Data Science

Obiettivi formativi

The aim of the course is to provide students with an overview of the functioning of

intellectual property, competition and data protection law from both an economic and

legal perspective. By the end of the course students are expected to have acquired a

general understanding of the main policy issues involved, and should be able to identify

and apply the relevant legal rules, both substantial and procedural, in situations that can

be considered routinary to professionals and businesses operating in the data science

industry.

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Knowledge and understanding
The aim of the course is to introduce students to the new information economy and the economics of network industries. Students are expected to gain insight into how the specific features of technology and demand affect market structure, firms’ strategies and business models, as well as public policy in network industries.

Applying knowledge and understanding
By the end of the course, students should be able to use methods and models of microeconomics and industrial organization to understand and analyze the competitive dynamics in the new information economy, and specifically in network industries.

Making judgements
Lectures, practical exercises and problem-solving sessions will provide students with the ability to assess the main strengths and weaknesses of theoretical models when used to explain empirical evidence and case studies in the new information economy.

Communication
By the end of the course, students are able to point out the main features of the new information economy and network industries, and to discuss relevant information, ideas, problems and solutions both with a specialized and a non-specialized audience. These capabilities are tested and evaluated in the final written exam and possibly in the oral exam as well as in the project work.

Lifelong learning skills
Students are expected to develop those learning skills necessary to undertake additional studies on relevant topics in the field of the new information economy with a high degree of autonomy. During the course, students are encouraged to investigate further any topics of major interest, by consulting supplementary academic publications, specialized books, and internet sites. These capabilities are tested and evaluated in the final written exam and possibly in the oral exam as well as in the project work, where students may have to discuss and solve some new problems based on the topics and material covered in class.

Obiettivi formativi

The main goal of the course is to present the basic concepts of data

management systems. The first part of the course introduces the main aspects

of relational database systems, including basic functionalities, file and index

organizations, and query processing. The second part of the course aims

at presenting the main non-relational approaches to data management, in

particular, multidimensional data management, large-scale data management,

and open data management.

Obiettivi formativi

Obiettivi generali:

Lo scopo del corso è quello di fornire agli studenti i concetti di base dei sistemi distribuiti e quindi di concentrarsi sulle tecnologie di cloud computing. Il corso copre aspetti teorici e pratici con un focus su esempi reali. Alla fine del corso si suppone che gli studenti siano in grado di scegliere, configurare e utilizzare i servizi cloud e progettare e distribuire architetture scalabili ed applicazioni elastiche.

Obiettivi specifici:

Conoscenza e capacità di comprensione:
Al termine del corso, lo studente sarà in grado di descrivere e spiegare
- i concetti generali dei sistemi distribuiti
- il concetto di virtualizzazione di sistema e applicazione
- i meccanismi e gli algoritmi utilizzati nel cloud computing
- le tecnologie per lo storage cloud
- i framework per l’elaborazione dei bigdata
- i problemi di sicurezza informatica e le soluzioni nel cloud computing

Conoscenza e capacità di comprensione applicate:
Al termine del corso, lo studente sarà in grado di
- progettare e implementare un'architettura scalabile e distribuire un'applicazione elastica
- presentare risultati pratici sotto forma di rapporto tecnico
- analizzare e presentare lavori scientifici
- selezionare, configurare ed eseguire servizi cloud utilizzando la GUI e l'API di gestione offerte dai provider IaaS
- progettare e configurare infrastrutture scalabili e applicazioni distribuite elastiche.
- fare scelte progettuali che tengano conto dei problemi di sicurezza informatica

Autonomia di giudizio:
Al termine del corso, lo studente:
- sara’ in grado di valutare e confrontare le tecnologie cloud e i servizi cloud, nonché i framework di elaborazione dei big data
- sara’ in grado di identificare, valutare e confrontare soluzioni all'avanguardia
- rafforzera’ la sua capacità di pensiero critico

Abilità comunicative:
Al termine del corso lo studente:
- sara’ in grado di discutere e trasmettere la propria opinione sulle tecnologie cloud
- sara’ grado di presentare l'analisi di un argomento selezionato a un vasto pubblico

Capacità di apprendere:
Durante il corso, lo studente svilupperà e migliorerà la sua capacità di pensiero critico attraverso lo studio e l'analisi di lavori scientifici e di documentazione tecnica. Inoltre, lo studente migliorerà la sua capacità di integrare informazioni da diverse fonti, ad es. libri, documenti tecnici/scientifici ed esperienze pratiche.

