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Course
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Credits
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Scientific Disciplinary Sector Code
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Contact Hours
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Exercise Hours
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Laboratory Hours
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Personal Study Hours
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Type of Activity
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Language
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21210215 -
Basi di dati e big data
(objectives)
The course has the goal of presenting of models, methods and tools for the management of large sets of data (“data bases”) and for their design and implementation. Specific attention will be devoted to the issues related to the use of data bases for analytics and evaluation. The student will make experiences with traditional systems, based on the relational model and SQL as well as to specific tools for data warehousing and data mining.
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DI SANZO PIERANGELO
( syllabus)
Introduction to databases
Data modeling overview
The relational model
The SQL language
Database management systems
Data warehouse
Introduction to data mining
Data mining problems
Big Data: characteristics, aspects and applications
Frameworks and technologies for Big Data
( reference books)
Title: Basi di dati
Edition: 6
Authors: Paolo Atzeni, Stefano Ceri, Piero Fraternali, Stefano Paraboschi, Riccardo Torlone
Publication date: February 1, 2023
Publisher: McGraw Hill
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6
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ING-INF/05
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40
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-
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-
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-
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Related or supplementary learning activities
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ITA |
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21210218 -
Dati e analisi per la politica economica
(objectives)
The course focuses on the central role that empirical evidence plays in the economic policy debate. The aim of the course is to explore how to organise the analysis of data and evaluation tools in order to assess the impacts of economic policies with counterfactual techniques. Having defined the basic conceptual and methodological elements, reference will be made to a set of specific economic policies (from local to global scale) in order to help students analyse and gain awareness of: data sources in the relevant socio-economic context, interoperability and requirements for economic policy analysis, relevant evaluation questions, empirical applications to isolate the effects of so-called confounding factors. At the end of the course the student will be able to: set up the evaluation design of a policy instrument, select data, manage the surveillance, monitoring and evaluation phases in the implementation of economic policies, interpret economic results.
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9
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SECS-P/02
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60
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-
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-
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Core compulsory activities
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ITA |
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21210217 -
Economia dei mercati digitali
(objectives)
The course aims to provide students with the basic contents and tools for understanding and analyzing digital markets. The first part of the course is dedicated to the study of the fundamentals of the functioning of markets and industrial policies. A specific study on the characteristics of the markets and digital platforms will be provided in the second part. In particular, the main economic characteristics of digital platforms with two or more sides will be illustrated (such as network externalities, feedback loops, demand interdependence and multi-homing),since they are essential to understand the competitive dynamics and strategic conduct in these markets. In this context, particular attention will be paid to the role played by big data in the market dynamics and in the various business models implemented. The accumulation of data and the use of analysis and processing tools, the result of the increasingly wide availability of sophisticated analytical and technological tools (including cloud computing, artificial intelligence and machine learning), represent one of the main challenges for institutions, policy-makers and scholars and forces us to rethink the boundaries that have traditionally delimited the respective areas of intervention of regulation and antitrust. The aforementioned contents will be accompanied by the analysis of the most current and relevant case studies involving the main Big Techs.
The knowledge and skills acquired will make it possible to elaborate, analyze and discuss the characteristics and dynamics of the digital economy and the role played in it by big data, platforms and regulatory authorities.
Prerequisites: Economia Politica.
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PIERUCCI ELEONORA
( syllabus)
• Tutte le slides delle lezioni saranno rese disponibili, insieme a tutti gli altri materiali rilevanti, esclusivamente sulla piattaforma moodle del corso.
• I paragrafi da studiare dei capitoli di riferimento riportati tra parentesi verranno meglio indicati durante le lezioni e coincideranno con le parti che verranno trattate in aula e nelle slides.
