Data Science

Educational objectives

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.

Educational objectives

The aim of the course is to introduce students to the application of optimization techniques for training machine learning problems. Students are expected to acquire knowledge about standard models used in machine learning (Deep Networks and Support Vector Machines), understand which model is most appropriate to use in each context, and learn about the latest optimization algorithms for determining the parameters (training) of these models that best fit the available data.

Educational objectives

GENERAL DESCRIPTION
The goal of this course is to provide an overview of stochastic processes, with applications to Data Science in
mind. Stochastic processes and probability are important in data science because they can be used to model
and analyze a wide range of data sets, from financial data to sensor data. The course will cover three parts: a
gentle introduction to combinatorial stochastic processes, Gaussian processes, and probabilistic causality.
Programming in R, Matlab or Python is useful, but it is not essential. Programs in R will be used.
SPECIFIC OBJECTIVES:
1. Knowledge and understanding: Understand the basics of combinatorial stochastic processes and
Gaussian processes, and their applications in data science. Understand the fundamentals of
probabilistic causality and be able to apply these concepts to real-world data science problems.
2. Application: Apply stochastic process to real-world data sets, using programming languages such as R,
Matlab, or Python.
3. Autonomy of judgement: Analyze the benefits and limitations of different stochastic process models
and determine the best model to use for a given data set.
4. Communication: Communicate effectively about stochastic processes, including design constraints,
solutions, and potential applications.
5. Learning skills: Develop studies in the field of stochastic processes for data science, including the ability
to undertake research in this area.

Educational objectives

General Objectives:

The purpose of the course is to give students the basic concepts of distributed systems and then to focus on cloud computing technologies. The course cover theoretical and practical aspects with a focus on real examples. At the end of the course students are supposed to be capable to chose, setup and use cloud services and to design and deploy scalable architectures and elastic applications.

Specific Objectives:

Knowledge and understanding:
On completion of the course, the student will be be able to describe and to explain
- the general concepts related to distributed systems
- the concepts of system and application virtualization
- the mechanisms and algorithms used in cloud computing
- the technologies for cloud storage
- the big data processing frameworks
- the cyber security issues and solutions in cloud computing

Applying knowledge and understanding:
On completion of the course, the student will be able:
- to design and to implement a scalable architecture and to deploy an elastic application
- to write and to present practical results in the form of technical report
- to analyze and to present scientific work
- to select, to configure and to run cloud services by using management GUI and API offered by IaaS providers
- to design and to configure elastic infrastructure and to deploy elastic applications.
- to make design choices that account for cyber security issues

Making judgements:
On completion of the course, the student will:
- be capable to assess and to compare cloud technologies and cloud services, as well as big data processing frameworks
- be capable to identify, to assess and to compare state of the art solutions
- strengthen his/her critical thinking ability

Communication skills:
On completion of the course, the student will:
- be capable to discuss on and to convey his/her own opinion on cloud technologies
- be capable to present the analysis of a selected topic to a wide audience

Learning skills:
During the course, the student will develop and will enhance his/her critical thinking skill by means of studying and analyzing scientific work and technical documentation. Moreover, the student will improve his/her capability to integrate information from different sources, e.g. books, technical/scientific papers, practical experiences.

Educational objectives

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.

Educational objectives

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 technologies for classification and learning, with emphasis on textual data sources.
Applications will include mining of consumer preferences, online marketplaces, digital marketing
and Natural Language Processing applications such as sentiment analysis. As part of the course students will carry on a
field study on a relevant use case for a selected application.

Educational objectives

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.

Educational objectives

General
This course aims to exploit advanced techniques from network science and complex systems to understand and eventually predict social-relevant issues (information diffusion, mobility, etc.).
The course aims to design efficient strategies to extract knowledge from data through the complex systems approach by stressing the combination of network science and complex systems to build sound mathematical models of complex phenomena.
The course will introduce advanced topics of networks science and diffusion models and address the data-driven modeling of complex socio-technical systems (e.g., misinformation diffusion, echo chambers formation, bot detection, mobility patterns, system resilience).
The first part of the course will explore the foundational aspects of advanced topics of complex networks (multilayer networks, percolation theory, time-varying graphs). The second part will apply those concepts to actual cases from up-to-date scientific findings ranging from the effect of feed algorithms on social dynamics to patterns of human mobility, passing through information operations, and bot detection.
We will use data from real case scenarios (from Facebook, Twitter, Mobility Data, etc.) to analyze phenomena and build and validate models of complex phenomena.

