Studying at the University of Verona

Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.

Study Plan

This information is intended exclusively for students already enrolled in this course.
If you are a new student interested in enrolling, you can find information about the course of study on the course page:

Laurea magistrale in International Economics and Business - Enrollment from 2025/2026

The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.

2° Year  activated in the A.Y. 2022/2023

ModulesCreditsTAFSSD
One module between the following
Stage
3
F
-
Final exam
12
E
-
activated in the A.Y. 2022/2023
ModulesCreditsTAFSSD
One module between the following
Stage
3
F
-
Final exam
12
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
Further language skills
3
F
-
Between the years: 1°- 2°

Legend | Type of training activity (TTA)

TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.




S Placements in companies, public or private institutions and professional associations

Teaching code

4S010380

Teacher

Angelo Zago

Coordinator

Angelo Zago

Credits

2

Also offered in courses:

Language

English en

Scientific Disciplinary Sector (SSD)

NN - -

Period

primo semestre (lauree magistrali) dal Oct 4, 2021 al Dec 17, 2021.

Learning outcomes

At the end of this class, the student will know more about how to collect, manage and use data for business analytics purposes, using Excel proficiently:
▪ to use data for descriptive purposes;
▪ to use data for predictive purposes;
▪ to use data for prescriptive purposes.

Program

1. The concept of Business Analytics (what is it?, why is it needed?, what is it good for?, how can we
implement it?
2. Descriptive analytics
2.1. Visualization, exploration and data analysis
2.1.1. Pivot Tables
2.1.2. Dashboards
3. Predictive analytics
3.1.Predictive models (from causal models to time series models)
3.2. Excel tools for predictive analytics with practical international examples (what if analysis, scenario
manager, data tables) using OECD data.
4. Prescriptive analytics
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Periodo di svolgimento: tra fine ottobre e inizio novembre 2021

Destinatari: Studenti del CdLM in IEB; il corso è però aperto anche agli studenti di altri CdLM, fino ad un massimo di 30 partecipanti.

Examination Methods

The recognition of credits is subject to the attendance of the scheduled classes and the passing of a final test consisting of the application of the concepts learned during the module to a database chosen by the student and the preparation of a summary report of the analysis performed (about 2000 words).
--- --- ---
Data proposta per l’appello verbalizzante: 15 gennaio 2022

Impegno orario richiesto: 50 ore

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE