The broad diversity of the labs and institutions involved in the Data Science Ph.D. program is reflected in the list of courses available.
The list below is only the list of suggested courses, but the students can select their courses from the even broader list of courses available from all the institutions involved.
This list is about the courses provided in Academic Year 2018-2019. The list of courses for Academic Year 2020-2021 is available here
Advanced Methods for Complex Systems I
Provided by:
IMT
From:
PhD in Economics, Networks and Business Analytics (ENBA)
Sede:
IMT Lucca
Lecturers:
Diego GARLASCHELLI
Semester:
2
Hours:
20
Exam:
Y
Educational Goals:
This interdisciplinary course aims at introducing rigorous tools from statistical physics, information theory and probability theory to investigate real-world complex systems arising in different fields of research. First, some key aspects of complexity encountered in physical, biological, social, economic and technological systems will be reviewed. Then, emphasis will be put on the construction of theoretical models based on the concept of constrained randomness, i.e. the maximisation of the entropy subject to suitable constraints. This will lead to the introduction of maximum-entropy models that serve as mathematical benchmarks for the properties of highly heterogeneous complex systems. Special cases of interest include statistical ensembles of complex networks and of multivariate time-series with given properties. Comparisons between model outcomes and empirical properties will be presented systematically. Full mathematical derivations of the models, as well as methods of statistical inference, model selection and computer codes for parameter estimation on empirical data will be provided.
The course will include a combination of recent and ongoing research in the NETWORKS unit at IMT Lucca, thereby offering directions for possible PhD projects in this area.
Prerequisites:
Solid mathematical background, scientific curiosity, interest in multidisciplinarity, passion for theory.
Advanced Methods for Complex Systems II
Provided by:
IMT
From:
PhD in Economics, Networks and Business Analytics (ENBA)
Lecturers:
Diego GARLASCHELLI
Semester:
2
Hours:
20
Exam:
Y
Educational Goals:
The second part of the course ÒAdvanced Methods for Complex SystemsÓ focuses on advanced practical applications of the concepts introduced in the first part. In particular, emphasis will be put on the successful areas of pattern detection and network reconstruction from partial information. Network pattern detection is the identification of robust empirical patterns (like scale invariance, clustering, assortatitvity, reciprocity, motifs, etc.) that are widespread across real-world networks and that deviate systematically from some null hypothesis formalised in terms of a suitable random graph model. The models introduced in part I will then be used here for pattern detection purposes. The problem of community detection will also be covered, with an emphasis on the differences between finding communities in network data and in correlation matrices constructed from (e.g. financial or neural) time series databases. The problem of network reconstruction from partial topological information will be addressed concentrating on the reconstruction of financial and interbank networks from node-specific properties, with the purpose of improving stress tests and systemic risk estimates in real markets and offering better tools to policy makers. The statistical physics methods recently found by central banks to be the best-performing reconstruction techniques will be reviewed in detail.
The course will include a combination of recent and ongoing research in the NETWORKS unit at IMT Lucca, thereby offering directions for possible PhD projects in this area.
Prerequisites:
Solid mathematical background, scientific curiosity, interest in multidisciplinarity, successful completion of the course ÒAdvanced Methods for Complex Systems IÓ.
Note: completion of this second part of the course is not required in order to move on to the third part (parts II and III can be understood in parallel independently of each other, after part I is completed), although it would surely provide a useful overview of practical motivations for part III.
Advanced Methods for Complex Systems III
Provided by:
IMT
From:
PhD in Economics, Networks and Business Analytics (ENBA)
Lecturers:
Diego GARLASCHELLI
Semester:
2
Hours:
20
Exam:
Y
Educational Goals:
The third part of the course ÒAdvanced Methods for Complex SystemsÓ focuses heavily on deeper theoretical aspects and their consequences. Particular emphasis will be put on the distinction between maximum-entropy models of complex systems with ÒsoftÓ and ÒhardÓ properties. In statistical physics, the resulting models are known as the ÒcanonicalÓ and ÒmicrocanonicalÓ ensembles respectively. Many of the results in statistical physics (e.g. the calculation of certain entropies), discrete mathematics (e.g. the combinatorial enumeration of possible configurations of a system with given properties), and information theory (e.g. the calculation of the maximum compressibility of information sequences) rely of the concept of Òensemble equivalenceÓ, i.e. the asymptotic equivalence of soft and hard ensembles in the large size limit. Surprisingly, various complex systems have been found to violate the property of ensemble equivalence. For these systems, the standard approach is not appropriate and new developments are needed. Several intriguing challenges open up, including the uniform sampling of realisations of large complex systems, the combinatorial enumeration of systems with heterogeneous constraints and the recalculation of traditional information-theoretic bounds on communication. Examples of these open challenges will be provided, along with tentative solutions that are underway.
The course will include a combination of recent and ongoing research in the NETWORKS unit at IMT Lucca, thereby offering directions for possible PhD projects in this area.
Prerequisites:
Unlimited passion for theory and multidisciplinarity, successful completion of the course ÒAdvanced Methods for Complex Systems IÓ. Note: knowledge of the content of the course ÒAdvanced Methods for Complex Systems IIÓ is not required (parts II and III can be understood in parallel independently of each other, after part I is completed), although it would surely provide a useful overview of practical motivations for this part.
Agent Based Macroeconomics
Provided by:
S.ANNA
From:
Altro PhD (Economics)
Lecturers:
Andrea ROVENTINI
Semester:
2
Hours:
18
Exam:
Y
Educational Goals:
The course provides an overview of agent-based macroeconomics. After having discussed the limit of standard Dynamic Stochastic General Equilibrium (DSGE) models, the agent-based computational economic approach is presented, stressing how it can be employed for economic policy. Different agent-based models are then introduced (i) to stress the role of agent heterogeneity and interactions for the emergence of endogenous growth; (ii) to study how the economy self-organize after business-cycle shocks; (ii) to analyse the joint impact of monetary and macro-prudential policies; (iv) to assess how endogenous growth, catching-up and divergence emerge in an open-economy, multy-country framework. Finally, the family of the Schumpeter meeting Keynes (K+S) models is presented, stressing its capability to jointly account for endogenous growth and fluctuations and macro and micro stylized facts. The K+S model is employed as a laboratory to study the short-and long-run impact of different ensembles of innovation, industrial, monetary, fiscal, monetary, labour market climate policies. Syllabus: (1) From DSGE models to macroeconomic ABMS, (2) Exploring the role of heterogeneity and interactions with ÒsimpleÓ macro ABMs. (3) Topics in agent-based macroeconomics. (4) The family of Schumpeter meeting Keynes models.
Agent Based Modeling
Provided by:
S.ANNA
From:
Altro PhD (Economics)
Lecturers:
Marco VALENTE
Semester:
2
Hours:
18
Exam:
N
Educational Goals:
This course is intended to serve as a broad introduction to the huge literature using agent-based computational approaches to the study of economic dynamics. It is organized in three parts. The first one (ÒWhy?Ó) will discuss the roots of the critiques to the mainstream paradigm from a methodological, empirical and experimental perspective. We shall briefly review the building blocks of mainstream models (rationality, equilibrium, interactions, etc.) and shortly present some of the evidence coming from cognitive psychology and experimental economics, network theory and empirical studies, supporting the idea that bounded rationality, non-trivial interactions, non-equilibrium dynamics, heterogeneity, etc. are irreducible features of modern economies. In the second part (ÒWhat?Ó) we shall discuss what ACE is and what are its main tools of analysis. We will define an ABM and present many examples of classes of ABMS, from the simplest (cellular automata, evolutionary games) to the most complicated ones (micro-founded macro models). The third part (ÒHow?Ó) aims at understanding how ABMs can be designed, implemented and statistically analyzed. We shall briefly present the basics of programming, by both discussing the pros and cons of using simulation platforms (Matlab, NetLogo, Swarm, LSD, etc.) vs. computer languages (Java, C++, etc.) and providing some simple Òhands-onÓ applications to cellular automata. Finally, we will see how the outputs of ABMs simulation should be treated from a statistical point of view (e.g., Montecarlo techniques) and we will discuss two hot topics in ABM research: empirical validation and policy analysis. Syllabus: Part I. Why? Why Agent-Based Computational Economics (ACE) and Agent-Based Models (ABMs)? Empirical and theoretical underpinnings. Part II. What? The structure of ABMs; Flexibility of ABMs; Examples. Part III: How? Designing and implementing ABMS; Statistical analysis of ABMs; Applications. Part IV: Selected Topics in ACE; Empirical validation; Macroeconomic policy; Object-oriented programming.
