Corsi di Laurea Corsi di Laurea Magistrale Corsi di Laurea Magistrale
a Ciclo Unico
Scuola di Scienze
SCP7078720, A.A. 2017/18

Informazioni valide per gli studenti immatricolati nell'A.A. 2017/18

Principali informazioni sull'insegnamento
Corso di studio Corso di laurea magistrale in
SC2377, ordinamento 2017/18, A.A. 2017/18
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Crediti formativi 12.0
Tipo di valutazione Voto
Sito della struttura didattica
Dipartimento di riferimento Dipartimento di Matematica
Obbligo di frequenza No
Lingua di erogazione INGLESE
Corso singolo È possibile iscriversi all'insegnamento come corso singolo
Corso a libera scelta È possibile utilizzare l'insegnamento come corso a libera scelta

Altri docenti ARMIR BUJARI INF/01

Dettaglio crediti formativi
Tipologia Ambito Disciplinare Settore Scientifico-Disciplinare Crediti
CARATTERIZZANTE Tecnologie dell'informatica INF/01 6.0
CARATTERIZZANTE Tecnologie dell'informatica ING-INF/05 6.0

Modalità di erogazione
Periodo di erogazione Primo semestre
Anno di corso I Anno
Modalità di erogazione frontale

Organizzazione della didattica
Tipo ore Crediti Ore di
Ore Studio
LEZIONE 12.0 96 204.0 Nessun turno

Inizio attività didattiche 02/10/2017
Fine attività didattiche 19/01/2018

Commissioni d'esame
Nessuna commissione d'esame definita

Prerequisiti: The student should have basic knowledge of programming and algorithms.
Conoscenze e abilita' da acquisire: This class teaches the concepts, methods, and technologies at the basis of storage, networking, and processing of data and big data. Concerning storage, the basics of relational databases are introduced, followed by a review of non-relational solutions typically adopted for big data. Basics of systems for storage of streams of data are presented as well. The part concerning networking provides an introduction to fundamental concepts in the design and implementation of computer communication networks, their protocols, and applications. Topics covered in this part include: layered network architecture, data link protocols, network and transport protocols and applications. Examples will be drawn from the Internet TCP/IP protocol suite. After that, advanced and emerging networking paradigms aimed at addressing QoS and engineering flexibility of current infrastructure networks are introduced. Topics covered range from software defined networking to cloud provisioning schemes and datacenters. The programming part focuses on programming for data scientists using Python, starting from the description of its interactive computational environment, and continuing with storage, data manipulation, and visualization.
Modalita' di esame: The student is expected to pass a written and an oral exam.
Criteri di valutazione: The written and the oral exams will be evaluated on the basis of the following criteria: i) student’s knowledge of the concepts, methods, and technologies at the basis of the topics covered in the course; ii) student’s capacity for synthesis, clarity, and abstraction.
Contenuti: The course will cover the topics listed below:
- Databases
Introduction to relational databases: data model; relational algebra; SQL; DBMS;
NoSQL technologies: characteristics of NoSQL databases; aggregate data models: key value stores, document databases, column family stores, graph databases, others; distribution models: sharding, replication (master-slave,peer-to-peer).
Streams of Data: architecture(s); data modeling; query processing and optimization.
Networking Fundamentals: Network architectures (OSI Model); TCP and UDP Transport layer protocols; IP Addressing and Routing; Link Layer Forwarding; DNS and DHCP.
Advanced Networking: Virtual LAN (VLAN) and Virtual eXtensible Lan (VXLAN), Software Defined Networking: control, data plane and virtualization; concepts on Cloud Computing: service and deployment models: data centers architectures, topologies, addressing, routing, traffic characteristics; Case Study: The Web of Things (IoT standards and protocols).
- Programming
Programming for Data Scientist using Python: computational environment (IPython and Jupyter); storage and manipulation (NumPy and Pandas); data visualization (Matplotlib).
Attivita' di apprendimento previste e metodologie di insegnamento: The course consists of lectures.
Eventuali indicazioni sui materiali di studio: Slides presented during the lectures are made ​​available to students as reference material.
Testi di riferimento: