ECBA017 - DATA SCIENCE IN ECONOMICS LABORATORY

courses

ID:

ECBA017

Duration (hours):

48

CFU:

SSD:

STATISTICA ECONOMICA

Located in:

PESCARA

Url:

Course Details:

ECONOMICS AND BUSINESS ANALYTICS/CORSO GENERICO Year: 2

Year:

2025

Course Catalogue:

https://unich.coursecatalogue.cineca.it/af/2025?co...

Date/time interval

Secondo Semestre (11/02/2026 - 18/05/2026)

Course Objectives

The training aim for the student is to reach the following learning outcomes:

Knowledge and ability to understand

The course aims to provide methodological and application advances of specific data science methods. In particular, students will analyze some types of economic and business data through specific statistical techniques. We also want to encourage students to specialize in the use of the open source statistical package R.

Ability to apply knowledge and understanding

At the end of the teaching course, the student will also be able to analyze data bases, even large ones, with sophisticated statistical methods, with the help of case studies carried out with the statistical software R. The acquired knowledge will allow him to critically interpret economic and / or business relationships.

Course Prerequisites

The knowledge of Basic Data Science Techniques is requested.

Teaching Methods

Lectures.

R practice and exercises.

Assessment Methods

Knowledge and ability to understand

To verify the learning a test and an interview are scheduled. The test will consist of theoretical questions and empirical exercises on the whole program with particular attention to the use of the software R, simulating some statistical analysis on real case studies. The final evaluation, expressed in thirtieths, takes into account both the test and the interview.

Ability to apply knowledge and understanding

During the test and the interview, students' ability to apply the knowledge of advanced data science models is tested so as to be able to deal with specific case studies.

Texts

Course slides.

James G, Witten D, Hastie T, Tibshirani R (2013). 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

1.Network data Analysis

2. Advanced Regressive Methods

3. Cluster Analysis

4. Time Series

5. Factor Analysis and Structural Equation Models

6. Correspondences Analysis

7. Multidimensional Scaling

Course Language

Italian

More information

E-mail: benedett@unich.it

In the first semester the Professor sees students only by appointment (benedett@unich.it). In the second semester, the office hours for students is scheduled for Wednesday from 4 pm to 6 pm, studio DEC 2 ° floor, Viale Pindaro 42.