Statistics for Big Data

A.Y. 2018/2019
Lesson for
6
Max ECTS
40
Overall hours
SSD
SECS-S/01
Language
Italian
Learning objectives
Il corso si propone di introdurre ed illustrare specifiche metodologie statistiche, informatiche e di data mining per l'analisi di Big Data. L'implementazione di tali tecniche avverrà mediante l'impiego del software statistico R. Al termine del corso, lo studente dovrà aver acquisito adeguate competenze statistiche e di programmazione che gli consentano di padroneggiare gli strumenti statistici ed informatici necessari per l'analisi dei dati e l'estrapolazione delle informazioni di interesse derivante dai dati stessi.

Course structure and Syllabus

Active edition
Yes
Responsible
SECS-S/01 - STATISTICS - University credits: 6
Lessons: 40 hours
Professor: Manzi Giancarlo
ATTENDING STUDENTS
Syllabus
The course will be organized according to the following topics:

FIRST PART :

1) DATA MINING TECHNIQUES 1: supervised models
1.1 generalized linear models (logit, probit and tobit)
1.2 multilevel models

2) DATA MINING 2 TECHNIQUES: unsupervised models
2.1 cluster analysis
2.2 principal component analysis
2.3 factor analysis
2.4 cross-validation
2.5 text mining

SECOND PART :

1) Introduction to programming in R and Python
2) Data mash up techniques
3) Cloud computing techniques
4) Web scraping techniques
5) Interaction with relational and non-relational databases
6) Big data analytics
NON-ATTENDING STUDENTS
Syllabus
The course will be organized according to the following topics:

FIRST PART :

1) DATA MINING TECHNIQUES 1: supervised models
1.1 generalized linear models (logit, probit and tobit)
1.2 multilevel models

2) DATA MINING 2 TECHNIQUES: unsupervised models
2.1 cluster analysis
2.2 principal component analysis
2.3 factor analysis
2.4 cross-validation
2.5 text mining

SECOND PART :

1) Introduction to programming in R and Python
2) Data mash up techniques
3) Cloud computing techniques
4) Web scraping techniques
5) Interaction with relational and non-relational databases
6) Big data analytics
Lesson period
Third trimester
Lesson period
Third trimester
Assessment methods
Esame
Assessment result
voto verbalizzato in trentesimi
Educational website(s)
Professor(s)
Reception:
Wed 4.30PM-7.30PM.
Room 37, 3rd Floor.