Algorithm Accountability
Provided by:
S.ANNA
Sede:
S. Anna
Lecturers:
Giovanni COMANDE, G. MALGIERI
Semester:
2
Hours:
10
Exam:
Y
Programme:
Defining and Justifying Accountability. Accountability in the Machine Learning Context. Algorithm Transparency. Transparency and the Marketplace/ Competition Law. Methods of Transparency. Technical and Legal Options to Enhance Transparency & Accountability. People Analytics. Behavioural ÒNudgingÓ. New Emerging Human Rights in the age of Behavioral Data Science and Neurotechnologies: Towards "Mental Privacy" and "Decision Integrity". Legal and ethical implication of computational capacity. Machine Learning. GDPR Solutions: The Right to an Explanation, etc.
Analytics in Economics and Management
Provided by:
IMT
From:
Altro PhD (Institutions, Markets and Technologies)
Sede:
IMT
Lecturers:
Massimo RICCABONI
Semester:
2
Hours:
20
Exam:
Y
Educational Goals:
The aim of this course is to teach students how to produce a research paper in economics and management using hands-on supervised machine learning tools for different data structures. We will bridge the gap between applications of methods in published papers and practical lessons for producing your own research. After introductions to up-to-date illustrative contributions to literature, students will be asked to perform their own analyses and comment results after applications to microdata provided during the course. The objective is to develop a critical understanding of the iterative research process leading from real economic data to the choice of the best tools available from the analyst kit. Final scores will be based 50% on individual presentations of a selected supplemental reading and 50% on an individual homework.
Applied methods: Causal inference in Microeconometrics
Provided by:
S.ANNA
From:
Altro PhD (Economics)
Sede:
Nizza
Lecturers:
Manule BAGUES, Mauro SYLOS LABINI
Semester:
2
Hours:
20
Exam:
N
Educational Goals:
The course introduces PhD students to the main empirical strategies for causal inference. It has a practical flavor; emphasis is not on proofs but on intuitions and on applications. Basic knowledge of Econometrics and Statistics is assumed. Software used is STATA. Outline: 1. Introduction, 2. Randomized Control Trials, 3. Instrumental variables, 4. Identification based on observables, 5. Differences-in- differences, 6. Regression discontinuity designs, 7. Non-standard standard error issues, 8. Introduction to Structural analysis
Basic Principles and Applications of Brain Imaging Methodologies to Neuroscience
Provided by:
IMT
From:
Altro PhD (Institutions, Markets and Technologies)
Lecturers:
Emiliano RICCIARDI, Monica BETTA Simone ROSSI, Luca CECCHETTI
Hours:
64
Educational Goals:
The course aims at introducing the fundamentals of brain metabolism and brain imaging methodologies. Neuroimaging techniques provided cognitive and social neuroscience with an unprecedented tool to investigate the neural correlates of behavior and mental functions. Here we will review the basic principles, research and clinical applications of positron emission tomography (PET), functional magnetic resonance imaging (fMRI), electroencephalography (EEG) and magnetoencephalography (MEG), non-invasive brain stimulation tools. Solid background in the concepts common to many types of neuroimaging, ranging from study design to data processing and interpretation, will be discussed to address neuroscientific questions. In particular, we will first review the basics of neurophysiology to understand the principles of brain imaging. Then, methodologies of data processing for the main brain imaging tools will be provided to the students with hands-on sessions: students will become familiar with the main pipelines for PET, fMRI and EEG data reconstruction, realignment, spatio-temporal normalization, first and second-level analyses. At the end of the course, students are expected to have general background knowledge of the basic principles, methodologies and applications of the most important brain functional techniques and to be prepared to evaluate the applicability of, and the results provided by, these methodologies for different problems in cognitive and clinical neuroscience.
Basics of databases, exploratory data analysis and programming
Provided by:
UNIPI, CNR
From:
Master II Livello Big Data Analytics
Sede:
Computer Science Department, Unversity of Pisa
Lecturers:
Anna MONREALE, Franco TURINI
Semester:
2
Hours:
48
Timetable:
TBA
Educational Goals:
The objective of the module is to align the competences of the students in computer science and base, in particular about databases, structured data analysis and programming languages.
Big data analytics
Provided by:
UNIPI, CNR
From:
M.Sc. in Data Science and BI
Sede:
Polo Fibonacci, University of Pisa
Lecturers:
Fosca GIANNOTTI
Semester:
1
Hours:
40
Timetable:
https://www.di.unipi.it/en/education/mds/timetable-mds
Educational Goals:
In our digital society, every human activity is mediated by information technologies. Therefore, every activity leaves digital traces behind, that can be stored in some repository. Phone call records, transaction records, web search logs, movement trajectories, social media texts and tweets, Every minute, an avalanche of Òbig dataÓ is produced by humans, consciously or not, that represents a novel, accurate digital proxy of social activities at global scale. Big data provide an unprecedented Òsocial microscopeÓ, a novel opportunity to understand the complexity of our societies, and a paradigm shift for the social sciences. Objective of the course is twofold: an introduction to the emergent field of big data analytics and social mining, aimed at acquiring and analyzing big data from multiple sources to the purpose of discovering the patterns and models of human behavior that explain social phenomena and an introduction to the technological scenario of scalable analytics.
Prerequisites:
The students are expected to be familiar with key management (financial & managerial accounting, cash flow analysis, org design, business processes) and strategy (PorterÕs models, innovation management basics) concepts before starting the course. For management engineering students, the course is highly recommended at 2nd year of the MSc degree (useful complement for PSSP). Recommended for students wishing to apply for Junior Consulting projects. For data science & computer science students, EGI course is recommended.
Programme:
Module 1: Foundations of competitive intelligence
- Systems thinking for management
- CI process and Key Intelligence Topics
- Sources and collection techniques
- Organizing CI in the companies
Module 2: Competitor and Market intelligence tools
- Competitive benchmarking (to assess competitive cost of operations, to analyze the true capabilities of a rival, as well as its immediate future actions)
- Blindspots
- Business ecosystems, platforms and business model innovation
Module 3: Corporate foresight tools
- Technology intelligence tools (i.e. patent analysis)
- Scenario analysis tools and techniques
- Weak signals and early warning system
Big Data and healthcare
Provided by:
S.ANNA
Lecturers:
Giuseppe TURCHETTI
Semester:
2
Hours:
10
Exam:
Y
Educational Goals:
Obiettivo principale del corso quello di analizzare le potenzialit dei Big Data nel settore della sanit, sia con riferimento alle attivit di ricerca e sviluppo delle imprese biomedicali (pharma, medical devices, biotech) che con riferimento agli obiettivi di salute e di sostenibilit economica dei sistemi sanitari nazionali. Una attenzione particolare, inoltre, verr dedicata a esplorare e definire modelli di servizio e di business che favoriscano, grazie ai Big Data, un maggiore engagement del paziente - e del cittadino in generale - nei confronti della aderenza alla terapia, del controllo attivo (empowerment) e della gestione (self management) della propria malattia o del proprio wellness, anche in una prospettiva di prevenzione. Infine, verranno svolte esecitazioni pratiche finalizzate a disegnare nuovi modelli organizzativi e di interazione tra diversi stakeholders nel settore della sanit (cittadini, attori del sistema sanitario, industria biomedicale, pagatori, ecc.) e modelli di stima dell'impatto di salute ed economico derivante dall'utilizzo di Big Data in sanit.
Big Data Ethics
Provided by:
UNIPI, S.ANNA
From:
Master II Livello Big Data Analytics
Sede:
Computer Science Department, Unversity of Pisa
Lecturers:
A. MONREALE, S. RUGGIERI, G, COMANDE'
Semester:
2
Hours:
40
Timetable:
TBA
Educational Goals:
The module aims to introduce ethical and legal notions of privacy, anonymity, transparency and discrimination, also referring the Directives and Regulations of the European Union and their ongoing evolution. Tho module will show Privacy-by-Design models and technologies that are useful to protect the users' rights and that allow the analysis of Big Data without harming the right to the protection of personal data, to transparency and to a fair treatment.
Big data in/for/from the Public Sector
Provided by:
S.ANNA
Lecturers:
Francesca BIONDI, Fabio PACINI
Semester:
2
Hours:
20
Exam:
Y
Educational Goals:
The aim of the course is to provide a general understanding of Public and Constitutional law to the students, who will get a conceptual framework of the processes through which each public policy is constructed, implemented and eventually evaluated by political and administrative institutions.