Parte I:
1. Introduzione e fondamenti di micro (capp.1 e 2 Pepall, L., Richards, D. J., Norman G. e Calzolari, G).
2. Discriminazioni di prezzo (capp. 5 e 6 Pepall, L., Richards, D. J., Norman G. e Calzolari, G).
3. Oligopolio statico (capp. 8 e 9 Pepall, L., Richards, D. J., Norman G. e Calzolari, G).
4. Collusione e giochi ripetuti (cap. 13 Pepall, L., Richards, D. J., Norman G. e Calzolari, G).
5. Fusioni (cap. 15 Pepall, L., Richards, D. J., Norman G. e Calzolari, G).
Parte II:
1. Introduzione alle reti e ai mercati digitali (cap. 19 Pepall, L., Richards, D. J., Norman G. e Calzolari, G).
2. Mercati a due o più versanti e piattaforme digitali (cap. 1 Belleflamme P. e Peitz).
3. Il ruolo dei Big data (cap. 2 Belleflamme P. e Peitz).
4. Strategie e politiche di prezzo delle piattaforme (capp. 4 e 5 Belleflamme P. e Peitz).
5. Governance e design delle piattaforme (cap. 6 Belleflamme P. e Peitz).
Libro di testo
Pepall, L., Richards, D. J., Norman G. e Calzolari, G., “Organizzazione Industriale” IV edizione, Mc Graw Hill Education.
Belleflamme P. e Peitz M. (2021), The Economics of Platform, Cambridge University Press. Liberamente scaricabile: https://www.cambridge.org/core/books/economics-of-platforms/1465A930513786676D369128B0AF9D21
( reference books)
Pepall, L., Richards, D. J., Norman G. e Calzolari, G., “Organizzazione Industriale” IV edizione, Mc Graw Hill Education.
Belleflamme P. e Peitz M. (2021), The Economics of Platform, Cambridge University Press. Liberamente scaricabile: https://www.cambridge.org/core/books/economics-of-platforms/1465A930513786676D369128B0AF9D21
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9
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SECS-P/06
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60
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-
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-
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Core compulsory activities
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ITA |
Optional group:
Due insegnamenti a scelta libera (di seguito gli insegnamenti consigliati) - (show)
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12
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21210225 -
Big data, pubblica amministrazione e digitalizzazione
(objectives)
The course examines the fundamental freedoms affected by the phenomenon of digitalisation, as well as the limits and opportunities of digitalisation for the functioning of the public administration and for the access to and use of big data in the Fin Tech and social fields (labour market, social security, health, training, etc.). The student must first be familiar with the basic legal categories of technological innovation and the digitalisation of businesses and markets (the exams of Legal Foundations of Technological Innovation and Digitalisation of Business and Labour, both compulsory, are necessary requirements to take the present exam).
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ATRIPALDI MARIANGELA
( syllabus)
The following topics, among others, will be addressed during the course:
Digital administration. The constitutional framework;
Legal issues of big data and digital infrastructures;
The European Digital Single Market and the EU Privacy Regulation;
Digital citizenship;
The modernisation and digitisation of the PA and the impulses of the supranational order;
Digitisation and the right to good administration;
The computerised document and computerised protocol;
Transparency and access in the age of digital administration;
Smart legal contracts in the public-private sector;
The digitisation of public contracts;
The telematic administrative process;
The Digital Market Act;
Digital administrative acts and procedures.
( reference books)
It is recommended the study of:
Il diritto dell'amministrazione pubblica digitale
Roberto Cavallo Perin (Editor) Diana-Urania Galetta (Editor)
Giappichelli, 2020.
Other papers may be distributed during class
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6
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IUS/09
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40
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-
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-
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-
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Elective activities
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ITA |
21210464 -
Machine learning and data processing
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BENEDETTO FRANCESCO
( syllabus)
Artificial Intelligence and Machine Learning Fundamentals. Types of Artificial Intelligence,
Classical AI System, Machine Learning Definition, Classical Approach and Applications.
Fundamentals of Machine Learning, Types of Learning, Training Methods,
Generalization Methods. Data Issues, Insufficient Data, Non-Representative
Data, Poor Quality Data. Machine Learning Issues Related to
Modeling, Model Selection, Complexity and Hyper-Parameter Setting.
Supervised Learning for Regression Problems. Linear Models, Mean
Square Error, Learning as MSE Minimization. Polynomial Regression.