Obiettivi formativi

The course will present fundamental technologies for advanced data mining

applications. The course will start with presenting the methodologies for storing

and retrieving information on the Web, mining application logs, mining social

media, collaborative filtering and personalization. The course will also present

the basic technlogies for mining geo-spatial data and data produced from

large-scale sensing environments. Applications will include mining of consumer

preferences, computational advertising, online marketplaces, digital marketing

and mining data in physical spaces. As part of the course students will carry on a

field study on a relevant use case for a selected application.

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General Objectives
Ensuring the privacy of personal data, and securing the computing infrastructures, are key concerns when collecting and analyzing sensitive data sets. Example of these data sets include medical data, personal communication, personal and company-wide financial information. The course is meant to cover an overview of modern techniques aimed at protecting data privacy and security in such applications.

Specific Objectives
The students will learn the basic cryptographic techniques and their application to obtaining privacy of data in several applications, including cloud computing, statistical databases, distributed computation, and cryptocurrencies.

Knowledge and Understanding
-) Modern cryptographic techniques and their limitations.
-) Techniques for achieving privacy in statistical databases.
-) Techniques for designing cryptographic currencies and distributed ledgers.
-) Techniques for secure distributed multiparty computation.

Applying knowledge and understanding:
-) How to select the right cryptographic scheme for a particular application.
-) How to design a differentially private mechanism.
-) How to program a secure cryptosystem, or a secure smart contract, or a secure cryptographic protocol.

Autonomy of Judgment
The students will be able to judge the security of the main cryptographic applications.

Communication Skills
How to describe the security of cryptographic standards, privacy-preserving statistical databases, and blockchains.

Next Study Abilities
The students interested in research will learn what are the main open challenges in the area, and will obtain the necessary background for a deeper study of the subjects.

Obiettivi formativi

Obiettivi generali:
Conoscenza dei principali scenari applicativi di interesse nell'analisi di
collezioni di dati di grandi dimensioni.
Conoscenza e comprensione dei principali problemi metodologici e di analisi posti dalla
dimensione crescente dei dati.
Conoscenza delle principali tecniche di soluzione e dei principali strumenti
a disposizione per implementarle.
Comprensione degli aspetti teorici e fondazionali delle principali tecniche per
l'analisi di collezioni di dati di grandi dimensioni
Capacità di tradurre le nozioni acquisite in programmi per la soluzione di problemi
specifici.
Conoscenza delle principali tecniche di valutazione e loro applicazione a scenari specifici.

Obiettivi specifici:
Capacità di:
- individuare le tecniche più adatte a un problema di analisi di dati di grandi dimensioni;
- implementare la soluzione proposta, individuando gli strumenti più adatti a
raggiungere lo scopo tra quelli disponibili;
- progettare e realizzari scenari sperimentali per valutare le soluzioni proposte
in condizioni realistiche;

Conoscenza e comprensione:
- conoscenza dei principali scenari applicativi;
- conoscenza delle principali tecniche di analisi;
- comprensione dei presupposti teorici e metodologici alla base delle tecniche principali
- conoscenza e comprensione delle principali tecniche e indici di valutazione
delle prestazioni

Applicare conoscenza e comprensione:
- essere in grado di tradurre esigenze applicative in problemi concreti di analisi
dei dati;
- essere in grado di identificare gli aspetti del problema, se presenti, che potrebbero rendere
la dimensione (o dimensionalità) dei dati un fattore critico;
- essere in grado di individuare le tecniche e gli strumenti più adatti alla soluzione dei
problemi concreti di cui sopra;
- essere in grado di stimare a priori, almeno qualitativamente, la scalabilità delle
soluzioni proposte;

Capacità critiche e di giudizio:
Essere in grado di valutare, anche sperimentalmente, l'efficacia, l'efficienza e la scalabilità
delle soluzioni proposte

Capacità comunicative:
Essere in grado di descrivere in modo efficace le specifiche di un
problema e di comunicare ad altri le scelte adottate e le motivazioni sottostanti a
tali scelte.

