CLEC/M

Language:

Italian
Textbooks:

Course slides.
James G, Witten D, Hastie T, Tibshirani R (2021). An Introduction to Statistical Learning with Applications in R. Springer.

Further readings:
Giudici P, Figini S (2009). Applied Data Mining for Business and Industry. Wiley
Ledolter J. (2013). Data Mining and Business Analytics With R. Wiley
Shmueli G, Bruce PC, Yahav I, Patel NR, Lichtendahl KC, Jr. (2018). Data Mining for Business Analytics. Wiley
Learning objectives:

The course of data science in economics aims to contribute to the student's learning process by providing methodologies for quantitative analyses that are useful for economic and business decisions.
In particular, these learning objectives can be associated with the following expected learning outcomes:

Knowledge and understanding
The course aims to provide the basic methodological and application knowledge of data science. Furthermore, we want to provide useful tools for the statistical analysis of certain types of economic and business data. Finally, great attention will be given to the open source statistical package R.

Applying knowledge and understanding
At the end of the course, the student will also be able to analyze data- bases, including large ones, with modern statistical techniques, with the help of case studies carried out with the statistical software R. The acquired knowledge will allow him/her to critically interpret the economic and / or business dynamics.
Prerequisite:

The knowledge of Basic Statistics is requested.
Teaching methods:

Lectures.
R practice and exercises.
Exam type:

Knowledge and understanding
The verification of the learning outcomes will be carried out through a written and oral examination. The written exam will cover the whole program with particular attention to the use of R software. Students will also have to prepare and discuss some statistical analyses, carried out with R, concerning real case studies (data sets can be found on the internet ). These documents must be sent to the Professor at least one week before the exam date.
The score of the exam is assigned by a vote expressed in 30.

Applying knowledge and understanding
During the exam and the development of the applied work, students' ability to apply the knowledge of data science models is verified to be able to face concrete analysis situations.
Sostenibilità:

The course presents some empirical exercises relating to some of the Sustainable Development objectives of the UN Agenda 2030. In particular, issues related to the objectives are addressed in the course: -Goal 8: Promote inclusive and sustainable economic growth, employment and decent work for all;
-Goal 10: Reduce inequality within and among countries;
-Goal 12: Ensure sustainable consumption and production patterns.
Agenda 2030 Objectives for sustainable development:
Further information:

E-mail: postigli@unich.it
For further details and for downloading the slides: fad.unich.it, page of Data Science in Economia
In the first semester the Professor meets students only by appointment (postigli@unich.it).
In the second semester, the office hours for students is scheduled for Friday from 11:00 to 13:00, green stair, floor 2, Viale Pindaro, 42.

1. Introduction to R statistical software. Explorative analysis with R.
2. Statistical methods for the analysis of economic data:
2.1 Multiple linear regression with R.
2.2 Multiple linear regression for spatial data with R.
2.3 Logistic regression model with R.
2.4 Principal Component Analysis with R. 2.5 Decision trees with R.

1. Introduction to R statistical software. Explorative analysis with R.
Using R as calculator. Explorative analysis for univariate and bivariate data.
2. Statistical methods for the analysis of economic data:
2.1 Multiple linear regression with R.
Specification of the model. Estimate and Hypothesis testing. Goodness of fit. Using R for multiple linear regression..
2.2 Logistic regression model with R.
Qualitative response variable. Estimate and interpretation of the results. Using R for logistic regression.
2.3 Multiple linear regression for spatial data with R.
Specification of different spatial models. Estimate and interpretation. Using R for spatial regression.
2.4 Principal Component Analysis with R.
Definition and derivation of principal components. Interpreting Principal Components. Using R for Principal Component Analysis.
2.5 Regression and classification trees with R.
Definition of the problem. Division criteria. Pruning. Bagging and Random forest. Using R for regression trees.