Overfitting and Underfitting. Hyper-Parameter Optimization, Validation Set. Examples of
Programming in Matlab and Python Language.
Supervised Learning for Classification Problems. Binary Classification,
Logistic Regression. Metrics for binary classification problems, accuracy,
precision and recall, confusion matrix, F1-score, ROC and AuC. Separating hyperplanes
for binary classification problems, theoretical aspects and definition of hyperplane, concept of
margin, support vectors and linear non-separability. Programming examples in
Matlab and Python language.
Fundamental algorithms for supervised learning. Support vector
machines, separating hyperplanes with soft-margin constraints, kernel tricks and linearity.
Machine learning decision trees, choice of attributes and values, information
entropy. Ensemble learning, parallel models, random forest.
Sequential ensemble models and boosting. Model selection methods, validation set,
error decomposition and balancing, bias-variance trade-off.
Fundamental algorithms for unsupervised learning.
Clustering algorithms. K-means and optimal value of k, clustering applications, supervised approach,
semisupervised, with clustering. DBSCAN, practical rules advantages and disadvantages of
DBSCAN compared to k.means. Algorithms for dimensionality reduction, PCA, example
of using PCA for dimensionality reduction.
Learning with artificial neural networks. The perceptron. Multi-layer perceptron networks
(MLP). Clustering with neural networks. Learning vector quantization (LVQ). The
manifold learning problem and SOM networks. Deep learning with neural networks. Fundamental principles.
Convolutional neural networks (CNN). Notes on advanced Deep-Learning architectures. . Convolutional
neural networks, convolution layer, activation layer, pooling layer. Examples of
programming in Matlab and Python language.
Analysis, selection and transformation of data. Image analysis, decomposition in YUV/YCbCr color spaces. Time/frequency data analysis, Fourier transform (outline),
spectrograms. Programming examples in Matlab and Python.
Natural language processing. Transfer learning and model architectures for text
classification, question-answering, machine translation and text
generation.
( reference books)
Lecture notes by the teacher on the University Moodle and MS Teams platform
Book: G. Barone, “Machine Learning and Artificial Intelligence: methodologies for the development of automatic systems”, Dario Flaccovio Editore, 217 pp.
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6
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ING-INF/03
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40
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Elective activities
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ITA |
21210071 -
FINANCIAL REPORTING
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Derived from
21210071 BILANCIO in Economia L-33 Ciaburri Mirella
( syllabus)
Part 1 - The theory of Financial Statements
-Introduction to the study of the Financial Statements
- The basic functions of the Financial Statements
- The nature of income and capital
- The purposes of the Financial Statements
- The relationship between income, evaluation criteria and purposes of the Financial Statements
- The goals of the Financial Statements in corporate doctrine: the "alpha" goal and the "beta" goal
Part 2 - The Financial Statements according to the Civil Code
- The Financial Statement documents - Structure and content (Articles 2423-ter - 2425 of the Italian Civil Code)
- The general clause and the principles for preparing the Financial Statements (articles 2423 - 2423-bis of the Italian Civil Code)
- The specific evaluation criteria (art. 2426 of the civil code)
- Fixed assets
- Receivables
- Inventories (Lifo, Fifo, Cmp)
- Work in progress assets
- The content of the Notes and the Management Report
Part 3 – Accounting
- Closing accounting entries, general closure and reopening of accounts
- Fixed assets – Depreciation, amortization and devaluation
- Financial Investments – Equity Method
- Inventories and work in progress – FIFO, LIFO and WACC
Part 4 - Fundamentals of financial statement analysis
- Reclassification of BS and IS
- Ratios
- Statement of Cash Flows – Direct and Indirect Method
( reference books)
Suggested readings:
Part 1 and 2: Tutino M. (2017), Bilancio e nuovi OIC, Cedam
Part 3: Paoloni M., Celli M. (2012), Introduzione alla contabilità generale, Cedam
Part 4: Paolucci G. (2016), Analisi di Bilancio, Hoepli
Slides and exercises available on the Professor's web page
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6
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SECS-P/07
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40
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Elective activities
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ITA |
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Università Roma Tre