Caricamento in corso...
Data science for networks
01UJARV
A.A. 2019/20
Course Language
Inglese
Degree programme(s)
Doctorate Research in Ingegneria Elettrica, Elettronica E Delle Comunicazioni - Torino
Course structure
| Teaching | Hours |
|---|---|
| Lezioni | 20 |
Lecturers
| Teacher | Status | SSD | h.Les | h.Ex | h.Lab | h.Tut | Years teaching |
|---|---|---|---|---|---|---|---|
| Vassio Luca | Ricercatore a tempo det. L.240/10 art.24-B | IINF-05/A | 15 | 0 | 0 | 0 | 1 |
Co-lectures
Context
| SSD | CFU | Activities | Area context | *** N/A *** | 4 |
|---|
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
Modalità di esame:
Exam:
...
Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.
Exam:
In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.