40720 - Data Mining

Learning outcomes

The integrated course Data Analysis for Managerial Decision-Making provides students with the theoretical and practical skills necessary to analyse and interpret empirical data using econometric methods. Once the basics have been consolidated, more advanced methods are introduced, focusing on panel data, which is now a cornerstone of modern economic analysis. The main objective of the course is to provide students with the techniques necessary to conduct rigorous empirical research, in which critical thinking, knowledge of econometric software and data-driven decision-making enable them to tackle complex economic and sustainability challenges in managerial contexts.

Course contents

The course introduces the software and analytical tools commonly used in empirical data analysis and provides students with extensive hands-on experience through practical applications.

The initial steps to get the class up to speed include

1. Review of the fundamental concepts of descriptive statistics and hypothesis testing. Introduction to Stata programming.

2. Simple linear regression and multiple linear regression: OLS estimation, inference, model comparison, residual analysis, inclusion of categorical variables in the model. Advancement in Stata programming.

Readings/Bibliography

The material (articles, commented notes & slides, programs and data-sets) will be distributed during the lectures and make available on the platform Virtuale.
The reference textbook is:
Wooldridge J.M. 2020 Introductory Econometrics. A Modern Approach, Cengage, 7th Edition, chapters 1, 2, 3, 4, 6, 7, 8.

For an overview of Stata: Baum, C. F. (2006) An Introduction to Modern Econometrics Using Stata, Stata Press. Why programming in Stata? Have a look at Cox N. J. (2001) Speaking Stata: How to repeat yourself without going mad, The Stata Journal, 1, Number 1, pp. 86–97.

Teaching methods

To ensure a smooth transition from theory to practice in econometrics, theoretical lectures are combined with working sessions. During the practical empirical applications, students will use Stata throughout practical laboratory sessions.

At the end of the course, students will be able to critically evaluate articles that present basic empirical analyses and to model and estimate their own regression of interest, using the most appropriate methods according to the problem they face.

Assessment methods

The integrated course Data Analysis for Managerial Decision-Making consists of two modules:

Data Mining
Econometrics for Management

Each module includes

• a research assignment (continuous assessment);
• an individual written examination.

The final grade for the integrated course is calculated only after both modules have been successfully completed, according to the weighting established for each module.

Continuous assessment
Students may choose to participate in the continuous assessment offered during the teaching period.

The continuous assessment consists of one empirical project, to be completed individually or in groups of up to four students, and accounts for 40% of the final grade for the respective module.

The project is designed to reinforce the concepts covered during the lectures through the analysis of empirical data using Stata. Students are expected to justify their methodological choices, interpret their results, and critically discuss their findings.

The project forms an integral part of the learning activities carried out during the course. Consequently, it is valid only for the academic year in which it is assigned. 

For the Data Mining module, the project must be submitted and assessed no later than the March examination session immediately following the end of the course.

Students enrolled in the integrated course should refer to the Econometrics for Management syllabus for the corresponding deadline applicable to that module.

After the relevant examination session, the continuous assessment expires and cannot be carried forward to subsequent examination sessions or academic years.

The use of Artificial Intelligence tools (e.g. ChatGPT, Claude, Gemini, Copilot or similar systems) is permitted as a support for learning. Students remain fully responsible for the submitted work and must disclose any AI assistance. Reports produced with AI support must include a brief appendix describing:

the AI tool(s) used;
the prompts or prompting strategy employed;
how the AI-generated output was used;
a critical evaluation of its usefulness, accuracy and limitations.

The purpose of allowing AI tools is to develop students' ability to critically evaluate, verify and improve AI-generated suggestions through independent econometric reasoning, rather than replace their own analysis.

Failure to disclose AI assistance may be treated as a breach of academic integrity.

All empirical assignments must be fully reproducible. Students are expected to submit the complete Stata do-files required to generate all tables, figures and results presented in their reports.

Written examination
The written examination accounts for 60% of the final grade for students who complete the continuous assessment.

It is delivered through the EOL platform and consists of the interpretation of Stata output together with open-ended questions assessing the student's understanding of regression analysis, hypothesis testing and the data analysis techniques covered during the course.

Students who do not complete the continuous assessment within the prescribed period, or who choose not to participate in it, will be assessed through a written examination covering the entire syllabus of the Data Mining module. In this case, the written examination determines 100% of the final grade for the module.

The final grade may be:
30 cum laude outstanding performance demonstrating complete mastery of the subject together with excellent analytical and interpretative skills.
28-30 excellent knowledge of the subject and very good analytical skills.
24-27 good knowledge of the subject with appropriate methodological understanding.
18-23 satisfactory performance despite theoretical or methodological weaknesses.
<18 insufficient achievement of the learning outcomes.

Teaching tools

Theoretical lectures are complemented by practical laboratory sessions, during which students receive guidance on implementing empirical analyses using Stata. Datasets and programming files required to perform the empirical applications will be provided during the course. All teaching materials, including slides, datasets, programming files and supplementary notes, will be made available on the Virtuale platform.

A Microsoft Teams virtual classroom will also be available for students who are exceptionally unable to attend a lecture in person and for communication outside class.

Stata software: students can access Stata free of charge through the University CAMPUS licence using their institutional credentials:
https://www.unibo.it/secure/software-stata/

Office hours

See the website of Maria Elena Bontempi

See the website of Federica Galli