Admission requirements
To ensure active participation and successful completion of the course, students are expected to meet the following prerequisites:
Prior coursework in research methods within political science or a closely related field, with a solid understanding of research design and the logic of empirical inquiry.
A strong grasp of quantitative methods, including familiarity with basic statistical concepts such as regression analysis, hypothesis testing, and data visualization.
Working knowledge of R statistical software, including the ability to import, manipulate, and analyze data using R. While the course will include guided coding sessions, it is not an introductory course in R programming.
Students who are unsure whether their background fits the course expectations are strongly encouraged to contact the instructor prior to registration.
Description
This seminar offers an in-depth introduction to computational methods that are transforming the study of politics and international relations. Designed for Master’s students with an interest in data-driven approaches, the course provides a conceptual and practical foundation in techniques such as supervised and unsupervised machine learning, natural language processing (NLP), network analysis, and predictive modeling. Students will explore how these methods can be used to investigate political phenomena—ranging from conflict forecasting and electoral behavior to the diffusion of ideas and international negotiations.
The course combines theoretical readings with hands-on exercises, encouraging students to critically evaluate existing studies and begin crafting their own computational research ideas. Emphasis will be placed on real-world datasets, ethical implications of algorithmic decision-making, and the strengths and limitations of these methods in political and IR research contexts.
Course objectives
Understand the core principles of computational social science as applied to political analysis.
Explore the use of tools such as text analysis, network modeling, and predictive algorithms.
Evaluate published studies for methodological rigor and ethical integrity.
Build foundational technical skills in R relevant to political data analysis.
Develop a well-scoped research proposal that applies computational methods to a political science or IR question.
Mode of instruction
This course will be taught through a combination of lectures, class discussions, and hands-on lab/workshop activities. Each session is structured to blend theoretical engagement with practical application. Active participation in all components is essential for mastering the course material. Students are expected to come prepared, having completed readings and any assigned exercises in advance.
Assessment method
The final grade for the course is established by determining the weighted average of the following five graded components:
Class Participation (20%)
Active and prepared participation in lectures, discussions, and lab/workshop activities.
In-Class Assignment/Activity (20%)
A graded in-class task (e.g., coding challenge or applied analysis) completed during one of the workshop sessions.
Homework Assignments (15%)
Individual weekly or bi-weekly assignments that test theoretical understanding and practical application of computational methods.
Final Project (20%)
Students will develop and submit a computational research proposal. This will include a clear research question, methodology, dataset (or mock dataset), analytical strategy, and ethical reflection. The final project may be presented in class during the last session.
Presentation (25%)
Students will present their final project.
Reading list
Besides assigned academic articles, I will share my weekly handouts on my GitHub page. I will use different books and articles to teach this course, which will be cited accordingly in my handouts in case you want to spend more time on the topics covered in class. There is not mandatory book purchasing. I recommend “The Elements of Statistical Learning (ESLII)” by Hastie, Tibshirani, and Friedman for a good reference on statistical learning. Thanks to the authors of the book, it is available online for free.
Registration
See 'Practical Information'
Timetable
See 'MyTimetable