Course Descriptions

The course introduces the main theories which can be applied to the explanation of political and social phenomena. You will then discover how these theories can be differently applied in diverse models designed to unravel the core up-to-date political problems. The knowledge of the most important theories and models will create the necessary basis for further skills acquisition in the following courses.

This course introduces the basics of social theory construction and model building. It starts with the notion of causal thinking and further develops the understanding of model thinking as such. The course is built upon the methodological paradigm of structural individualism: the macro-phenomena should be always studied as the outcomes of the actions and interactions of actors on the micro-level. This will be our key to understanding how social and political processes emerge and develop. We will cover the main mechanisms that can be applied for the explanation of social and political phenomena (i.e., belief-, opportunity-, and desire-based mechanisms), and build our first theoretical causal models explaining real and actual examples – from (anti)vaccination to the current riots and social movements.

This course is central to the program and is designed to develop both theoretical knowledge and empirical skills of social and political data analysis. You will learn the concept of social and political attitudes, their role in social and political theory, and discover main approaches to the development of attitudinal scales and their further statistical analysis – validation, assessment, and interpretation in the comparative perspective. You will use these concepts and methods in the analysis of social and political processes in Europe and other regions of the world with cross-country comparative perspective. The course will be built around the particular cases in accordance with research interests of the students, and result in a group project-based work on specific case-studies. The course will co-taught by a faculty team with complementing expertise. We hope that for some students their project work will lead to publications.

This course will teach you the main methods of data preparation and data analysis. You will be introduced to the principles of critical data analysis focused on the study of cultural, ethical, and socio-technical problems at the intersection of social sciences, informatics, and society. Besides learning the methods of data analysis as such, students will develop a critical approach to such topics as big data, data science, data ethics and privacy, and analyzing how data systems and algorithms can help to solve social problems.

Agent-based modeling (ABM) uses computer simulations, in which complex phenomena arise from the actions of individual agents. The model helps either explain social processes or assess the outcomes of social policies through artificial experiments. In the basic course, we will focus on computational modeling of social behavior, using NetLogo language. You will learn to develop the model setup with modifiable parameters, wrap agents' decision-making in computer code, conduct simulation experiments, and statistically analyze the results.

The course will cover both the applications of social network analysis (SNA) to various domains of social and political life and the basic concepts and techniques in SNA as a quantitative method. Practical hands-on sessions will teach the use of Gephi and other programs to analyze and visualize networks, and seminars will focus on discussing research papers with good analytical examples of SNA use.

This course offers a toolbox for mining big textual data, for example, all publications in a large national media outlet in a given year or a set of parliament inquires for several years. Students will go from analyzing basic word statistics and co-occurrence of terms to document classification, topic modeling, and applying word embedding and clustering methods to such downstream tasks as sentiment analysis and ideological scaling. Advanced students will work with neural networks and contextualized word-embeddings (GPT-2/3, BERT, etc.). Besides gaining practical knowledge, you will learn the possibilities of the automated text analysis as well as its pitfalls and important caveats about applying statistical tests to language data.

The course discusses digital transformation in public administration and governance, starting from the early attempts to introduce e-government across the globe to the recent developments in policy informatics and algorithmic governance. The students will get acquainted with the trends in e-governance and e-participation development from the perspectives of Political Science and Public Administration. The course also deals with the issues of ethics, as well as the impact of digital technologies on political dynamics and governance.

This course focuses on predictive data mining techniques. Its purpose is to educate you in the understanding and application of predictive statistical techniques, both supervised (classification and regression) and unsupervised (clustering) methods. The course includes the use of tools for predictive data mining in Python.

 During this course we will explore how causal relationships can be established in the social sciences, primarily in political science. Often quantitative data are either unavailable or do not allow conclusions to be drawn beyond the correlation between the factors of interest. Unfortunately, correlation does not equal causality. Experimental methods allow us to make claims that A causes B, not just that A is related to B. In addition, there are many cases where empirical data simply cannot be reliably observed or collected: this includes such things as corruption, propensity to cheat, or electoral fraud. During the course we will discuss in particular how these sensitive topics can be studied through laboratory or online experiments. The course involves independent or group research work: the main part of the course reporting will be a research paper where an original research question is supposed to be answered based on an experimental design. The most advanced experimental designs will be able to be implemented and we will collect data for them either online or in the lab.