Programme:
The first module (10 hrs.) will focus on the current and future implications of the use of Big Data within the public decision-making across the different policy areas. In order to obtain a necessary alignment of competences for students coming from different backgrounds, the first part of the module will be devoted to a concise description of political decision-making from the point of view of Public and Constitutional law, both in Italy and abroad. The course will then deal with the analysis of the current and possible uses of Big Data within these public decision-making processes, as well as the ethical and political issues that it raises. The second module (10 hrs.) will focus, with an interactive approach, on the opportunities that can be given by the exploitation of Big Data produced and made available by political and administrative institutions.
Big data sources, crowdsourcing, crowdsensing
Provided by:
UNIPI, CNR
From:
Master II Livello Big Data Analytics
Sede:
Computer Science Department, Unversity of Pisa
Lecturers:
Maurizio TESCONI
Semester:
2
Hours:
20
Timetable:
TBA
Educational Goals:
This module presentes techniques and methods for acquisition of Big Data from a large sources of data available, including mobile phone data, GPS data, customer purchase data, social network data, open and administrative data, environmental and personal sensor data. We discuss also several participatory methods for crowdsourcing or crowdsensing collection of data through ad hoc campains like serious games and viral diffusion.
Bioinformatics
Provided by:
UNIPI
From:
M.Sc. in Computer Science
Sede:
Polo Fibonacci, University of Pisa
Lecturers:
Nadia PISANTI
Semester:
2
Hours:
48
Timetable:
https://www.di.unipi.it/en/education/mcs/timetable-wif
Educational Goals:
This course has the goal to give the student an overview of algorithmic methods that have been conceived for the analysis of genomic sequences. We will focus both on theoretical and combinatorial aspects as well as on practical issues such as whole genomes sequencing, sequences alignments, the inference of repeated patterns and of long approximated repetitions, the computation of genomic distances, and several biologically relevant problems for the management and investigation of genomic data. The exam has the goal to evaluate the students understanding of the problems and the methods described in the course. Moreover, the exam is additionally meant as a chance to learn how a scientific paper is like, and how to make an oral presentation on scientific/technical topics, that is designed for a specific audience.
Prerequisites:
A Basic course on algorithms
Programme:
A brief introduction to molecular biology: DNA, proteins, the cell, the synthesis of a protein.
Sequences Alignments: Dynamic Programming methods for local, global, and semi-local alignments. Computing the Longest Common Subsequences. Multiple Alignments.
Pattern Matching: Exact Pattern Matching: algorithms (Knuth-)Morris-Pratt, Boyer-Moore, Karp-Rabin with preprocessing of the pattern. Algorithm with preprocessing of the text: use of indexes.
Motifs Extraction: KMR Algorithm for the extracion of exact motifs and its modifications for the inference of approximate motifs.
Finding Repetitions: Algorithms for the inference of long approximate repetitions. Filters for preprocessing.
Fragment Assembly: Genomes sequencing: some history, scientific opportunities, and practical problems. Some possible approaches for the problem of assembling sequenced fragments. Link with the ÒShortest common superstringÓ problem, the Greedy solution. Data structures for representing and searching sequencing data.
New Generation Sequencing: Applications of High Throughput Sequencing and its algorithmic problems and challenges. Investigating data types resulting from the existing biotechnologies, and the possible data structures and algorithms for their storage and analysis.
Cloud Computing & Big-Data
Provided by:
S.ANNA
From:
Altro PhD (TECIP)
Lecturers:
Tommaso CUCINOTTA
Semester:
1
Hours:
30
Exam:
Y
Educational Goals:
This course provides an overview of the challenges to face, and the technical solutions to embrace, when building large-scale, fault-tolerant, distributed and replicated real-time cloud services. These systems need to be capable of serving millions/billions of requests per second with industrial-grade reliability, availability and performance, and are composed of thousands of components spanning across millions of machines, worldwide. The course focuses on design, development and operations of scalable software systems, including big-data processing and analytics, where the huge volumes of data to handle mandates the use of heavily distributed algorithms. The course covers also basic concepts on architectures of data-centre/cloud infrastructures.
Prerequisites:
Computer architectures and networks
Cloud Computing & Big-Data Lab
Provided by:
S.ANNA
From:
Altro PhD (TECIP)
Lecturers:
Tommaso CUCINOTTA
Semester:
2
Hours:
30
Exam:
Y
Educational Goals:
Hands-on follow-up to the Cloud Computing & Big-Data course. This is an applied course where students will put in practice the theoretical/abstract concepts acquired in the general course on Cloud Computing & Big-Data. During the practical sessions, we'll have a deep dive on such concepts as: machine virtualization (KVM) and OS-level virtualization (LXC) on Linux; virtual networking on Linux; network programming and distributed RPC frameworks; programming abstractions for cloud and distributed computing; elasticity in practice; big-data programming frameworks; Hadoop Map-Reduce; Apache Spark. Requisites: (1) Cloud Computing & Big-Data course (both of 2016 courses on Cloud Computing and Big-Data & Analytics work) (2) Computer programming & scripting.
Prerequisites:
Computer architectures and networks
Complementi di chimica fisica
Provided by:
SNS
Sede:
SNS
Lecturers:
da definire
Semester:
2
Hours:
40
Exam:
Y
Timetable:
da definire
Educational Goals:
da definire
Prerequisites:
da definire
Programme:
da definire
Complements of Bioinformatics
Provided by:
S.ANNA
From:
Altro PhD (Life Sciences)
Lecturers:
Andrea ZUCCOLO
Semester:
1
Hours:
20
Exam:
Y
Programme:
What is bioinformatics: a historical perspective from 1953 up to the next-gen sequencing;
Similarity searches: intro, basics, global vs local, dynamic programming, dot-plot, fasta, blast scoring matrix; pattern discovery
Following the journey of a genome: from reads to assembly to annotation to comparative genomics:
o Overall sequencing strategy (de novo vs reference based)
o Technical differences (sequencers, strategies: Hierarchical-BAC based, Whole Genome Shotgun, mixed strategies etc.)
o Genome assemblers: greedy graph, OLC, De Brujin's graph based
o The ÒvalidationÓ and Òquality assessmentÓ of a genome assembly
o Annotation: genes, pseudogenes, promoters, transcription factors, repeats (and Transposable Elements)
o Comparisons: SNPs, rearrangements, epigenomic, RNA seq
Complex Networks for Data Science
Provided by:
IMT
Sede:
IMT Lucca
Lecturers:
Guido CALDARELLI, T. SQUARTINI, G. CIMINI
Semester:
2
Hours:
40
Exam:
Y
Educational Goals:
The course aims at providing an overview of methods to analyse complex networks.
Prerequisites:
Solid mathematical background, scientific curiosity, interest in multidisciplinarity, passion for theory.
Programme:
Part I - Introduction to Complex Networks (Graph Theory Introduction. Properties of Complex Networks. Community Detection. Ranking Algorithms. Static Models of Graphs. Dynamical Models of Graphs. Fitness Models. Financial Networks).
Part II - Algorithms and Applications (Centrality Measures. Spectral Properties of Graphs. Community Detection. Bipartite Networks. Ranking and Reputation Algorithms. Trade Network Datasets. Multilayer Networks. Infrastructural Networks).
Part III - Statistical Mechanics of Networks (Complex Networks Randomization: A Primer. Basics of Information Theory. The Exponential Random Graphs Framework: From Zero to Shannon. The Maximum-Likelihood Recipe for Parameters Estimation. Hypothesis Testing on Networks: Pattern Detection, Network Filtering, Network Projection. The Dutch Interbank Network Case-Study. Network Reconstruction: A Survey of Existing Methods. Network Reconstruction: Moving Towards Entropy-Based Recipes. The World Trade Web Case-Study. International Economic Networks: The Interplay between Trade, Finance, Production and Migrations).
Part IV - Dynamical Processes on Networks (Master Equations, Models of Growing Networks - Continuous Description. Epidemics. Scaling and Percolation on Networks. Contagion in Financial Networks. Game Theory).
Cultural Heritage and Law
Provided by:
IMT
From:
Altro PhD (Institutions, Markets and Technologies)
Lecturers:
Lorenzo CASINI
Hours:
60
Educational Goals:
International Law, EU Law, and Domestic Law on Cultural Heritage. Basic elements of comparative law. Definition of Cultural Heritage. The institution of protection of cultural heritage in Italy. Fundamental principles and main public interests: protection, circulation, access. Problems and cases (Case law). - European Landscape Convention and Domestic Law on Landscape. Basic elements of comparative law. Principles and main issues: definition of landscape; levels of governance; public law instruments.Problems and cases (Case law).