Capacità di apprendimento:
Il corso consentirà lo sviluppo di capacità di approfondimento autonomo
su argomenti del corso o ad essi correlati. Metterà lo studente nelle condizioni
di individuare e consultare in modo critico manuali avanzati o letteratura scientifica
per affrontare scenari nuovi oppure per applicare tecniche alternative a scenari noti.

Obiettivi formativi

This is a methodological course whose objectives are to make the students able to
represent information distributed over a graph and to solve complex optimization
problems in distributed form. The problems concern the extraction of information from
data distributed across various machines and to make inferences about networked
data. These goals are achieved by first teaching the theoretical foundations
necessary to formulate and solve the above problems and then inviting the students to
write Matlab programs implementing the algorithms solving problems of interest.

Obiettivi formativi

General objectives:
The course will present to students advanced and most recent concepts of machine learning and their application in computer vision via deep neural network (DNN) models. It will include theory and practical coding, as well as a final hands-on project. Towards the coding assignments and the final project, the students will work in teams and present their ideas and project outcome to the class.

Specific objectives
The first part of the course includes delving into state-of-the-art DNN models for classification and regression applied to detection (where the objects are in the image), pose estimation (whether people stand, sit or crunch) and re-identification (estimating a unique vector representation for each person). The course further discusses DNNs for multi-task objectives (joint detection, pose estimation, re-identification, segmentation, depth estimation etc). This first part would include DNNs which apply to video sequences, by leveraging memory (e.g. LSTMs) or attention (Transformers).
The second part of the course delves into models, training techniques and data manipulation for generalization, domain adaptation and meta-learning. Further to transfer learning (how pre-trained models may be deployed for other tasks), it discusses multi-modal (with different sensor modalities such as depth or thermal cameras) and self-supervision (e.g. training the DNN model by solving jigsaw puzzles) to auto-annotate large amounts of data. Finally, it presents domain adaptation (e.g. apply daytime-detectors for night vision) and meta-learning, a most recent framework to learn how to learn a task, e.g. online or from little available data.

Knowledge and understanding:
At the end of the course students will be familiar with state-of-the-art DNN models for multiple tasks and multi-task objectives, as well as generalization and the effective use of labelled and unlabelled data for learning, self-supervision and meta-learning.

Apply knowledge and understanding:
At the end of the course students will have become familiar with the most recent advances in machine learning across a variety of tasks, their adaptation to novel domains and the continual self-learning of algorithms. They will be able to explain the algorithms and choose the most appropriate techniques for a given problem. They will be able to experiment with existing implementations and design and write programs for new solutions for a given task or problem in the two fields.

Critical and judgment skills:
Students will be able to analyse a problem or task and identify the most suitable methodologies and techniques to apply in terms of the effective resolution of the problem (accuracy) and its feasibility, including the efficiency, the required amount of data and annotation. Further to class discussions, critical and judgemental skills would be the result of assignments, a course project and a final project report.

Communication skills:
Students will acquire the ability to expose their knowledge in a clear and organized way, which will be verified through a final project presentation and its discussion.
Students will be able to express their solutions rigorously and to explain the structure of the code they have written.

Learning ability:
The acquired knowledge will enable students to face the study of other problems in machine learning and computer vision. Learning ability would result from the chosen lecture topics, covering most broad areas in advanced machine learning, as well as from the final project, for which students would deep dive into a new topic, beyond the thought material.

