- Docente: Marco Rosso
- Credits: 6
- SSD: SECS-P/01
- Language: English
- Teaching Mode: In-person learning (entirely or partially)
- Campus: Bologna
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Corso:
Second cycle degree programme (LM) in
Economics and Public Policy (cod. 6758)
Also valid for Second cycle degree programme (LM) in Applied Economics and Markets (cod. 6756)
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from Apr 14, 2026 to May 13, 2026
Learning outcomes
This course provides theory and tools for using Python as a programming language in economic research. It (i) introduces students to the basic logic and syntax of Python (e.g. object-oriented programming) and (ii) emphasizes how to use it to perform Data Science-related tasks (working with relational dataset, big data, data visualization, among others).
Course contents
This course provides both theoretical foundations and practical tools for using Python in economic research, with a particular focus on text analysis.
The first part introduces the logic and syntax of Python, including core programming concepts and object-oriented programming. The second part covers computational methods for extracting information from large text datasets.
Topics include natural language processing (NLP), machine learning for text, and text classification.
Students apply these techniques to real-world economic problems through practical exercises, case studies, and a final project.
Readings/Bibliography
There is no single textbook for this course. All required readings and
teaching materials (slides, notes, exercises) will be made available on Virtuale.
Key references:
- VanderPlas, J. (2016), "Python Data Science Handbook", O'Reilly (available online).
- Sargent, T. and Stachurski, J., "QuantEcon Lectures" (available online).
- Gentzkow, M., Kelly, B., and Taddy, M. (2019), "Text as Data", Journal of Economic Literature, 57(3), 535–74.
Teaching methods
Frontal lectures and in-class exercises.
Assessment methods
The final grade is assigned on a 0–30 cum laude scale.
Attending students:
- Written research proposal (15 pt);
- Code deliverable: Track A or Track B (10 pt);
- Oral presentation in class (5pt);
- Weekly quizzes on Virtuale (up to 3 bonus pt).
Non-attending students:
- Written research proposal (15 pt);
- Code deliverable: Track A or Track B (10 pt);
- Extended methodology appendix (6 pt).
Students select Track A (corpus building) or Track B (corpus analysis) by end of April. Full assessment criteria are available on Virtuale.
Teaching tools
Slides, lecture notes, and exercises are made available on the university platform Virtuale.
Office hours
See the website of Marco Rosso