., 2016. “The Role of Administrative Data in the Big Data Revolution in Social Science Research.” Social Science Research, Special issue on Big Data in the Social Sciences 59, 112 • Kopczuk, W., Saez, E., Song, J., 2010. “Earnings Inequality and Mobility in the United States: Evidence from Social Security Data Since 1937.” The Quarterly Journal of Economics 125, 91128. • Machine Learning Algorithms in Society. Kleinberg, Jon, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan. 2018. “Human Decisions and Machine Predictions.” The Quarterly Journal of Economics 133 (1):23793 CORE TEXTS • Alvarez, R.M. (2016). Computational Social Science: Discovery and Prediction. New York, NY: Cambridge University Press. • Kellstedt, P.M. & Whitten, G.D. (2018). The Fundamentals of Political Science Research. Cambridge Core. • Toshkov, D. (2016). Research Design in Political Science. London New York, NY: Palgrave. SUPPLEMENTARY READINGS • Ceron, A., Curini, L. & Iacus, S.M. (2017). Politics and Big Data: Nowcasting and Forecasting Elections with Social Media. London ; New York, NY: Routledge. • Cioffi-Revilla, C. (2017). Introduction to Computational Social Science: Principles and Applications (2 edition.). New York, NY: Springer-Verlag • Gerring, J. (2012). Social Science Methodology: A Unified Framework (2 edition). New York: Cambridge University Press. • Jungherr, A. (2015). Analyzing Political Communication with Digital Trace Data: The Role of Twitter Messages in Social Science Research. Cham: Springer Verlag. • Lowndes, V., Marsh, D. & Stoker, G. (2017). Theory and Methods in Political Science (4 edition). Basingstoke: Palgrave MacMillan.	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

- Erogato presso 9793877_2 KNOWLEDGE DISCOVERY in Data science for management LM-91 SPAMPINATO CONCETTO

ING-INF/05

Attività formative caratterizzanti

ENG

Gruppo opzionale: SUPPLEMENTARY COURSES - (visualizza)

9793962 - THESIS

300

Per la prova finale e la lingua straniera (art.10, comma 5, lettera c)

ENG

9793963 - ELECTIVE COURSE

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

Gruppo opzionale: Supplementary course among "Fundamental of economics" and "Basics of computing" - (visualizza)

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. - CASERTA Maurizio Giuseppe Giacomo (programma) This course teaches the most important basic concepts in microeconomics. Students will be introduced to a range of economic tools from microeconomics used to study models explicitly involving consumer and firm behavior in monopoly, oligopoly, perfect competition and the role of prices and institutions. For each topic case studies and real-world examples will be analyzed. Hal R. Varian, INTERMEDIATE MICROECONOMICS, Ninth Edition, W. W. Norton & Company, 2020	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. - ORTIS ALESSANDRO	9	INF/01	60	-	-	-	-	Attività formative affini ed integrative	ENG

9793926 - DATA BASE AND BIG DATA ANALYTICS (obiettivi)

- DATA BASE (obiettivi)

- PALAZZO SIMONE

ING-INF/05

Attività formative caratterizzanti

ENG

- BIG DATA ANALYTICS

Erogato anche in altro semestre o anno

9793875 - DATA ANALYSIS AND STATISTICAL LEARNING (obiettivi)

- DATA ANALYSIS (obiettivi)

- Erogato presso 9793875_1 DATA ANALYSIS in Data science for management LM-91 PUNZO ANTONIO

SECS-S/01

Attività formative caratterizzanti

ENG

- STATISTICAL LEARNING

Erogato anche in altro semestre o anno

9793879 - OPTIMIZATION (obiettivi)

- Erogato presso 9793879 OPTIMIZATION in Data science for management LM-91 DANIELE Patrizia

MAT/09

Attività formative caratterizzanti

ENG

9793940 - STATISTICAL LABORATORY (obiettivi)

- Erogato presso 9793940 STATISTICAL LABORATORY in Data science for management LM-91 FURNARI ANTONINO

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. - TOMARCHIO Orazio	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. - Erogato presso 9793875_2 STATISTICAL LEARNING in Data science for management LM-91 INGRASSIA Salvatore	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. - Erogato presso 9793878 BEHAVIORAL ECONOMICS AND COMPLEXITY in Data science for management LM-91 BIONDO ALESSIO EMANUELE	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. - Erogato presso 9793876 DIGITAL INNOVATION AND TRANSFORMATION MANAGEMENT in Data science for management LM-91 ROMANO Marco	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

- Erogato presso 9793877_1 ADVANCED MACHINE LEARNING in Data science for management LM-91 CARCHIOLO Vincenza

INF/01

Attività formative caratterizzanti

ENG

- KNOWLEDGE DISCOVERY

Erogato anche in altro semestre o anno

9793927 - IoT AND BIG DATA FOR SMART SPACES

- PALESI MAURIZIO

ING-INF/05

Attività formative caratterizzanti

ENG

9793928 - BIG DATA FOR SMART MANUFACTURING

- CAVALIERI Salvatore

ING-INF/05

Attività formative caratterizzanti

ENG

Gruppo opzionale: SUPPLEMENTARY COURSES - (visualizza)

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 - COMMIS GIULIA (programma) Modalità di raccolta dei dati. Introduzione ai metodi di ricerca del sondaggio. Componente dei sondaggi. Approcci alla consegna dei sondaggi. Tecniche di campionamento. Progettazione di questionari. Definire gli obiettivi della ricerca. Identificare la popolazione e il campione. Domande di progettazione. Tipi di misure e Domande. Tipi di errore nei sondaggi. Valutazione delle domande del sondaggio. Preparazione dei dati del sondaggio per l'analisi. Formattazione di un file di dati. Codifica e inserimento dati. Pulizia dei dati. Laboratorio in R e/o in SAS. Analisi statistiche dei dati del questionario. Descrizione delle risposte. Descrizione delle Relazioni tra variabili. Analisi di classi latenti. Teoria della risposta all'oggetto. Laboratorio in R e/o in SAS. alissard B. (2012), Analysis of Questionnaire Data with R, CRC Press, Boca Raton Bartolucci F., Bacci S., Gnaldi M. (2016), Statistical Analysis of Questionnaires, CRC Press, Boca Raton Fowler F. J. (2009), Survey Research Methods, SAGE Publications, Thousand Oaks, California Lecture notes and slides	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

- SPAMPINATO CONCETTO

ING-INF/05

Attività formative caratterizzanti

ENG

9793962 - THESIS

300

Per la prova finale e la lingua straniera (art.10, comma 5, lettera c)

ENG

9793963 - ELECTIVE COURSE

Attività formative a scelta dello studente (art.10, comma 5, lettera a)

ENG

Gruppo opzionale: SUPPLEMENTARY COURSES - (visualizza)

