Python Course for Data Science (M. Puliga):

- Introduction to the language: basic statements, cycles and functions

- Diving into the language: advanced types: sets and dictionaries, classes and modules, using PIP and ipython

- Scraping the web: introduction to BeautifulSoup, the regular expressions module re, the request module

- Introduction to Plotting: basic numpy, plotting overview

- Data science utilities: introduction to SQL (sqlite/mysql)

Getting, Organizing and Analyzing the Data (A. Petersen):

- a crashcourse in scraping and parsing XML based websites (in mathematica & python)

- a crashcourse on using Gephi (open source network visualization program)

- a crashcourse on data organization, processing, analysis and visualization

Multivariate Statistical Analysis (M. Di Lascio):

This part of the course aims at introducing the most important and widespread multivariate analysis methods. After introducing multivariate random variables and multivariate data, the course focuses on the study of the relationships between variables and on the study of the similarities between units. In particular, the course deals with:

- dimension reduction methods (principal component analysis and factor analysis);

- analysis tools for linear and nonlinear multivariate dependence (canonical correlation analysis and copula models); distance?based and model?based clustering techniques.

Causal Inference and Evaluation Methods (F. Mealli):

This part of the course deals with statistical methods for inferring causal effects from data from randomized experiments or observational studies. Students will develop expertise to assess the credibility of causal claims and the ability to apply the relevant statistical methods for causal analyses.

Prerequisites: Lin. Alg. + Opt. Control + Found. of Prob. & Stat. Inf.

- Introduction to the language: basic statements, cycles and functions

- Diving into the language: advanced types: sets and dictionaries, classes and modules, using PIP and ipython

- Scraping the web: introduction to BeautifulSoup, the regular expressions module re, the request module

- Introduction to Plotting: basic numpy, plotting overview

- Data science utilities: introduction to SQL (sqlite/mysql)

Getting, Organizing and Analyzing the Data (A. Petersen):

- a crashcourse in scraping and parsing XML based websites (in mathematica & python)

- a crashcourse on using Gephi (open source network visualization program)

- a crashcourse on data organization, processing, analysis and visualization

Multivariate Statistical Analysis (M. Di Lascio):

This part of the course aims at introducing the most important and widespread multivariate analysis methods. After introducing multivariate random variables and multivariate data, the course focuses on the study of the relationships between variables and on the study of the similarities between units. In particular, the course deals with:

- dimension reduction methods (principal component analysis and factor analysis);

- analysis tools for linear and nonlinear multivariate dependence (canonical correlation analysis and copula models); distance?based and model?based clustering techniques.

Causal Inference and Evaluation Methods (F. Mealli):

This part of the course deals with statistical methods for inferring causal effects from data from randomized experiments or observational studies. Students will develop expertise to assess the credibility of causal claims and the ability to apply the relevant statistical methods for causal analyses.

Prerequisites: Lin. Alg. + Opt. Control + Found. of Prob. & Stat. Inf.