Data Driven Innovation
Provided by:
UNIPI, S.ANNA
From:
Master II Livello Big Data Analytics
Sede:
Computer Science Department, Unversity of Pisa
Lecturers:
Alberto DI MININ, Andrea PICCALUGA
Semester:
2
Hours:
20
Timetable:
TBA
Educational Goals:
The module aims to show the main characteristics of the innovation processes in companies and institutions. After some basics of innovation economics, the management of the innovation processes will be presented (role of R&D, Open Innovation, etc.). The module also shows new innovation opportunities available after the last progresses in large scale data acquisition and elaboration, the basics of business models and start-ups. An exercise of business model innovation will try to explore che big data potential in opening new business opportunities.
Data Journalism & Story Telling
Provided by:
UNIPI, CNR
From:
Master II Livello Big Data Analytics
Sede:
Computer Science Department, Unversity of Pisa
Lecturers:
Luce DE BIASE, Andrea MARCHETTI
Semester:
2
Hours:
20
Timetable:
TBA
Educational Goals:
The module aims to teach how to present the knowledge extracted from big data using multimedia story telling. It also shows some of the most recent and meaningful experiences of journalism and story telling based on quantitative information extracted from different data sources.
Data Management for Business Intelligence
Provided by:
UNIPI, CNR
From:
Master II Livello Big Data Analytics
Sede:
Computer Science Department, Unversity of Pisa
Lecturers:
Salvatore RUGGIERI
Semester:
2
Hours:
20
Timetable:
TBA
Educational Goals:
The module shows technologies and systems for accessing, managing and analysing Big Data for decision support. Technologies and analysis of problems are shown using examples and case studies in lab. The student will acquire skills on the main technologies for business intelligence and big data management, including data warehouse and online analytical processing technologies.
Data Mining
Provided by:
UNIPI
From:
M.Sc. in Data Science and BI
Sede:
Polo Fibonacci, University of Pisa
Lecturers:
Dino PEDRESCHI
Hours:
80
Timetable:
https://www.di.unipi.it/en/education/mds/timetable-mds
Educational Goals:
DATA MINING: FOUNDATIONS
The formidable advances in computing power, data acquisition, data storage and connectivity have created unprecedented amounts of data. Data mining, i.e., the science of extracting knowledge from these masses of data, has therefore been affirmed as an interdisciplinary branch of computer science. Data mining techniques have been applied to many industrial, scientific, and social problems, and are believed to have an ever deeper impact on society. The course objective is to provide an introduction to the basic concepts of data mining and the process of extracting knowledge, with insights into analytical models and the most common algorithms.
DATA MINING: ADVANCED ASPECTS AND APPLICATIONS
The second part of the course completes the knowledge of the first module with: a review of advanced techniques for the mining of new forms of data; a review of the main application areas and case studies.
Programme:
DATA MINING: FOUNDATIONS
Fundamentals of data mining and the knowledge discovery process
Explorative Data Analysis and Visual analytics
Frequent patterns and Rules
Clustering: partition based techniques, density based techniques and hierarchical techniques
Classification: decision trees and Bayesian Methods
Analytical experiments with data mining tools
DATA MINING: advanced aspects and applications
Mining of time series and spatio-temporal data
Mining of sequential data and graphs
Advanced techniques for classification, clustering and outlier detection
Language, standard and architectures of data mining systems
Social impact of data mining
Data mining and privacy protection
Case studies in socio-economic domains (marketing and CRM, mobility and transport, public health, etc.)
Data Mining and Machine Learning
Provided by:
UNIPI, CNR
From:
Master II Livello Big Data Analytics
Sede:
Computer Science Department, Unversity of Pisa
Lecturers:
Dino PEDRESCHI, Fosca GIANNOTTI
Semester:
2
Hours:
40
Timetable:
TBA
Educational Goals:
The module provides an introduction to base concepts of data mining and knowledge extraction process, introducing analytical models and algorithms for clustering, classification and pattern discovery, also referring Big Data sources.
Data Science Colloqium
Provided by:
ALL
Lecturers:
Dino PEDRESCHI (coord.)
Hours:
40
Data Visualization & Visual analytics
Provided by:
UNIPI, CNR
From:
Master II Livello Big Data Analytics
Sede:
Computer Science Department, Unversity of Pisa
Lecturers:
Salvatore RINZIVILLO
Semester:
2
Hours:
20
Timetable:
TBA
Educational Goals:
The module aims to present the basic methods and techniques for the visualization and presentation of the information obtained from different sources: structured data (relational, hierarchical, trees), network data (social network), temporal data, spatial data and spatio-temporal data. Studying the existing methods and tools, some scenarios of visual analytics will be presented.
Economic Networks
Provided by:
S.ANNA
From:
Altro PhD (Economics)
Sede:
Nizza
Lecturers:
Giorgio FAGIOLO
Semester:
2
Hours:
18
Exam:
Y
Educational Goals:
This course introduces the Òscience of networksÓ for economists. The first part of the course discusses examples of real-world networks in hard and social sciences. We ask why networks are important for economists and what are the main network-related questions as far as models and empirical analyses are concerned. We then present more formally graph theory and explore network statistics. We finally move to models of network formation and present some relevant applications to economics (e.g. trade networks). Syllabus:Ê¥ What is a network ¥ Examples of networks ¥ Why networks in economics and social sciences ¥ Networks and graphs ¥ Measures and metrics on networks ¥ Distribution of metrics and measures in large networks ¥ Models of network formation ¥ Null statistical network models ¥ Tutorial #1: Network data and network visualization ¥ Tutorial #2: Network measures and metrics ¥ Tutorial #3: Distributional analysis and network randomization ¥ Economic applications.
Ethics and legal dimensions of data science
Provided by:
S.ANNA
Sede:
Sant'Anna
Lecturers:
Giovani COMANDE' (coord.)
Semester:
2
Hours:
10
Exam:
Y
Educational Goals:
The aim of the course is to provide a general understanding of Public and Constitutional law to the students, who will get a conceptual framework of the processes through which each public policy is constructed, implemented and eventually evaluated by political and administrative institutions. The first module (10 hrs.) will focus on the current and future implications of the use of Big Data within the public decision-making across the different policy areas. In order to obtain a necessary alignment of competences for students coming from different backgrounds, the first part of the module will be devoted to a concise description of political decision-making from the point of view of Public and Constitutional law, both in Italy and abroad. The course will then deal with the analysis of the current and possible uses of Big Data within these public decision-making processes, as well as the ethical and political issues that it raises. The second module (10 hrs.) will focus, with an interactive approach, on the opportunities that can be given by the exploitation of Big Data produced and made available by political and administrative institutions.
European Statistical System
Provided by:
UNIPI
From:
M.Sc. Economics - Official Statistics
Lecturers:
Monica PRATESI (coord.)
Hours:
40
Programme:
On 27 October 2017 in Room F2 from 12.15 to 13.45 and on 30 October 2017 in Room L2 from 12.15 to
13.45, Prof. Daniela Ghio (Ministero dell'Interno) will take the Seminars on "The European Statistical
System";
- On 10, 13, 17 and 20 November 2017 from 12.15 to 13.45, Prof. Luigi Biggeri (University of Florence) will
take lectures on "The estimation and computation of Income, Consumption and PPPs in the European
Statistical System" (Room F2 and L2);
- On 9 November 2017 from 12.15 to 13.45 in Room F2, Prof. Tommaso Rondinella (ISTAT Roma) will take
lectures on "Measuring Equitable and Sustainable Well-being: the Italian BES and the Sustainable
Development Goals";
- On 21st of November 2017 and the 5th of December 2017 at 16.00, Prof. Natalie Shlomo, Magna
Room, distance lectures;
- On 30 th November 2017 and 1 st December 2017 from 12.15 to 13.45 (Room F2), Prof. Ulrich Rendtel
Freie University , lectures ÒIssues of the analysis of socio-economic panel surveysÓ;
- On 27th November 2017 from 12.15 to 13,45 (Room L2 )Lecture Prof. Parthasarathi Lahiri, Maryland
Population Research Center, ÒBig Data, Big Promise, Big Challenge: Can Small Area Estimation Play a Role
in the Big Data Centric World?Ó
Genomica avanzata
Provided by:
S.ANNA, UNIPI
From:
M.Sc. Biotecnologie Molecolari
Lecturers:
Mario Enrico PE', Andrea ZUCCOLO
Semester:
2
Hours:
64
Exam:
Y
Educational Goals:
Il corso si propone di fornire conoscenze di base sulla struttura, la funzione e lÕevoluzione dei genomi di procarioti e di eucarioti. Saranno considerate le diverse metodiche utilizzate per lo studio dei genomi e sar discusso come lÕadozione di approcci genomici hanno cambiato il modo di affrontare le problematiche biologiche. Il corso inoltre prevede di introdurre gli studenti allÕutilizzo e alla comprensione degli strumenti bioinformatici necessari alla gestione e allÕanalisi dei dati provenienti da esperimenti di sequenziamento. Accanto alla presentazione delle tecniche e degli algoritmi sottostanti saranno proposte attivit pratiche su dati reali relativi a genomi batterici, animali e vegetali.