9793955 - CLOUD COMPUTING AND BIG DATA - PAPPALARDO Giuseppe	6	INF/01	40	-	-	-	-	Attività formative affini ed integrative	ENG
9793943 - BIG DATA SENSING, COMPRESSION AND COMMUNICATION - GALLUCCIO LAURA	6	ING-INF/03	40	-	-	-	-	Attività formative affini ed integrative	ENG
9793930 - MODELLING AND ANALYSIS FOR COMPLEX SYSTEMS - NUNNARI Giuseppe	6	ING-INF/04	40	-	-	-	-	Attività formative affini ed integrative	ENG
9793931 - DATA AND METHODS FOR PUBLIC POLICIES EVALUATION - GUCCIO Calogero	6	SECS-P/03	40	-	-	-	-	Attività formative affini ed integrative	ENG
9793932 - CREDIT RISK MANAGEMENT - MURIANA FRANCESCO	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 - MAUGERI ANDREA GIUSEPPE	6	MED/42	40	-	-	-	-	Attività formative affini ed integrative	ENG
9793936 - DECISION SCIENCES - GIARLOTTA Alfio	6	SECS-S/06	40	-	-	-	-	Attività formative affini ed integrative	ENG
9793939 - METHODOLOGY OF POLITICAL SCIENCE - CARAMMIA MARCELLO (programma) The large-scale, high-frequency and real-timeliness of Big Data (BD) provide unprecedented opportunities for understanding, monitoring and forecasting social and political phenomenons. Just to provide some examples, real-time tweets can be used to monitor moods and opinions as well as to forecast election results; online polls and surveys can be used to measure political preferences; political speeches can be used to trace the relevance of policy issues over time and space; geotagged event data can be used for monitoring and early warning of social uprisings or conflicts. Similarly, the potential of BD for the evaluation of policy programmes is enormous. Big Data, and the Data Science (DS) needed to analyse them, represent the largest transformation of social -science for decades – so much so that a ‘new’ field of studies emerged that is referred to as Computational Social Science. This course focuses on the methods for designing political (and social) science research, with a special emphasis on innovative (big) data sources and DS methods. We will discuss how to design a political science research project, from asking good research questions to selecting data sources, formulating and testing hypotheses, and evaluating findings. Throughout, we will highlight the key differences between designing research for exploring problems, for developing theories, and for making forecasts – and we will attempt to build bridges between them. While you will be expected to pick up your topic and develop your own research project throughout the course, this is not a primarily technical course. Rather than on analytical techniques or statistical modelling, the emphasis will be on developing the ability to design, carry out and evaluate research that exploits the potential of big data and data science to understand political and social problems. NOTA BENE: Students should not be intimidated by the amount of readings! Before each seminar, you will normally be expected to read one chapter on the ‘general’ topic under discussion. One additional reading will normally be a piece of research (see research in focus below) that we will use to make sense of how ‘general’ questions are addressed ‘in practice’ in applied research. In addition to the single topics enumerated below, we will also have 'research in focus' sessions to discuss particular applications of computational political science. RESEARCH IN FOCUS Computational political science: applications of big data science to political science research/I. • Big data in surveys for the study of elections, public opinion and representation (Warshaw in Alvarez 2016). • Political event real time data (Beieler et al in Alvarez 2016). • Network analysis (Sinclair in Alvarez 2016). • Social media and protests (Tucker et al in Alvarez 2016). • Social marketing for smart government (Griepentrog in Alvarez 2016). • Machine learning algorithms for election fraud detection (Levin et al in Alvarez 2016). • Social media for nowcasting and forecasting elections (Ceron/Curini/Iacus 2017). Computational political science: applications of big data science to political science research/II. • International Trade with Big Data C. A. Hidalgo, B. Klinger, A.-L. Barab´asi, R. Hausmann. “The Product Space Conditions the Development of Nations.” Science 317.5837 (2007): 482-487 • Lobbying and Campaign Contribution. In Song Kim. “Political Cleavages within Industry: Firm-level Lobbying for Trade Liberalization.” American Political Science Review, 111.1: 1-20. • Stephen Ansolabehere, John M. de Figueiredo, and James M. Snyder. “Why is There so Little Money in U.S. Politics?” Journal of Economic Perspectives, 17.1 (2003): 105-130 Identifying Behavioral Patterns using Massive Data Reading • Gary King, Jennifer Pan, and Margaret E Roberts. “How Censorship in China Allows Government Criticism but Silences Collective Expression.” American Political Science Review, 107.2: 326-343. • Pierson, E., Simoiu, C., Overgoor, J., Corbett-Davies, S., Ramachandran, V., Phillips, C., and Goel, S. (2017). “A large-scale Analysis of Racial Disparities in Police Stops across the United States.” arXiv preprint arXiv:1706.05678. Measuring Ideological and Political Preferences using Social Network Data • Robert Bond and Solomon Messing. “Quantifying Social Media’s Political Space: Estimating Ideology from Publicly Revealed Preferences on Facebook.” American Political Science Review 109.1 (2015): 62-78. • Pablo Barbera “Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data.” Political Analysis 23.1 (2014): 76-91 What do Politicians Do? • Justin Grimmer, Solomon Messing, and Sean Westwood. “How Words and Money Cultivate a Personal Vote: The Effect of Legislator Credit Claiming on Constituent Credit Allocation.” American Political Science Review, 106.4 (2012), 703-719 Big Administrative Data: Promises and Pitfalls • Connelly, R., Playford, C.J., Gayle, V., Dibben, C., 2016. “The Role of Administrative Data in the Big Data Revolution in Social Science Research.” Social Science Research, Special issue on Big Data in the Social Sciences 59, 112 • Kopczuk, W., Saez, E., Song, J., 2010. “Earnings Inequality and Mobility in the United States: Evidence from Social Security Data Since 1937.” The Quarterly Journal of Economics 125, 91128. • Machine Learning Algorithms in Society. Kleinberg, Jon, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan. 2018. “Human Decisions and Machine Predictions.” The Quarterly Journal of Economics 133 (1):23793 CORE TEXTS • Alvarez, R.M. (2016). Computational Social Science: Discovery and Prediction. New York, NY: Cambridge University Press. • Kellstedt, P.M. & Whitten, G.D. (2018). The Fundamentals of Political Science Research. Cambridge Core. • Toshkov, D. (2016). Research Design in Political Science. London New York, NY: Palgrave. SUPPLEMENTARY READINGS • Ceron, A., Curini, L. & Iacus, S.M. (2017). Politics and Big Data: Nowcasting and Forecasting Elections with Social Media. London ; New York, NY: Routledge. • Cioffi-Revilla, C. (2017). Introduction to Computational Social Science: Principles and Applications (2 edition.). New York, NY: Springer-Verlag • Gerring, J. (2012). Social Science Methodology: A Unified Framework (2 edition). New York: Cambridge University Press. • Jungherr, A. (2015). Analyzing Political Communication with Digital Trace Data: The Role of Twitter Messages in Social Science Research. Cham: Springer Verlag. • Lowndes, V., Marsh, D. & Stoker, G. (2017). Theory and Methods in Political Science (4 edition). Basingstoke: Palgrave MacMillan.	6	SPS/04	40	-	-	-	-	Attività formative affini ed integrative	ENG
9793970 - PRIVATE LAW FOR INFORMATION TECHNOLOGY - AMORE GIULIANA	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