Obiettivi formativi

GENERALI
L'obiettivo di questo corso è fornire una panoramica del vasto mondo delle tecnologie wireless e cablate che verranno utilizzate per gli ambienti intelligenti. Queste tecnologie saranno in grado di fornire infrastrutture di rete e piattaforme per l’elaborazione delle informazioni digitali utilizzate in ambienti urbani e in ambienti intelligenti.
I recenti progressi in settori quali quelli dell’edge computing, dell'apprendimento automatico, delle reti wireless e rete di sensori consentono varie applicazioni ambientali intelligenti nella vita di tutti i giorni. L'obiettivo principale di questo corso è presentare e discutere i recenti progressi nell'area dell'Internet of Things, in particolare su tecnologie, architetture, algoritmi e protocolli per ambienti intelligenti con enfasi sulle applicazioni reali di ambienti intelligenti. Il corso presenterà gli aspetti di comunicazione e networking, nonché l'elaborazione dei dati da utilizzare per la progettazione dell'applicazione. Il corso proporrà due casi di studio nel campo degli ambienti intelligenti: monitoraggio del traffico veicolare per applicazioni ITS e reti wireless a basso consumo energetico. In entrambi i casi verranno forniti strumenti, modelli e metodologie per la progettazione di applicazioni per ambienti intelligenti.

SPECIFICI
• Conoscenza e capacità di comprensione: Conoscere i recenti progressi nell'area dell'Internet delle cose, in particolare su tecnologie, architetture, algoritmi e protocolli per ambienti intelligenti con enfasi sulle applicazioni e sulle piattaforme di elaborazione.

• Capacità di applicare conoscenza e comprensione: saper applicare criteri e tecniche di progettazione di piattaforme intelligenti per l’acquisizione dei dati, per la comunicazione in rete e per le applicazioni in contesti di ambienti intelligenti.
• Autonomia di giudizio: saper analizzare benefici e limiti di progetti ambienti intelligenti.
• Abilità comunicative: saper presentare progetti su ambienti intelligenti e di IoT, compresi vincoli progettuali, soluzioni e possibilità d’impiego.
• Capacità di apprendimento: capacità di sviluppare studi più avanzati nell’ambito delle tecnologie di elaborazione e di rete in ambienti intelligenti.

Obiettivi formativi

L'obiettivo del corso è la presentazione di concetti per lo studio di reti sociali e di mercati online. Useremo delle teorie delle aree di algoritmi, statistica, economia e sociologia per studiare la struttura di reti complesse e per presentare modelli che ci permettano di capire come varie entità interagiscono fra di loro e dei fenomeni che si verificano da tale interazioni.

Obiettivi formativi

Devising new machine learning methods and statistical models is a fun and extremely fruitful “art”. But these powerful tools are not useful unless we understand when they work, and when they fail. The main goal of statistical learning theory is thus to study, in a
statistical framework, the properties of learning algorithms mainly in the form of so-called error bounds.
This course introduces the techniques that are used to obtain such results, combining methodology with theoretical foundations and computational aspects. It treats both the basic principles to design successful learning algorithms and the “science” of analyzing an algorithm’s statistical properties and performance guarantees.
Theorems are presented together with practical aspects of methodology and intuition to help students develop tools for selecting appropriate methods and approaches to problems in their own data analyses.
Methods for a wide variety of applied problems will be explored and implemented on open-source software like R (www.r-project.org), Keras (https://keras.io/) and TensorFlow (https://www.tensorflow.org/).
Knowledge and understanding

On successful completion of this course, students will:
know the main learning methodologies and paradigms with their strengths and weakness;
be able to identify a proper learning model for a given problem;
assess the empirical and theoretical performance of different learning models;
know the main platforms, programming languages and solutions to develop effective implementations.

Applying knowledge and understanding

Besides the understanding of theoretical aspects, thanks to applied homeworks and a final project possibly linked to hackathons or other data analysis competitions, the students will constantly be challenged to use and evaluate modern learning techniques and algorithms.

Making judgements

On successful completion of this course, students will develop a positive critical attitude towards the empirical and theoretical evaluation of statistical learning paradigms and techniques.

Communication skills

In preparing the report and oral presentation for the final project, students will learn how to effectively communicate original ideas, experimental results and the principles behind advanced data analytic techniques in written and oral form. They will also understand how to offer constructive critiques on the presentations of their peers.

Learning skills

In this course the students will develop the skills necessary for a successful understanding as well as development of new learning methodologies together with their effective implementation. The goal is of course to grow a active attitude towards continued learning throughout a professional career.

Obiettivi formativi

General Objectives of the course

The general objectives of the course are:
- Present a general framework for the development of quantitative models for economic analysis and management;
- Provide the basic concepts and a guide to analyse the specialised literature;
- Propose a unified framework on the main methodologies available to compare the productivity and efficiency of Decision Making Units (DMUs);
- Introduce to the relevant roles played by the data for the development of effective quantitative models of socio-economic systems;
- Make an introduction to the main softwares available to implement the quantitative models presented during the course;
- Provide laboratory sessions to implement the quantitative models presented during the course in practice;
- Present several applications in the field of economics and management, including public sector services as potential group project works, to be developed by the students according to their personal interest and background;
- Interact with students through seminars, assisted laboratory, oral presentations and the realization of a project work on real data.

Specific objectives of the course
• KNOWLEDGE AND UNDERSTANDING: DEMONSTRATE THE KNOWLEDGE OF THE BASIC METHODS FOR THE DEVELOPMENT OF QUANTITATIVE MODELS FOR ECONOMIC ANALYSIS AND MANAGEMENT ;
• ABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING: TO BE ABLE TO DEVELOP QUANTITATIVE ECONOMIC MODELS ON THE BASE OF THE KNOWLEDGE AND TECHNIQUES LEARNED DURING THE COURSE;
• JUDGMENT AUTONOMY: TO BE ABLE TO DEVELOP A QUANTITATIVE ECONOMIC MODEL WITH CRITICAL SPIRIT, CHOOSING THE APPROPRIATE METHOD AND CORRECTLY IMPLEMENTING IT.
• COMMUNICATION SKILLS: BEING ABLE TO COMMUNICATE THE RESULTS OF THE ANALYSIS AND ITS INFORMATION TO DIFFERENT TYPES OF INTERLOCUTORS;
• LEARNING SKILLS: TO DEVELOP THE NECESSARY SKILLS TO APPLY AND DEVELOP AUTONOMOUSLY THE METHODS AND MODELS LEARNED DURING THE COURSE.

Obiettivi formativi

Knowledge and understanding
The aim of the course is to introduce students to the application of optimization techniques to training problems arising in machine learning. Students are expected to gain insight into standard models in Machine Learning (Deep Networks and Support Vector Machines) and into more recent optimization algorithms for determining the parameters (training) of such models that best fit to the available data.

Applying knowledge and understanding
By the end of the course, students should be able to select the correct model for the problem at hand and either to use standard software specialized to the application and/or to develop their own optimization algorithm.
modelli di apprendimento automatico applicati ai casi studio in apprendimento automatico.

Making judgements
Lectures, practical exercises and project sessions will provide students with the ability to assess the main strengths and weaknesses of the different machine learning models applied to case studies in machine learning.

Communication
By the end of the course, students are able to point out the main features of a machine learning problem and explain techniques for its solution both with a specialized and a non-specialized audience. These abilities are tested and evaluated in the projects developed in small groups thus encouraging team building and a proactive learning process coupling with collaborative learning. These abilities can also be checked in the final oral exam.

Lifelong learning skills
Students are expected to develop those learning skills necessary to undertake additional studies on the relevant topics with a high degree of autonomy. During the classes, students are encouraged to work on projects into small groups thus stimulating student activity and engagement. They are pushed to consult supplementary research publications and internet sites to exploit tricks and detailed choices needed to accomplish the tasks effectively. These capabilities are tested and evaluated in the development of the final reports of the projects where students have to discuss the main issues of the addressed problems and their choices to overcome the difficulties, based on the topics and material covered in class.

Obiettivi formativi

What subset of Big Data can be used in the ambit of Official Statistics and what domains of Official Statistics can be enriched through the availability of new data sources.
How new data sources can be used in Official Statistics, by taking into account challenges, needs and risks in this exercise.

Definition of the role of Big Data in the context of Official Statistics.
How to frame the measurement of social, demographic and economic phenomena through Big Data by considering challenges, needs and risks.