High Performance & Scalable Analytics, NO-SQL Big Data Platforms
Provided by:
UNIPI, CNR
From:
Master II Livello Big Data Analytics
Sede:
Computer Science Department, Unversity of Pisa
Lecturers:
Roberto TRASARTI, Claudio LUCCHESE
Semester:
2
Hours:
20
Timetable:
TBA
Educational Goals:
The aim of this course is to introduce the student with the high performance Big Data management tools. The student will gain expertise in the use od NO-SQL platforms for the analysis and mining of large data volumes, thus performing tasks that would not be feasible with traditional data bases.
How to do research
Provided by:
S.ANNA
From:
Altro PhD (Emerging Digital Technologies()
Lecturers:
Giorgio BUTTAZZO
Semester:
2
Hours:
30
Exam:
Y
Educational Goals:
The course will cover the entire process needed during a research study, from the initial phase in which a new problem is addressed, formalized, and solved, up to the final phase in which the achieved results have to be communicated to the scientific community. The course is divided in six lectures of 3 hours each. The first lecture explains the meaning of research and how to approach the various steps involved in the process. The second lecture explains how to write papers, set a good structure, outline the contents, make figures, cite references, and avoid common mistakes. The third lecture explains how the scientific publication process works and how to participate in it, simulating a typical paper selection process of a conference. The fourth lecture explains how to write successful research projects. The fifth lecture will simulate a program committee meeting, where papers are discussed, evaluated, and comments are sent back to the authors for revision. The sixth lecture explains how to make presentations, good slides, and have the appropriate attitude when presenting the work. The seventh lecture part is devoted to paper presentations, simulating a small conference run by the students.
Identification, Analysis and Control of Dynamical Systems
Provided by:
IMT
From:
Altro PhD (Institutions, Markets and Technologies)
Sede:
IMT Lucca
Lecturers:
Alberto BEMPORAD
Semester:
1
Hours:
20
Exam:
Y
Educational Goals:
The course provides an introduction to dynamical systems, with emphasis on linear systems in state-space form. After introducing the basic concepts of stability, controllability and observability, the course covers the main techniques for the synthesis of stabilizing controllers (state-feedback controllers and linear quadratic regulators) and of state estimators (Luenberger observer and Kalman filter). The course also briefly covers data-driven approaches of parametric identification to obtain models of dynamical systems from a set of data, with emphasis on the analysis of the robustness of the estimated models w.r.t. noise on data and on the numerical implementation of the algorithms.
Prerequisites:
Linear algebra and matrix computation, calculus and mathematical analysis
Programme:
Equilibrium points and stability. Linearization of nonlinear systems. Discretization of continous-time systems. Transfer functions. Observability and controllability of LTI systems. Luenberger's observer and state-feedback controllers. Linear quadratic regulator and Kalman filter. Basic concepts on linear parameter-varying systems. System identification: Least-squares methods and recursive identification, instrumental-variables methods, consistent and unbiased estimators, prediction error methods.
Information Retrieval
Provided by:
UNIPI, CNR
From:
Master II Livello Big Data Analytics
Sede:
Computer Science Department, Unversity of Pisa
Lecturers:
Paolo FERRAGINA
Semester:
2
Hours:
40
Timetable:
TBA
Educational Goals:
The module aims to teach the software modules used to build a modern search engine and the analysis of the performance and the computational limits of the algorithmics solutions currently used in each of them. Practical and theoretical fundmentals for the organization and the analysis of IR systems.
Intellectual Property for and in data science
Provided by:
S.ANNA
Sede:
S. Anna
Lecturers:
Caterina SGANGA
Semester:
2
Hours:
10
Exam:
Y
Programme:
Database protection applied to Big Data. Ownership of user generated contents. IP contracts.Trade secrets vs privacy rights. Trade secrets vs freedom of research and freedom to conduct a business. Patent in DataScience: obstacles, potentialities. Intellectual Property Owner in DataScience. The intellectual property rights of artificial intelligence generated ÒcreationsÓ and ÒinventionsÓ. Machine rationality versus machine artistic creativity.
Introduction to Cognitive and Social Psyschology
Provided by:
IMT
From:
Altro PhD (Institutions, Markets and Technologies)
Lecturers:
Pietro PIETRINI, Emiliano RICCIARDI
Hours:
24
Educational Goals:
This course will provide an introduction to general themes in Cognitive and Social Psychology. In the first part of the course, we will review seminal findings that had a major impact on our knowledge of cognitive processes and social interactions, as well as more recent studies that took advantage of neuroimaging, electrophysiology and brain stimulation methods to shed new light on decision-making and social behaviors. During the second part of the course, students will be asked to perform a brief presentation of a research article and to critically discuss positive aspects and limitations of the study. The course will include seminars and lectures by renowned researchers in the field and will educate PhD candidates about the influence of social aspects of the human nature on cognitive and brain functioning (and vice-versa) in an intellectually motivating manner.
Introduction to Evolutionary Biology
Provided by:
S.ANNA
From:
Altro PhD (Life Sciences)
Lecturers:
Andrea ZUCCOLO
Semester:
2
Hours:
15
Exam:
Y
Educational Goals:
The main goal of the course is to introduce the basic principles underlying evolutionary biology and to present and discuss rationale and methods of phylogenetic inference. We'll discuss the process of selecting and gathering appropriate datasets for subsequent analyses and we'll explain three widely used methods of phylogenetic inference: parsimony, distance, and likelihood methods. Particular attention will be paid to the methods used to evaluate objectively the reliability and accuracy of the resulting inferences. Finally, we'll critically consider the results of phylogenetic reconstructionÑhow they can shed light on past evolutionary events, such as gene duplications and lateral gene transfers, as well as how they can be used for other purposes, such as predicting gene function and resolving RNA secondary structures.
Introduction to neural networks
Provided by:
S.ANNA
From:
Altro PhD (Emerging Digital Technologies()
Lecturers:
Giorgio BUTTAZZO
Semester:
2
Hours:
30
Exam:
Y
Prerequisites:
C programming and matrix math
Programme:
Motivations of research. Main neural models. Learning paradigms.
Associative memories and Hopfield networks
Layered networks.
Kohonen networks
Reinforcement Learning
Legally Compliant Data Science
Provided by:
S.ANNA, UNIPI
Lecturers:
Giovanni COMANDE'
Semester:
1
Hours:
36
Exam:
Y
Machine Learning
Provided by:
IMT
From:
Altro PhD (Institutions, Markets and Technologies)
Lecturers:
Giorgio Stefano GNECCO
Hours:
20
Educational Goals:
The course provides an introduction to basic concepts in machine learning. Topics include: learning theory (bias/variance tradeoff; Vapnik-Chervonenkis dimension and Rademacher complexity, cross-validation, feature selection); supervised learning (linear regression, logistic regression, support vector machines); unsupervised learning (clustering, principal and independent component analysis); semisupervised learning (Laplacian support vector machines); online learning (perceptron algorithm); hidden Markov models.
Microeconometria
Provided by:
S.ANNA
From:
Altro PhD (Economics)
Lecturers:
Federico TAMAGNI
Semester:
2
Hours:
30
Exam:
Y
Educational Goals:
Il corso presenta le principali tecniche econometriche per dati micro e panel: propriet e problemi di inconsistenza delle stime OLS; modelli a variabile dipendente discreta; modelli panel lineari (statici e dinamici); soluzioni ai problemi di endogeneita' e selezione del campione; tecniche per l'analisi distribuzionale (stime kernel e regressioni quantiliche). Le lezioni prevedono sessioni di illustrazione ed esercitazione al computer, utilizzando il software statistico STATA.
Microeconomics
Provided by:
S.ANNA
From:
Altro PhD (Economics)
Lecturers:
Laura MAGAZZINI, Federico TAMAGNI
Semester:
2
Hours:
20
Exam:
Y
Educational Goals:
The course aims at providing students the econometric tools for dealing with the analysis of individual-level data on the economic behavior of individuals or firms. Regression methods for the analysis of static and dynamic panel data models and estimation of limited dependent variable models will be considered, including discrete choice models, censored and truncated regressions, and sample selection. Besides the theoretical background, students will be exposed to the discussion and the analysis of examples and empirical applications. The course requires knowledge of basic econometric tools for the analysis of linear models (regression methods) and of the method of maximum likelihood for estimation. The software STATA will be employed for the analysis. A basic working knowledge is preferred. Outline: (1). Linear panel data models: 1.1. The ÔpanelÕ solution to the omitted variable bias. 1.2. Estimation: fixed effects versus random effects. 1.3. Dynamic panel data models: the Nickel (1981) bias; GMM estimation. (2). Discrete choice models: 2.1. The linear probability model. 2.2. Logit and probit. 2.3. Multinomial models. (3). Count data models. (4). Truncation and censoring: 4.1. Truncated regression. 4.2. The censored regression model (Tobit). 4.3. Sample selection.