Gruppo opzionale: Supplementary course among "Fundamental of economics" and "Basics of computing" - (visualizza)

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. - CASERTA Maurizio Giuseppe Giacomo (programma) This course teaches the most important basic concepts in microeconomics. Students will be introduced to a range of economic tools from microeconomics used to study models explicitly involving consumer and firm behavior in monopoly, oligopoly, perfect competition and the role of prices and institutions. For each topic case studies and real-world examples will be analyzed. Hal R. Varian, INTERMEDIATE MICROECONOMICS, Ninth Edition, W. W. Norton & Company, 2020	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. - ORTIS ALESSANDRO	9	INF/01	60	-	-	-	-	Attività formative affini ed integrative	ENG

9793926 - DATA BASE AND BIG DATA ANALYTICS (obiettivi)

- DATA BASE (obiettivi)

- Erogato presso 9793926_1 DATA BASE in Data science for management LM-91 PALAZZO SIMONE

ING-INF/05

Attività formative caratterizzanti

ENG

- BIG DATA ANALYTICS

Erogato anche in altro semestre o anno

9793875 - DATA ANALYSIS AND STATISTICAL LEARNING (obiettivi)

- DATA ANALYSIS (obiettivi)

- PUNZO ANTONIO

SECS-S/01

Attività formative caratterizzanti

ENG

- STATISTICAL LEARNING

Erogato anche in altro semestre o anno

9793879 - OPTIMIZATION (obiettivi)

- Erogato presso 9793879 OPTIMIZATION in Data science for management LM-91 DANIELE Patrizia

MAT/09

Attività formative caratterizzanti

ENG

9793940 - STATISTICAL LABORATORY (obiettivi)

- Erogato presso 9793940 STATISTICAL LABORATORY in Data science for management LM-91 FURNARI ANTONINO