Obiettivi formativi

The module aims at providing a general background on the remote sensing

systems for Earth Observation from space‐borne platforms and on data

processing techniques. It describes, using a system approach, the characteristics

of the system to be specified to fulfil the final user requirements in different

domains of application. Remote sensing basics and simple wave‐interaction

models useful for data interpretation are reviewed together with technical

principles of the main remote sensors. The course also provides an overview

of the most important applications and bio‐geophysical parameters (of the

atmosphere, the ocean and the land) which can be retrieved. The most important

techniques for data processing and product generation, also by proposing

practical exercises using the computer, are analysed together with an overview

of the main Earth Observation satellite missions and the products they provide to

the final user.

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A first objective of the course is to provide a basic toolbox for the analysis of agent
and group interaction under uncertainty and asymmetric information, and its main
consequences on markets enabled from large-scale digital platforms.
A second objective of the course will be to provide the basic methods
for the use of big data for estimating relevant economic indices.
The active participation of students will be stimulated with game-theoretic
examples, presentations, simple experiments, case-studies and projects
involving the use of real-world data.

Obiettivi formativi

This course has the target of providing the students with the modern techniques of measuring quantitatively advanced topics in economic statistics. In particular our focus will be on three main interrelated directions: 1) the analysis of production and efficiency, specifically in the private but also in the public sectors, 2) economic dynamics of sectorial systems founded on micro data, 3) growth, ICT and technology in the modern economy.
This course uses statistical methods, both stochastic and deterministic, to analyze topics such as productivity, efficiency and growth at micro, sectorial, and for coherence at macro level. We first take into exam data from firms that will be useful for the mentioned three-levels study, then, as regards the efficiency analysis of productive units, such data will be employed in order to evaluate mergers and acquisitions of plants and firms and management of productive factors. Efficiency will be evaluated from the sides of costs, profits and revenues. As for the sectorial analysis, static and dynamic models will be considered to allow for forecasts and simulations in each sector for variables like production, labour, capital, raw materials, prices and capital gains. As a consequence, an aggregate analysis on the production, growth and prices will follow. We also deal with ICT and technical progress in the production process considering how and if the associated externalities are effective. We will use the following techniques for data analysis: accounting rules for the database, panel data econometrics, time series analysis for systems of equations, methods for differential equation systems. Topics on private and also public sectors will contribute to explain the relationship between economic structure and the actual crisis. Specifically, lectures also include the examinations of cases study concerning the efficiency and productivity analysis on the recent patterns of the banking sector in the international context.

Obiettivi formativi

Il corso introduce le reti neurali nel contesto delle applicazioni legate al mondo della data science. Dopo un riepilogo dei concetti teorici di base, verranno introdotte alcune delle tecniche ed algoritmi sviluppati negli ultimi anni sotto il nome di “deep learning”, che hanno permesso di applicare con successo le reti neurali a problemi come la computer vision ed il natural language processing. A tal fine saranno introdotte prima le reti convolutive (per l’analisi di immagini), ed in seguito le reti ricorrenti per processare sequenze di dati ed affrontare problemi cosiddetti di ‘sequence to sequence’ (es., traduzione automatica). Ciascun argomento del corso sarà seguito da un laboratorio pratico in cui i concetti saranno sviluppati su casi d’uso concreti tramite l’utilizzo della libreria TensorFlow, rilasciata da Google in open source due anni fa.

Risultati di apprendimento attesi:
Gli studenti acquisiranno la capacità di sviluppare ed implementare tecniche di apprendimento automatiche (basate sulle reti neurali) per numerosi scenari applicativi, fra cui il riconoscimento di immagini e l’analisi del testo). Inoltre, gli studenti acquisiranno i concetti chiave che gli permetteranno di avvicinarsi a sotto-campi più avanzati in autonomia, come la neural machine translation. A livello pratico, gli studenti impareranno ad utilizzare le più recenti librerie per il deep learning, in particolare con l’utilizzo di TensorFlow (rilasciata da Google).

Obiettivi formativi

The course finds its motivation in the great availability and relevance of geospatial data (in particular big data), and it aims to provide the fundamentals on the main methodologies and techniques currently available for their acquisition, verification, analysis, storage and sharing.
In fact, the vast majority (a percentage close to 80%) of the currently available data has a geographical connotation, is intrinsically linked to a position; they are therefore named geospatial data. Furthermore, the ever-increasing availability of sensors capable of acquiring geospatial data, allowing the acquisition of larger and larger amounts of data, raises several important issues related to the correct, efficient and effective use of these geospatial big data.
Special attention is given to data coming from Global Navigation Satellite Systems (GNSS), Photogrammetry and Remote Sensing, Volunteered Geographic Information (VGI) and crowdsourcing, both regarding their analysis and management with freely available software and cloud-based platforms for planetary-scale environmental data analysis (Google Earth Engine).