Mobility Data Analysis
Provided by:
UNIPI, CNR
From:
Master II Livello Big Data Analytics
Sede:
Computer Science Department, Unversity of Pisa
Lecturers:
Mirco NANNI
Semester:
2
Hours:
20
Timetable:
TBA
Educational Goals:
The purpose of the course is to introduce the main analysis techniques for spatio-temporal data, with a particular focus on human mobility (including vehicles), aimed to better understand the overall mobility of a territory. The presentation will be supported by several case studies developed with the SoBigData.eu laboratory.
Model Predictive Control
Provided by:
IMT
From:
Altro PhD (Institutions, Markets and Technologies)
Sede:
IMT Lucca
Lecturers:
Alberto BEMPORAD
Semester:
2
Hours:
20
Exam:
Y
Educational Goals:
COURSE DESCRIPTION
Model Predictive Control (MPC) is a well-established technique for controlling multivariable systems subject to constraints on manipulated variables and outputs in an optimized way. Following a long history of success in the process industries, in recent years MPC is rapidly expanding in several other domains, such as in the automotive and aerospace industries, smart energy grids, and financial engineering.
The course is intended for students and engineers who want to learn the theory and practice of Model Predictive Control (MPC) of constrained linear, linear time-varying, nonlinear, stochastic, and hybrid dynamical systems, and numerical optimization methods for the implementation of MPC. The course will make use of the MPC Toolbox for MATLAB developed by the teacher and co-workers (distributed by The MathWorks, Inc.) for basic linear MPC, and of the Hybrid Toolbox for explicit and hybrid MPC.
Prerequisites:
Linear algebra and matrix computation, linear control systems, numerical optimization.
Programme:
General concepts of Model Predictive Control (MPC). MPC based on quadratic programming. General stability properties. MPC based on linear programming. Models of hybrid systems: discrete hybrid automata, mixed logical dynamical systems, piecewise affine systems. MPC for hybrid systems based on on-line mixed-integer optimization. Multiparametric programming and explicit linear MPC, explicit solutions of hybrid MPC. Stochastic MPC: basic concepts, approaches based on scenario enumeration. Linear parameter- and time-varying MPC and applications to nonlinear dynamical systems. Selected applications of MPC in various domains, with practical demonstration of the MATLAB toolboxes.
Numerical methods for optimal control
Provided by:
IMT
Sede:
IMT
Lecturers:
Mario ZANON
Semester:
1
Hours:
20
Exam:
Y
Educational Goals:
The students will learn how to effectively solve optimisation and optimal control problem in practice.
Prerequisites:
Basic knowledge in calculus, linear algebra and dynamical systems
Programme:
Many control and estimation tasks seek at minimizing a given cost while respecting a set of constraints, which belongs to the class of problems denoted as Optimal Control (OC). The most practical approach to solve OC problems is via direct methods, which consist in discretizing the problem to obtain a Nonlinear Program (NLP) which is then solved using one of the many available approaches. The course will be introduced by an overview of the available classes of algorithms for OC and place direct methods in this context. The core of the course is structured around the following two main parts.
1. NLP solvers:
This part of the course covers Nonlinear Programming first establishes a sound theoretical background on the characterization of local minima (maxima) by introducing geometric optimality concepts and relating them to the first- and second-order conditions for optimality, i.e. the Karush-Kuhn-Tucker conditions, constraint qualifications and curvature conditions.
Second, the theoretical concepts will be used to analyse the most successful algorithms for derivative-based nonconvex optimization, i.e. Sequential Quadratic Programming and Interior Point Methods, both based on NewtonÕs method. Since there does not exist a plug-and-play NLP solver, attention will be devoted to giving the students a solid understanding of the mechanisms underlying the algorithms so as to endow them with the ability to formulate the problem appropriately and choose the adequate algorithm for each situation.
2. Discretisation techniques:
This second part of the course covers the most successful discretization approaches, i.e. single-shooting, multiple-shooting and collocation. All mentioned approaches rely on the simulation of dynamical systems, for which a plethora of algorithms have been developed. The students will be explained the features of the different classes of algorithms, with particular attention on the numerical efficiency, simulation accuracy and sensitivity computation. Finally, the structure underlying the NLP obtained via direct methods for OC will be analysed in order to understand the immense benefits derived from developing dedicated structure-exploiting OCP solvers.
Numerical Optimization
Provided by:
IMT
From:
Altro PhD (Institutions, Markets and Technologies)
Sede:
IMT Lucca
Lecturers:
Alberto BEMPORAD
Semester:
1
Hours:
20
Exam:
Y
Educational Goals:
Optimization plays a key role in solving a large variety of decision problems that arise in engineering (design, process operations, embedded systems), data science, machine learning, business analytics, finance, economics, and many others. This course focuses on formulating optimization models and on the most popular numerical methods to solve them.
Prerequisites:
Linear algebra and matrix computation, calculus and mathematical analysis.
Programme:
Modeling: linear programming models, convex optimization models. Basic optimization theory: optimality conditions, sensitivity, duality. Algorithms for constrained convex optimization: active-set methods for linear and quadratic programming, proximal methods and ADMM, stochastic gradient, interior-point methods. Line-search methods for unconstrained nonlinear programming, sequential quadratic programming.
Peer to peer systems and blockchains
Provided by:
UNIPI
From:
M.Sc. in Computer Science
Sede:
Polo Fibonacci, University of Pisa
Lecturers:
Laura RICCI
Semester:
2
Hours:
48
Timetable:
https://www.di.unipi.it/en/education/mcs/timetable-wif
Educational Goals:
This course introduces the basic principles and tools to define and develop a peer to peer (P2P) system, with a focus on Distributed Hash Tables and the distributed technology of blockchains and on the cryptocurencies. The first part of the course introduces the general concepts underlying any P2P system (topology, information diffusion,...). This part also present several case studies (Bittorrent, the KAD network,..).Cryptocurrencies and, more in general, blockchains, are a recent Ókiller applicationÓ in the area of P2P systems. The second part of the course presents and discusses the blockchain technology and the decentralized digital currencies (cryptocurrencies) such as Bitcoin. The course introduces both the theory and principles at the basis of cryptocurrencies operations and practical examples of their use. This part introduces the cryptocurrency ecosystem and discusses the existing and potential interaction of cryptocurrencies with the banking, financial, legal and regulatory environment. Lastly the course details how innovative applications exploit blockchain technology
Prerequisites:
A basic course in networking
Programme:
P2P Topologies
Ð Peer to Peer (P2P) systems: general concepts
Ð Unstructured Overlays: Flooding, Random Walks, Epidemic Diffusion
Ð Structured Overlays: Distributed Hash Tables (DHT), Routing on a DHT
Ð Case Studies:
_ Bittorrent as a Content Distribution Network: KAD implementation of
the Kademlia DHT, game-based cooperation
¥ Complex Network for the analysis of P2P systems
Ð Network models
_ Random Graphs and Small Worlds
_ Small World navigability: Watts Strogatz and Kleinberg.
_ Complex networks navigability
¥ Cryptocurrencies and Blockchains
Ð basic concepts:
_ review of basic cryptographic tools (digital signatures, cryptographic hash,
Merkle trees.,..)
_ blockchains: definitions
_ distributed consensus: definitions
Ð the Bitcoin blockchains
_ Nakamoto consensus
_ Bitcoin mining mechanism
_ pseudoanonymity: traceability and mixing
_ the Bitcoin P2P Network
_ Bitcoin ecosystem
_ scalability issues
_ Bitcoin Extensions/alternatives: altcoins, sidechains, the StellarConsensus
Protocol
Ð Applications of blockchains
_ Ethereum: programming smart contracts
_ Blockchain 1.0: cryptocurrencies
_ Blockchain 2.0: financial instruments built on cryptocurrencies
_ Blockchain 3.0: applications beyond cryptocurrencies (DNS, lotteries, vot-
ing, IoT...)