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. - Erogato presso 9793926_2 BIG DATA ANALYTICS in Data science for management LM-91 TOMARCHIO Orazio	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. - INGRASSIA Salvatore	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. - ROMANO Marco	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. - BIONDO ALESSIO EMANUELE (programma) he course aims to present the developments in the context of Micro- and Macroeconomic Theory deriving from the analysis of, respectively, the behavioral approach in the choice paradigm and the emergent dynamics in collective phenomena. Thus, the first part of the course will provide a broad introduction to the concept of rationality in economics and to the advances of modern literature related to the implementation of bounded rationality and behavioral approaches. The second part, instead, will be focused on the concepts of complexity, heterogeneity and interaction in economic models, and special attention will be devoted to the study of macroeconomic agent-based models. For both blocks, a step-by-step introduction of essential ABM building elements will be provided. Finally, a user-friendly open access platform will be presented in order to enable students to build their own models and perform simulations to be confronted with empirical data for policies assessment. A - Economic Behavior and Microeconomics: Rational choice in Economics and Bounded Rationality; Introduction to Computational Economics. B - Complexity and Macroeconomics: Complexity and Emergent Macroeconomics; Network Economics, Agent-based Modeling. Becker G.S., The Economic Approach to Human Behavior, The University of Chicago Press, 1976, chapters: 1, 7, 8, 12, 13. Gilboa I., Rational Choice, 2010, The MIT Press, Cambridge. Simon H.A., (1955) A Behavioral Model of Rational Choice, The Quarterly Journal of Economics, vol.69, No.1., pp.99-118. Kahneman D., Tversky A., (1979) An Analysis of Decision under Risk, Econometrica, Vol. 47, No. 2 (Mar., 1979), pp. 263-291 Kahneman D., Tversky A., (1984) Choice, Values, Frames, American Psychologist, vol. 39, No. 4, 341-350. Cioffi-Revilla C., Introduction to Computational Social Science, Springer 2014, chapters: 5, 6, 7. Delli Gatti D., Gaffeo E., Gallegati M., Giulioni G., Palestrini A., Emergent Macroeconomics, 2008, Springer-Verlag, Milan. Jackson M.O., Social and Economic Networks, 2010, Princeton University Press, chapters: 2, 3, 6, 7, 8, 9, 12.	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

- Erogato presso 9793877_1 ADVANCED MACHINE LEARNING in Data science for management LM-91 CARCHIOLO Vincenza

INF/01

Attività formative caratterizzanti

ENG

- KNOWLEDGE DISCOVERY

Erogato anche in altro semestre o anno

9793884 - HIGH TECH MARKETS, INDUSTRIAL ORGANIZATION AND GROWTH

- TORRISI GIANPIERO

SECS-P/06

Attività formative caratterizzanti

ENG

9793885 - ACCOUNTING INFORMATION SYSTEMS

- CATALFO Pierluigi (programma)

SECS-P/07

Attività formative caratterizzanti

ENG

Gruppo opzionale: SUPPLEMENTARY COURSES - (visualizza)

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 - COMMIS GIULIA (programma) Modalità di raccolta dei dati. Introduzione ai metodi di ricerca del sondaggio. Componente dei sondaggi. Approcci alla consegna dei sondaggi. Tecniche di campionamento. Progettazione di questionari. Definire gli obiettivi della ricerca. Identificare la popolazione e il campione. Domande di progettazione. Tipi di misure e Domande. Tipi di errore nei sondaggi. Valutazione delle domande del sondaggio. Preparazione dei dati del sondaggio per l'analisi. Formattazione di un file di dati. Codifica e inserimento dati. Pulizia dei dati. Laboratorio in R e/o in SAS. Analisi statistiche dei dati del questionario. Descrizione delle risposte. Descrizione delle Relazioni tra variabili. Analisi di classi latenti. Teoria della risposta all'oggetto. Laboratorio in R e/o in SAS. alissard B. (2012), Analysis of Questionnaire Data with R, CRC Press, Boca Raton Bartolucci F., Bacci S., Gnaldi M. (2016), Statistical Analysis of Questionnaires, CRC Press, Boca Raton Fowler F. J. (2009), Survey Research Methods, SAGE Publications, Thousand Oaks, California Lecture notes and slides	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