Knowledge and understanding
Students who have passed the exam will know the fundamentals on the main methodologies and techniques currently available for geospatial data acquisition, verification, analysis, storage and sharing, with focus on Global Navigation Satellite Systems (GNSS), Photogrammetry and Remote Sensing, and cloud-based platforms for planetary-scale environmental data analysis (Google Earth Engine), being also aware of the relevant resources represented by Volunteered Geographic Information (VGI) and crowdsourcing

Applying knowledge and understanding
Students who have passed the exam will be able to plan and manage the acquisition, verification, analysis, storage and sharing of geospatial data necessary to solve interdisciplinary problems, using Global Navigation Satellite Systems (GNSS), Photogrammetry and Remote Sensing, and cloud-based platforms for planetary-scale environmental data analysis (Google Earth Engine), being also aware of the relevant additional contributions which can be supplied by Volunteered Geographic Information (VGI) and crowdsourcing

Making judgment
Students will acquire autonomy of judgment thanks to the skills developed during the execution of the numerical and practical exercises that will be proposed on three main topics of the course (Global Navigation Satellite Systems, Photogrammetry and Remote Sensing, Google Earth Engine)

Learning skills
The acquisition of basic methodological skills on the topics covered, together with state-of-the-art operational skills, favors the development of autonomous learning skills by the student, allowing continuous, autonomous and thorough updating.

Obiettivi formativi

General objectives. The general objectives of the course are: i) to provide students with a hands-on experience with basic biological concepts and common bioinformatics tools and databases; ii) to introduce students to the on-the-field application of networks in biology and medicine.
Specific objectives. Students are expected to acquire basic biology knowledge and skills, to understand the role of networks in the study of physiological mechanisms and diseases; to understand how to use network medicine algorithms and procedures.
Knowledge and understanding. The course will include theory and hands-on projects. Students will be trained in the basic theory and application of programs used for database searching, biological network inference and analysis.
Apply knowledge and understanding. At the end of the course students will have become familiar with basic biological concepts and bioinformatics databases and tools. Furthermore, on successful completion of this course, students will understand the use of networks as a paradigm for disease expression and course.
Critical and judgment skills. At the end of the course, students will be able to critically analyse the results of their analysis.
Communication skills. The students will be required to produce reports describing the hands-on projects with specific sections for the description of the obtained results and their discussion.
Learning ability. The projects will be developed in small groups encouraging team building. All the acquired abilities will be checked in a final oral exam during which a good division of teamwork will be rewarded.

Obiettivi formativi

General objectives. Digital data sources and digital traces of human behaviour have the potential to provide local and timely information about disease and health dynamics at the population level. The general aim of the course is to introduce students to the analysis of epidemiological and omics data and to the use of computational approaches for medical/clinical purposes.
Specific objectives. The course consists of two modules. The first module will deal with the opportunities and challenges of mining digital data sources for epidemiological and public health signals and will provide an overview of the state of the art of this emerging field. The second module will focus on “precision medicine”, an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person. With the second module, the students are expected to acquire basic biology knowledge and skills and to become familiar with the analysis and integration of omics data.
Knowledge and understanding. The course will include theory and hands-on lectures. Students will be trained in the basic theory for the identification of gene interactions and in the use of network science.
Apply knowledge and understanding. At the end of the course students will have become familiar with basic biological concepts, with the analysis of omics and epidemiological data and with the use of networks for the investigation of infectious disease dynamics and disease etiology, diagnosis, and treatment.
Critical and judgment skills. At the end of the course, students will be able to critically analyse the results of their analysis.
Communication skills. The students will be required to produce reports describing hands-on projects with specific sections for the description of the obtained results and their discussion.
Learning ability. The projects will be developed in small groups encouraging team building.