PhD+: Research valorization, innovatiom entrepreneural mindset
Provided by:
UNIPI
From:
Cross-PhD
Lecturers:
(vari)
Semester:
2
Timetable:
https://www.unipi.it/index.php/phd/item/11226-programme-2017
Educational Goals:
PhD+ is a unique programme aimed at fostering innovation and entrepreneurial mind-set in students and graduates of the University of Pisa, PhD students and PhDs of all Superior Graduate Schools in Tuscany, and academics. PhD+ consists of a series of interactive and engaging lectures combined with coaching and mentoring activities, given by top-level experts in innovation and technology transfer.
PhD+ is one of the best practice of training in research valorisation, innovation and entrepreneurship, also recognized by the Network of Design for Resilient Entrepreneurship, within the ENDuRE European project. To date, PhD+ has received many national and international awards, also thanks to the successes of its multi-awarded spin-offs.
From this year onwards the PhD+ will represent the qualified training offer within the Contamination Lab project.
The 2018 edition will take place from 6th February to 8th March and will present new features:
¥ more topics related to research valorisation and European funding opportunities will be discussed;
¥ ¥ this edition of the course will be more internationally-oriented, thanks to the collaboration with the Brasilian Federal University of Pernanbuco, allowing the students of these Universities to attend the lectures in video streaming by means of the Unipi Mediateca platform;
¥ it will be cutting edge on new technological trends.
The past PhD+ edition lectures and seminars are available in video streaming on the Mediateca platform of the University of Pisa.
Principles of Brain Anatomy and Physiology
Provided by:
IMT
From:
Altro PhD (Institutions, Markets and Technologies)
Lecturers:
Luca CECCHETTI, Michele EMDIN
Hours:
36
Educational Goals:
The course aims at introducing the fundamentals of brain anatomy and physiology. In the first part of the course we will revise the basics of neuron structure and function, as well as synaptic mechanisms and cytoarchitectonic properties of the cortical mantle, with particular regards to visual, auditory, somatosensory and motor systems. Moving from this fine-grained description of the human brain, we will focus on gross neuroanatomy: through the use of in-vivo state-of-the-art techniques, such as structural MRI and diffusion weighted imaging, we will review gyri and sulci of the cortex, subcortical structures, brainstem nuclei and major white matter fasciculi. The second part of the course will be devoted to the study of functional neuroanatomy, with insights on the relationship between specific brain structures and human cognition, collected using functional, metabolic and receptors mapping, as well as lesion studies. In particular, the the following topics will be covered: central and peripheral nervous systems, occipital parietal frontal temporal and limbic areas, subcortical nuclei and white matter fasciculi, cerebellum, methodologies of structural brain imaging: VBM, cortical thickness and folding, VLSM, Diffusion Weighted Imaging and Tractography (theory and methodologies of data processing, hands-on sessions). The last part of the course will instead cover topics related to peripheral and autonomous nervous system.
Principles of Concurrent and Distributed Programming
Provided by:
IMT
From:
Altro PhD (Institutions, Markets and Technologies)
Sede:
IMT Lucca
Lecturers:
Rocco DE NICOLA, Letterio GALLETTA
Semester:
1
Hours:
30
Exam:
Y
Educational Goals:
The objective of the course is to introduce the basics of concurrent and distributed programming through an illustration of the concepts and techniques related to modeling systems in which there are more components that are simultaneously active and need to coordinate and compete for the use of shared resources. At the end of the course, students will have a good understanding of the problems connected to concurrent programming and a good knowledge of the different approaches to modelling communication among distributed components and to safe resource sharing. By means of an hands-on approach, at the end of the course students be able to write and evaluate concurrent programs using different programming languages.
Prerequisites:
Basics of Computer Programming
Programming for data science
Provided by:
UNIPI
From:
M.Sc. in Data Science and BI
Sede:
Polo Fibonacci, University of Pisa
Lecturers:
Giuseppe PRENCIPE, Giulio ROSSETTI
Semester:
1
Hours:
96
Exam:
Y
Timetable:
https://www.di.unipi.it/en/education/mds/timetable-mds
Educational Goals:
This is an introductory course to computer programming and related mathematical/logic background for students without a Bachelor in Computer Science or in Computer Engineering. The objective is to smoothly introduce the student to the programming concepts and tools needed for typical data processing and data analysis tasks. The course consists of lectures and practice in computer labs. The student will be able to use computer programming languages and related mathematical notions for problem reasoning and solving. The student will be able to separate apart the problem constraint and solutions from the actual coding in a specific computer programming language. Computational thinking is the expected ability at the end of the course.
Prerequisites:
Basic mathematical notions as given in most of Bachelor programs.
Programme:
Sets, relations, functions, combinatorics, grammars, automata.
Propositional and first order logic.
Induction and recurrence relations.
Imperative programming.
Object oriented programming.
Programming stack and development tools.
Python programming.
C programming.
Public Healthcare Management & Big data
Provided by:
S.ANNA
Lecturers:
Sabina NUTI, Milena VAINIERI, Chiara SEGHIERI
Semester:
1
Hours:
20
Exam:
Y
Programme:
The primary objective of the course is to show how the data and information collected from internal sources of the Health System and external, through the involvement of the users as co-producers of health, can be used within public contexts in order to both support the strategic planning at all levels of governance and to measure the ability of the system to create value for the population with the available resources.
Python Programming for Complex Networks
Provided by:
IMT
From:
Altro PhD (Institutions, Markets and Technologies)
Lecturers:
Guido CALDARELLI
Hours:
20
Educational Goals:
We present in this course the latest results in the field of complex networks. They can solve immediate problems when studying the Internet and the WWW, and they can help sort information on a variety of other systems. This course is structured in such a way as to start with a specific problem and then present the theoretical tools needed to model or to sort out the most relevant information. In the course we shall follow the various students from the setup of the software in their pc (a basic in Python will be provided in another course) to the application of existing software and the writing of specific one for the personal line of research of the students.
Scientific Programming
Provided by:
SNS
Sede:
SNS
Lecturers:
Julien Roland Michel BLOINO
Hours:
60
Exam:
Y
Timetable:
https://www.sns.it/ugovserse/teaching/1580
Social network analysis
Provided by:
UNIPI
From:
M.Sc. in Data Science and BI
Sede:
Polo Fibonacci, University of Pisa
Lecturers:
Dino PEDRESCHI
Semester:
2
Hours:
40
Timetable:
https://www.di.unipi.it/en/education/mds/timetable-mds
Educational Goals:
Over the past decade there has been a growing public fascination with the complex ÒconnectednessÓ of modern society. This connectedness is found in many contexts: in the rapid growth of the Internet and the Web, in the ease with which global communication now takes place, and in the ability of news and information as well as epidemics and financial crises to spread around the world with surprising speed and intensity. These are phenomena that involve networks and the aggregate behavior of groups of people; they are based on the links that connect us and the ways in which each of our decisions can have subtle consequences for the outcomes of everyone else. This course is an introduction to the analysis of complex networks, with a special focus on social networks and the Web - their structure and function, and how it can be exploited to search for information. Drawing on ideas from computing and information science, applied mathematics, economics and sociology, the course describes the emerging field of study that is growing at the interface of all these areas, addressing fundamental questions about how the social, economic, and technological worlds are connected. Data-driven analysis of complex networks using a variety of models and software tools.
Programme:
Big graph data and social, information, biological and technological networks
The architecture of complexity and how real networks differ from random networks: node degree and long tails, social distance and small worlds, clustering and triadic closure. Comparing real networks and random graphs. The main models of network science: small world and preferential attachment.
Strong and weak ties, community structure and long-range bridges. Robustness of networks to failures and attacks. Cascades and spreading. Network models for diffusion and epidemics. The strength of weak ties for the diffusion of information. The strength of strong ties for the diffusion of innovation.
Practical network analytics with Cytoscape and Gephi. Simulation of network processes with NetLogo.
Social Network Analysis (Master)
Provided by:
UNIPI, CNR
From:
Master II Livello Big Data Analytics
Sede:
Computer Science Department, Unversity of Pisa
Lecturers:
Andrea PASSARELLA
Semester:
2
Hours:
20
Timetable:
TBA
Educational Goals:
This course introduces students to the theories, concepts and measures of Social Network Analysis (SNA), that is aimed at characterizing the structure of large-scale Online Social Networks (OSNs). The course presents both classroom teaching to introduce theoretical concepts, and hands-on computer work to apply the theory on real large-scale datasets obtained from OSNs like Facebook and Twitter. The course aims to discuss in particular how the structural properties of social networks can be analyzed through SNA techniques, and how these properties can be used to characterize social phenomena arising in the society.