- Erogato presso 9793877_2 KNOWLEDGE DISCOVERY in Data science for management LM-91 SPAMPINATO CONCETTO

ING-INF/05

Attività formative caratterizzanti

ENG

Gruppo opzionale: SUPPLEMENTARY COURSES - (visualizza)

9793955 - CLOUD COMPUTING AND BIG DATA - PAPPALARDO Giuseppe	6	INF/01	40	-	-	-	-	Attività formative affini ed integrative	ENG
9793943 - BIG DATA SENSING, COMPRESSION AND COMMUNICATION - GALLUCCIO LAURA	6	ING-INF/03	40	-	-	-	-	Attività formative affini ed integrative	ENG
9793930 - MODELLING AND ANALYSIS FOR COMPLEX SYSTEMS - NUNNARI Giuseppe	6	ING-INF/04	40	-	-	-	-	Attività formative affini ed integrative	ENG
9793931 - DATA AND METHODS FOR PUBLIC POLICIES EVALUATION - GUCCIO Calogero	6	SECS-P/03	40	-	-	-	-	Attività formative affini ed integrative	ENG
9793932 - CREDIT RISK MANAGEMENT - MURIANA FRANCESCO	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 - MAUGERI ANDREA GIUSEPPE	6	MED/42	40	-	-	-	-	Attività formative affini ed integrative	ENG
9793936 - DECISION SCIENCES - GIARLOTTA Alfio	6	SECS-S/06	40	-	-	-	-	Attività formative affini ed integrative	ENG
9793939 - METHODOLOGY OF POLITICAL SCIENCE - CARAMMIA MARCELLO (programma) The large-scale, high-frequency and real-timeliness of Big Data (BD) provide unprecedented opportunities for understanding, monitoring and forecasting social and political phenomenons. Just to provide some examples, real-time tweets can be used to monitor moods and opinions as well as to forecast election results; online polls and surveys can be used to measure political preferences; political speeches can be used to trace the relevance of policy issues over time and space; geotagged event data can be used for monitoring and early warning of social uprisings or conflicts. Similarly, the potential of BD for the evaluation of policy programmes is enormous. Big Data, and the Data Science (DS) needed to analyse them, represent the largest transformation of social -science for decades – so much so that a ‘new’ field of studies emerged that is referred to as Computational Social Science. This course focuses on the methods for designing political (and social) science research, with a special emphasis on innovative (big) data sources and DS methods. We will discuss how to design a political science research project, from asking good research questions to selecting data sources, formulating and testing hypotheses, and evaluating findings. Throughout, we will highlight the key differences between designing research for exploring problems, for developing theories, and for making forecasts – and we will attempt to build bridges between them. While you will be expected to pick up your topic and develop your own research project throughout the course, this is not a primarily technical course. Rather than on analytical techniques or statistical modelling, the emphasis will be on developing the ability to design, carry out and evaluate research that exploits the potential of big data and data science to understand political and social problems. NOTA BENE: Students should not be intimidated by the amount of readings! Before each seminar, you will normally be expected to read one chapter on the ‘general’ topic under discussion. One additional reading will normally be a piece of research (see research in focus below) that we will use to make sense of how ‘general’ questions are addressed ‘in practice’ in applied research. In addition to the single topics enumerated below, we will also have 'research in focus' sessions to discuss particular applications of computational political science. RESEARCH IN FOCUS Computational political science: applications of big data science to political science research/I. • Big data in surveys for the study of elections, public opinion and representation (Warshaw in Alvarez 2016). • Political event real time data (Beieler et al in Alvarez 2016). • Network analysis (Sinclair in Alvarez 2016). • Social media and protests (Tucker et al in Alvarez 2016). • Social marketing for smart government (Griepentrog in Alvarez 2016). • Machine learning algorithms for election fraud detection (Levin et al in Alvarez 2016). • Social media for nowcasting and forecasting elections (Ceron/Curini/Iacus 2017). Computational political science: applications of big data science to political science research/II. • International Trade with Big Data C. A. Hidalgo, B. Klinger, A.