Specific
• Knowledge and understanding: To know and discuss recent advances in the area of data-driven modeling of complex systems, in particular on algorithms and models to understand and eventually predict social dynamics (e.g., information diffusion, polarization)
• Applying knowledge and understanding: to know how to apply criteria and techniques for designing a data analysis framework exploiting the theory of complex systems.
• Making judgments: to select the most appropriate strategy to cope with the data-driven modeling of complex phenomena
• Communication skills: know how to present projects, including design constraints, solutions, and use possibilities.
• Learning skills: ability to develop more advanced studies in data-driven modeling of complex systems.

Educational objectives

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.

Educational objectives

Eng
Objectives
The goal of the course is to teach basic methodologies of signal processing and to show their
application to machine learning and data science. The methods include: (i) Standard tools for
processing time series and images, such as frequency analysis, filtering, and sampling; (ii) Sparse and
low-rank data models with applications to high-dimensional data processing (e.g., sparse recovery
matrix factorization, tensor completion); (iii) Graph signal processing tools, suitable to analyze and
process data defined over non-metric space domains (e.g., graphs, hypergraphs, topologies, etc.) with
the aim of performing graph machine learning tasks such as graph filtering, spectral clustering,
topology inference from data, and graph neural networks. Finally, it is shown how to formulate and
solve machine learning problems in distributed fashion, suitable for big data applications, where
learning and data processing must be necessarily performed over multiple machines. Homeworks
and exercises on real-world data will be carried out using Python and/or Matlab.
Specific Objectives:
1. Knowledge and understanding: Learn the basics of signal processing for machine learning and
be able to apply these concepts to real data science problems.
2. Application: Apply signal processing and machine learning techniques to real-world data sets,
using programming languages such as Python and Matlab.
3. Autonomy of judgement: Analyze the benefits and limitations of different signal processing
tools and models and determine the best methodology to use for a given data set.
4. Communication: Communicate effectively about signal processing for machine learning,
including design constraints, solutions, and potential applications.
5. Learning skills: Develop studies in the field of signal processing for machine learning,
including the ability to undertake research in this area.

Educational objectives

General Objectives:
Knowledge of main application scenarios in Big Data Computing.
Knowledge and understanding of main algorithms and approaches in Big Data Computing. Knowledge of
main tools to implement them.
Understanding of theoretical foundations underlying main techniques of analysis
Ability to implement the aforementioned algorithms, approaches and techniques and to
apply them to specific problems and scenarios.
Knowledge of main evaluation techniques and their application to practical scenarios.

Specific objectives:
Ability to:
- identify the most suitable techniques to address a data analysis problem where
data dimensionality is concern;
- implement the proposed solution, identifying the most appropriate design and
implementation tools, among available ones;
- Design and implement experiments to evaluate proposed solutions in realistic settings;

Knowledge and understanding:
- knowledge of main application scenarios;
- knowledge of main techniques of analysis;
- understanding of methodological and theoretical foundations of main analysis techniques;
- knowledge and understanding of main evalutation techniques and corresponding
performance indices

Apply knowledge and understanding:
- being able to translate application needs into specific data analysis
problems;
- being able to identify aspects of the problem for which data dimensionality
might play a critical role;
- being able to identify the most suitable techniques and tools to addresse the
aforementioned problems;
- being able to estimate in advance, at least qualitatively, the degree of scalability
of proposed solutions;

Critical and judgment skills:
Being able to evaluate, also experimentally, the effectiveness and efficiency of
proposed solutions

Communication skills:
Being able to effectively describe the requirements of a problem and
provide to third parties the relative specifications, design choices and
the reasons underlying these choices.