Socio-Economic Networks
Provided by:
IMT
From:
Altro PhD (Institutions, Markets and Technologies)
Lecturers:
Massimo RICCABONI
Hours:
20
Educational Goals:
The topic of the course will be the analysis of socio-economic networks. The course will consist of two parts: (1) micro level networks of individuals and firms, (2) macro-level networks of sectors and countries. The first part will focus on social networks and the division of (innovative) labor within and across firm boundaries. The second part on the empirics of macro networks in economics will have a strong focus on international trade, investments and human mobility. Both parts will give you a brief overview on the literature, which predominantly adopted an econometric approach to the analysis of networks.
Statistical methods for data science
Provided by:
UNIPI
From:
M.Sc. in Data Science and BI
Sede:
Polo Fibonacci, University of Pisa
Lecturers:
Salvatore RUGGIERI
Semester:
2
Hours:
48
Exam:
Y
Timetable:
https://www.di.unipi.it/en/education/mds/timetable-mds
Educational Goals:
The student who completes successfully the course will have a solid knowledge on the main concepts and tools of statistical analysis, including the definition of a statistical model, the inference of its parameters with confidence intervals, the use of hypothesis testing. and some basic knowledge of the statistics of linear time series. Finally the student will be able to use the language R for performing statistical analyses. The student will be able to understand the main concept of statistical analysis and to choose and apply the appropriate tool to the case under study. The student will also be able to use the language R for performing statistical analyses.
Prerequisites:
Basic knowledge of calculus. Basic knowledge of probability might be useful even if not indispensable.
Programme:
The program covers the basic methodologies, techniques and tools of statistical analysis. This includes basic knowledge of probability theory, random variables, convergence theorems, statistical models, estimation theory, and hypothesis testing. Other topics covered include bootstrap, expectation-maximization, and basic knowledge of time series analysis. Finally the program covers the use of the language R for statistical analysis.
Statistical Methods for Large, Complex Data
Provided by:
S.ANNA
From:
Altro PhD (Economics)
Lecturers:
Francesca CHIAROMONTE
Semester:
2
Hours:
10
Exam:
N
Educational Goals:
Outline: Lecture 1: Computational Assessment of Statistical Procedures. Resampling (e.g. Jacknife, Boostrap), Cross-Validation schemes and their uses. Lecture 2: High Dimensional Supervised Problems. Linear and Generalized Linear Models (review, including traditional feature selection), Shrinkage and Sparsification (Ridge, LASSO and other developments). Lecture 3: Ultra-High Dimensional Supervised Problems. Feature Screening algorithms for linear and generalized linear models, and model-free. Lecture 4: Ultra-High Sample Sizes. Significance and Effect Sizes, Subsampling strategies for Big Data.
Stochastic Processes and Stochastic Calculus
Provided by:
IMT
From:
Altro PhD (Institutions, Markets and Technologies)
Lecturers:
Irene CRIMALDI
Hours:
30
Educational Goals:
This course aims at introducing some important stochastic processes and Ito stochastic calculus. Some proofs are sketched or omitted in order to have more time for examples, applications and exercises. In particular, the course deals with the following topics: - Markov chains (definitions and basic properties, classification of states, invariant measure, stationary distribution, some convergence results and applications, passage problems, random walks, urn models, introduction to the Markov chain Monte Carlo method), - conditional expectation and conditional variance, - martingales (definitions and basic properties, Burkholder transform, stopping theorem and some applications, predictable compensator and Doob decomposition, some convergence results, game theory, random walks, urn models), - Poisson process, Birth-Death processes, - Wiener process (definitions, some properties, Donsker theorem, Kolmogorov-Smirnov test) and Ito calculus (Ito stochastic integral, Ito processes and stochastic differential, Ito formula, stochastic differential equations, Ornstein-Uhlenbeck process, Geometric Brownian motion, Feynman-Kac representation formula).
Prerequisites:
Matrix Algebra + Foundations of Probability and Statistical Inference
Strategic and competitive intelligence
Provided by:
UNIPI
From:
M.Sc. in Data Science and BI
Sede:
Polo Fibonacci, University of Pisa
Lecturers:
Antonelle MARTINI
Semester:
1
Hours:
40
Timetable:
https://www.di.unipi.it/en/education/mds/timetable-mds
Educational Goals:
CI programs have goals such as proactively detecting business opportunities or threats, eliminating or reducing blind- spots, risks and/or surprises; and reducing reaction time to competitor and marketplace changes. The end product of any worthwhile CI activity is what practitioners term Ôactionable intelligenceÕ Ð i.e. intelligence that management can act upon; perspective. It is more than analysing competitors: it is a process for gathering information, converting it into intelligence (about products, customers, competitors, and any aspect of the environment) and then using it in decision making. In this sense, big data brings big change to CI. The course is very interactive and includes also in-class seminars with experts on emerging topics, defined each year (i.e. patent analysis, due diligence, social network analysis for business). It provides many tools and techniques; HBS cases are used. Students will apply these tools in groups when analysing a preselected case company. They are expected to present early stage versions of their CI reports and, in the final workshop, they will present the results of their CI analysis, which is then discussed in plenary.
Summer school on Data Science
Provided by:
CNR
Lecturers:
(vari) TBD
Semester:
2
Survey Methods
Provided by:
UNIPI
From:
M.Sc. Economics - Official Statistics
Lecturers:
Monica PRATESI (coord.)
Hours:
40
Technologies for web marketing
Provided by:
UNIPI
From:
M.Sc. in Data Science and BI
Sede:
Polo Fibonacci, University of Pisa
Lecturers:
Salvatore RUGGIERI
Semester:
2
Hours:
48
Exam:
Y
Timetable:
https://www.di.unipi.it/en/education/mds/timetable-mds
Educational Goals:
The student who completes the course successfully will have a solid knowledge about information technologies for marketing decisions in the web, on how to market effectively, on how to being truly connected with customers, on how to improve the customer experience on a web site, on how to invest available resources, and on how measure success using web marketing technologies. The student will be able to understand and classify the large number of problems that arise in the application field of web marketing. The student will have be aware of the many privacy and legal issues related to web tracking, user profiling, and to the application of advertising, personalization and social media marketing strategies.
Prerequisites:
Some knowledge of how the Internet as a network, and some Internet programming (HTML, Javascript). Students must be fluent in English (the course is part of a Master degree held in English).
Text Analytics and Opinion Mining
Provided by:
UNIPI, CNR
From:
Master II Livello Big Data Analytics
Sede:
Computer Science Department, Unversity of Pisa
Lecturers:
Andrea ESULI
Semester:
2
Hours:
20
Timetable:
TBA
Educational Goals:
This module introduces the main methods of analysis and mining of opinions and personal evaluations for users based on Big Data generated on the web or other sources. Emphasis will be put on text mining method applied to text originated on social media. Lessons will be supported by case studies developed in the SoBigData.eu lab.
Topics in Statistical Learning
Provided by:
S.ANNA
From:
Altro PhD (Economics)
Lecturers:
Francesca CHIAROMONTE
Semester:
2
Hours:
30
Exam:
Y
Educational Goals:
This course will introduce the students to a number of topics in contemporary Statistical Learning selected from the following list: (i) Unsupervised classification; Clustering methods. (ii) Unsupervised dimension reduction; Principal Components Analysis and related techniques. (iii) Supervised classification methods. (iv) Linear and generalized linear models. (v) Non-parametric regression methods. (vi) Resampling methods, Cross Validation, the Bootstrap and permutation-based techniques. (vii) Feature selection and penalized fitting techniques for (generalized) linear models (viii) Supervised dimension reduction; Sufficient Dimension Reduction and related techniques. Compared to traditional courses on regression, linear models and multivariate statistics, the focus will be on analyzing actual datasets of interest to the students through group projects. We will also attempt to utilize a so-called active learning approach, leveraging publicly available MOOC materials.
Prerequisites:
A working knowledge of basic statistical inference procedures (point estimation, confidence intervals, testing) and regression modeling.
Visual analytics
Provided by:
UNIPI, CNR
From:
M.Sc. in Data Science and BI
Sede:
Polo Fibonacci, University of Pisa
Lecturers:
Salvatore RINZIVILLO
Semester:
2
Hours:
20
Timetable:
https://www.di.unipi.it/en/education/mds/timetable-mds
Web Mining
Provided by:
UNIPI, CNR
From:
Master II Livello Big Data Analytics
Sede:
Computer Science Department, Unversity of Pisa
Lecturers:
Raffaele PEREGO, Franco Maria NARDINI
Semester:
2
Hours:
20
Timetable:
TBA
Educational Goals:
This module presents how to analyse traces that users leave from querying Web search engines (query log). It presents the main applications of Web mining including: i) how to profile the interests/activities of users, ii) how to use information from query logs for forecasting social indicators and optimizing Web search engines. Teaching activities will be supported by several case studies developed in the SoBigData.eu laboratory.