-L. Barab´asi, R. Hausmann. “The Product Space Conditions the Development of Nations.” Science 317.5837 (2007): 482-487 • Lobbying and Campaign Contribution. In Song Kim. “Political Cleavages within Industry: Firm-level Lobbying for Trade Liberalization.” American Political Science Review, 111.1: 1-20. • Stephen Ansolabehere, John M. de Figueiredo, and James M. Snyder. “Why is There so Little Money in U.S. Politics?” Journal of Economic Perspectives, 17.1 (2003): 105-130 Identifying Behavioral Patterns using Massive Data Reading • Gary King, Jennifer Pan, and Margaret E Roberts. “How Censorship in China Allows Government Criticism but Silences Collective Expression.” American Political Science Review, 107.2: 326-343. • Pierson, E., Simoiu, C., Overgoor, J., Corbett-Davies, S., Ramachandran, V., Phillips, C., and Goel, S. (2017). “A large-scale Analysis of Racial Disparities in Police Stops across the United States.” arXiv preprint arXiv:1706.05678. Measuring Ideological and Political Preferences using Social Network Data • Robert Bond and Solomon Messing. “Quantifying Social Media’s Political Space: Estimating Ideology from Publicly Revealed Preferences on Facebook.” American Political Science Review 109.1 (2015): 62-78. • Pablo Barbera “Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data.” Political Analysis 23.1 (2014): 76-91 What do Politicians Do? • Justin Grimmer, Solomon Messing, and Sean Westwood. “How Words and Money Cultivate a Personal Vote: The Effect of Legislator Credit Claiming on Constituent Credit Allocation.” American Political Science Review, 106.4 (2012), 703-719 Big Administrative Data: Promises and Pitfalls • Connelly, R., Playford, C.J., Gayle, V., Dibben, C., 2016. “The Role of Administrative Data in the Big Data Revolution in Social Science Research.” Social Science Research, Special issue on Big Data in the Social Sciences 59, 112 • Kopczuk, W., Saez, E., Song, J., 2010. “Earnings Inequality and Mobility in the United States: Evidence from Social Security Data Since 1937.” The Quarterly Journal of Economics 125, 91128. • Machine Learning Algorithms in Society. Kleinberg, Jon, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan. 2018. “Human Decisions and Machine Predictions.” The Quarterly Journal of Economics 133 (1):23793 CORE TEXTS • Alvarez, R.M. (2016). Computational Social Science: Discovery and Prediction. New York, NY: Cambridge University Press. • Kellstedt, P.M. & Whitten, G.D. (2018). The Fundamentals of Political Science Research. Cambridge Core. • Toshkov, D. (2016). Research Design in Political Science. London New York, NY: Palgrave. SUPPLEMENTARY READINGS • Ceron, A., Curini, L. & Iacus, S.M. (2017). Politics and Big Data: Nowcasting and Forecasting Elections with Social Media. London ; New York, NY: Routledge. • Cioffi-Revilla, C. (2017). Introduction to Computational Social Science: Principles and Applications (2 edition.). New York, NY: Springer-Verlag • Gerring, J. (2012). Social Science Methodology: A Unified Framework (2 edition). New York: Cambridge University Press. • Jungherr, A. (2015). Analyzing Political Communication with Digital Trace Data: The Role of Twitter Messages in Social Science Research. Cham: Springer Verlag. • Lowndes, V., Marsh, D. & Stoker, G. (2017). Theory and Methods in Political Science (4 edition). Basingstoke: Palgrave MacMillan.	6	SPS/04	40	-	-	-	-	Attività formative affini ed integrative	ENG
9793970 - PRIVATE LAW FOR INFORMATION TECHNOLOGY - AMORE GIULIANA	6	IUS/01	40	-	-	-	-	Attività formative affini ed integrative	ENG

9793962 - THESIS

300

Per la prova finale e la lingua straniera (art.10, comma 5, lettera c)

ENG

9793963 - ELECTIVE COURSE

Attività formative a scelta dello studente (art.10, comma 5, lettera a)

ENG