Statistics for Big Data for Economics and Business

A.Y. 2019/2020
Lesson for
6
Max ECTS
40
Overall hours
SSD
SECS-S/03
Language
Italian
Learning objectives
This course aims at introducing and illustrating specific statistical, IT and machine learning methodologies for the analysis of Big Data in economic, business and financial applications. The course will focus mainly on the Python programming language, which is by far the most used in Big Data applications, but some parts will be devoted to the R language and other more classical languages such as Java. On the statistical side, supervised and unsupervised statistical learning themes will be proposed with some reference to Bayesian statistics.
At the end of the course, students will have acquired adequate statistical and programming skills allowing for mastering the tools necessary for the analysis of Big Data and the extrapolation of information of interest in the economic, business and financial fields.

Course structure and Syllabus

Active edition
Yes
Responsible
SECS-S/03 - ECONOMIC STATISTICS - University credits: 6
Lessons: 40 hours
Professor: Manzi Giancarlo
ATTENDING STUDENTS
Syllabus
PART ONE :

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


2) DATA MINING TECHNIQUES 2: unsupervised methods
2.1 cluster analysis
2.2 Principal Component Analysis
2.3 cross-validation
2.4 text mining

PART TWO:

1) Introduction to programming in R and Python
2) Data mash up techniques
3) Cloud computing techniques
4) Web scraping techniques
5) Relational and non-relational databases
6) Big data analytics
NON-ATTENDING STUDENTS
Syllabus
PART ONE :

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


2) DATA MINING TECHNIQUES 2: unsupervised methods
2.1 cluster analysis
2.2 Principal Component Analysis
2.3 cross-validation
2.4 text mining

PART TWO:

1) Introduction to programming in R and Python
2) Data mash up techniques
3) Cloud computing techniques
4) Web scraping techniques
5) 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
Professor(s)
Reception:
Wed 4.30PM-7.30PM.
Room 37, 3rd Floor.