Servizi per la didattica
PORTALE DELLA DIDATTICA

Data science for networks

01UJARV

A.A. 2019/20

Lingua dell'insegnamento

Inglese

Corsi di studio

Dottorato di ricerca in Ingegneria Elettrica, Elettronica E Delle Comunicazioni - Torino

Organizzazione dell'insegnamento
Didattica Ore
Lezioni 20
Docenti
Docente Qualifica Settore h.Lez h.Es h.Lab h.Tut Anni incarico
Vassio Luca   Assegnista di Ricerca   15 0 0 0 1
Vassio Luca   Docente esterno e/o collaboratore   15 0 0 0 1
Collaboratori
Espandi

Didattica
SSD CFU Attivita' formative Ambiti disciplinari
*** N/A ***    
2019/20
PERIOD: MAY - JUBE - JULY In this course, we will explore data analysis and prediction topics using complex network datasets. In particular, our focus will be on temporal and structural data. We will exploit real complex networks from popular repositories. The course will cover the following topics: introduction to network science, probabilistic network models, graph visualization techniques, random walks over graphs, cascades and time series. During each class, we will see examples using Python programming language and each student will execute some small programming assignment and data analysis and visualization, possibly using data from their personal research.
PERIOD: MAY - JUBE - JULY In this course, we will explore data analysis and prediction topics using complex network datasets. In particular, our focus will be on temporal and structural data. We will exploit real complex networks from popular repositories. The course will cover the following topics: introduction to network science, probabilistic network models, graph visualization techniques, random walks over graphs, cascades and time series. During each class, we will see examples using Python programming language and each student will execute some small programming assignment and data analysis and visualization, possibly using data from their personal research.
• Intro to network science • Algorithms over graphs • Graph visualization techniques • Random walks • Probabilistic network models • Cascades and time series
• Intro to network science • Algorithms over graphs • Graph visualization techniques • Random walks • Probabilistic network models • Cascades and time series
• 26/05 9:00-12:00 • 09/06 9:00-12:00 • 16/06 9:00-12:00 • 23/06 9:00-12:00 • 30/06 9:00-12:00 • 7/07 9:00-12:00 • 14/07 9:00-11:00
• 26/05 9:00-12:00 • 09/06 9:00-12:00 • 16/06 9:00-12:00 • 23/06 9:00-12:00 • 30/06 9:00-12:00 • 7/07 9:00-12:00 • 14/07 9:00-11:00
Modalitΰ di esame:
Exam:
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