98063 - Managing data to support business activities

Learning outcomes

The course aims to provide the theoretical knowledge and practical competencies related to the management and interpretation of data supporting corporate decision making. In particular, students will have understood how data could help managers analyze consumer behaviour, firms and markets. At the end of the course, students will be able to identify what data are necessary to understand the firm and its context, how it is appropriate to process such data and the limits and attention required to interpret them.

Course contents

Accounting module:

• Corporate equilibria, accountability and financial ratios
• Credit risk measurement in traditional accounting and finance literature: the use of linear discriminant analysis
• Machine learning for financial distress prediction: the use of Artificial Neural Networks

Banking module:

• Linear discriminant analysis: limits and overview of classical insolvency methodologies
• Probit/Logit models
• Regulation of credit risk and insolvency
• Inclusion of sustainability in credit risk management

Marketing module:

• Measurement, errors and data for consumer research
• Primary data collection
• Secondary consumer data
• Web analytics

Readings/Bibliography

Slides and materials provided by professors during the course

Teaching methods

The course teaching methods includes theoretical classes, practical exercises and cases discussion to apply the concepts learnt in class.

Assessment methods

Written exam

Evaluation grid:

<18 insufficient

18-23 sufficient

24-27 average/good

28-30 very good

30 cum laude excellent/outstanding

Teaching tools

Lectures, exercises with data analysis software

Office hours

See the website of Virginia Vannucci

See the website of Enrico Supino

See the website of Giovanni Cardillo