Learning ability:
The course will facilitate the development of skills for the independent
study of topics related to the course. It will also allow students to identify
and critically examine material contained in andvanced manuals and/or scientific
literature, allowing them to face new application scenarios and/or apply alternative
techniques to known ones.

Educational objectives

GENERAL
Goal of this course is to provide an overview of the large world of wireless and wired technologies that are will be used for the Smart Environments. These technologies will be able to provide infrastructures of networks and digital information used in the urban spaces and smart environments to build advanced applications.
Recent advances in areas like pervasive computing, machine learning, wireless and sensor networking enable various smart environment applications in everyday life. The main goal of this course is to present and discuss recent advances in the area of the Internet of Things, in particular on technologies, architectures, algorithms and protocols for smart environments with emphasis on real smart environment applications. The course will present the communication and networking aspects as well as the processing of data to be used for the application design. The course will propose two cases studies in the field of smart environments: Vehicular Traffic monitoring for ITS applications and Low Power Area Networks. In both cases instruments, models and methodologies for the design of smart environments applications will be provided.

SPECIFIC
• Knowledge and understanding: To know and discuss recent advances in the area of the Internet of Things, in particular on technologies, architectures, algorithms and protocols for smart environments with emphasis on real smart environment applications and processing tools.
• Applying knowledge and understanding: to know how to apply criteria and techniques for designing a smart platform from the data acquisition, the networking and the application.
• Making judgements: to know how to analyze benefits and limitations of smart environments and relevant applications.
• Communication skills: know how to present projects on smart environments and and IoT including design constraints, solutions and possibilities of use.
• Learning skills: ability to develop more advanced studies in the field of ambient intelligence.

Educational objectives

INGLESE:

General outcomes:

The course will present a broad survey of topics at the interface of computer
science, data science, and economics, emphasizing efficiency, robustness, and application to emerging online markets. It will introduce the principles of algorithmic game theory and mechanism design, algorithmic market design, as well as machine learning in games and markets. It will demonstrate applications to case studies in Web search and advertising, network economics, Data, cryptocurrency, and AI markets.

Specific outcomes:
Knowledge and understanding:
The algorithmic and mathematical economics principles underlying the design and the operation of efficient and robust online markets. The application of these principles in concrete examples of online markets.

Applying knowledge and understanding:
Being able to design and analyze algorithms for concrete online market applications with respect to the requirements of efficiency and robustness.

Making judgements:
Being able to evaluate the quality of an algorithm for online market applications, discriminating the modeling aspects from those related to algorithmic and system implementation.

Communication skills:
Ability to communicate and share the modeling choices and system requirements, as well as the results of the analysis of the efficiency of online market algorithms.

Learning skills:
The course stimulates the students to acquire learning skills at the crossroads of computer science, economics, and digital market applications, including the different languages used in these fields.

Educational objectives

Risultati di apprendimento attesi:
Graphs have applications in multiple areas, including social networks, bioinformatics, network medicine, computational chemistry, and they can be used to provide tools in these areas.

The course will present models and algorithms for the analysis of graphs as with applications on various areas. The goal at the end of the course, is for student to know algorithms and frameworks that can allow them to analyze large graph data.

Informazioni sui prerequisiti culturali e curriculari necessari
- Knowledge of basic algorithms
- Programming
- Linear algebra
- Probability
- Neural networks

Programma in italiano
• Theoretical algorithms for graph modeling and analysis:
◦ Real graph properties and models (Gnp, preferential attachment, Kleinberg’s reachability)
◦ Models for propagation (linear threshold, cascade) and for opinion formation
◦ Homophily and influence and algorithms for identifying and distinguishing
◦ Influence maximization
◦ Algorithms for graph alignment
◦ Dense subgraphs, community detection, graph minors
◦ Graph summarization and sampling
• Machine-learning approaches:
◦ Label propagation
◦ Graph transformers
◦ Knowledge-graph emdeddings
◦ Models for analysis of temporal graphs
◦ Explainability
• Architectures for handling large graph data:
◦ Spark GraphsX
◦ AWS Neptune
◦ AWS GraphStorm
◦ Neo4J

Modalità di valutazione delle conoscenze
Prova scritta
Prova orale
Valutazione progetto

Modalità di valutazione in italiano
Homeworks and/or project and oral exam or written exam

Esempi di domande e/o esercizi frequenti
Find the most influential nodes in a network.

Testi adottati
Material will be distributed online

Modalità di svolgimento
Didattica frontale/tradizionale

Modalità di svolgimento in italiano
The course is based on in-class theoretical lectures and sometimes in-class labs.

Modalità di frequenza
Classes are in person.

Programmazione:
http://aris.me/index.php/teaching

Educational objectives

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.

Educational objectives

1) Knowledge and understanding
During the lectures of Data-driven Economics, students acquire the basic theoretical elements of
econometric analysis. Theoretical lectures are aimed at guiding students in the acquisition of the basics
of simple and multiple regression models, starting from the relative assumptions, and then proceeding
with the estimation and inference procedures. The course contents cover both the estimation of linear
and non-linear models and the analysis of both cross-sectional and longitudinal data.
2) Applying knowledge and understanding
The students of the Data-driven Economics course are able to apply the notions acquired during the
theoretical lectures to a wide range of problems of an empirical nature. They acquire the ability to build
econometric models aimed at giving empirical content to economic relations and are also able to
establish a causal link between two or more variables in the economic field.
3) Making judgements
Students are encouraged to critically discuss empirical studies published in the economic/managerial
field in the classroom. The Data-driven Economics course also includes a laboratory in which students
apply the acquired knowledge of econometrics to the estimation of empirical models carried out using
data made available by the teacher.
4) Communication skills
At the end of the course, students are able to illustrate and explain the strengths and weaknesses of a
wide range of empirical methodologies to a variety of heterogeneous interlocutors in terms of training
and professional role. The acquisition of these skills is verified and evaluated not only during the final
exam, by means of a written test and a possible oral test, but also during flipped class sessions in which,
individually or in groups, students are called to present empirical studies published in the
economic/managerial field.
5) Learning skills
Students acquire the ability to independently conduct empirical analyses by building econometric
models to be estimated using data with diversified structures. The tools provided by the course allow
for the analysis of systems in which a large number of factors simultaneously contribute to explaining
their states and impact assessments that take into account the uncertainty and risk inherent in the
application of policies. The acquisition of these skills is verified and evaluated during the final exam, by
means a written test and a possible oral test, in which the student can be called to discuss empirical
problems on the basis of the topics covered and the reference material distributed during the course.

Educational objectives

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.

Educational objectives

General objectives: The course provides an overview on the use of deep neural networks in the context of data science and data science applications. The course is split into a methodological part (introducing basic concepts and tools for building neural networks), and a practical part with several hands-on coding sessions, followed by one homework, one final project, and an oral examination.
Specific objectives: The first part of the course will (briefly) reintroduce the mathematical skills required for the course, including linear algebra and numerical optimization. Then, we will survey basic neural network components ranging from linear models to fully-connected ones layers. We will then move to a selection of advanced models (convolutive networks, transformers, graph neural networks, autoregressive models), and a series of selected advanced topics (fairness, robustness, deployment of the models).
Knowledge and understanding: At the end of the course, the students will have a broad knowledge of state-of-the-art tools and techniques for implementing deep neural networks in several fields, as long as practical hands-on ability to translate conceptual designs into practical coding.
Critical and judgment skills: The students will learn to tackle a complex data science project, decomposing it into blocks that are solvable through one or more neural network models.
Communication skills: The students will learn to effectively communicate their knowledge along three major axes, (i) via suitably describing their final projects with a final report, (ii) orally for the final exam, and (iii) through careful code documentation and restructuring.
Learning ability: The students will be able to autonomously read and reimplement state-of-the-art papers and models going beyond the basic topics of the course, thanks to a selection of papers and tools that will be discussed during the lectures.

Educational objectives

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.

Educational objectives

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.

Educational objectives

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.

Educational objectives

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.

Educational objectives

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.

Educational objectives

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.

Educational objectives

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.