B2022 - PYTHON FOR ECONOMISTS

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 students to the basic logic and syntax of Python, including core programming concepts and object-oriented programming.

The second part centers on computational methods for extracting information from large text datasets. Topics include natural language processing (NLP), machine learning for text, and text classification. Both conceptual understanding and hands-on implementation are emphasized throughout.

Students will apply these techniques to real-world economic problems through practical exercises, case studies, and a final project. By the end of the course, students will have acquired the skills necessary to analyze and interpret large-scale textual data for research in economics.

Readings/Bibliography

There is no single textbook for this course. All required readings and teaching materials (slides, notes, exercises, etc.) will be made available on the university platform VIRTUALE.

Below is a partial list of references that will be covered—either fully or in part—during the course. This list may be updated before and/or during the semester:

• Bird, S., Klein, E., & Loper, E. (20XX). Natural Language Processing with Python (available online).

• Jurafsky, D., & Martin, J. H. (2023). Speech and Language Processing, 3rd Edition (available online).

Teaching methods

Frontal lectures and discussions in class.

Assessment methods

The final grade is assigned on a 0–30 cum laude scale and is based on the following components:

• Research proposal: worth up to 15 points. Depending on the number of enrolled students, the proposal may be developed individually or as a group project. It is due on a date to be determined, likely between the end of March and early April.

• Oral presentation: worth up to 15 points. Each proposal will be presented in class, either individually or as a group, depending on the project format.

• Active class participation: worth up to 4 bonus points.

Teaching tools

Slides, lecture notes, and readings will be made available on the university platform VIRTUALE.

Office hours

See the website of Alessandro Saia