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.

This course in advanced computational modeling will give you an opportunity to enhance your skills in three approaches: structural equation modeling, advanced (multilevel) social network analysis, advanced agent-based modeling. You will choose one to study in detail. You will also choose a data set to work with asking focused research questions, may be one that leads to your master thesis or some other interesting work that you would like to pursue. Those who will be interested in Advanced ABM will work on some realistic model of your interest. There we will aim at making realistic and robust simulations: refining agents' decision-making models with insight from social and cognitive sciences, determining model parameters using empirical data or calibration, and empirically validating the model. We hope that for some students their course papers will lead to publications.

This course focuses on developing skills in using Python for real analytical problems. Those students who never encountered Python before will be asked to take introductory course online as a prerequisite. The course covers Python with an emphasis on modern libraries for machine learning, data mining and data analysis.

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.

In this course you learn to explain various social phenomena. In each section of the course you will be presented with a specific type of explanation and discuss empirical research on this topic in a workshop. Each type of explanation will get its empirical illustration, which will help you to assess its strengths and weaknesses. As a result, you will gain in-depth knowledge of political and social issues and the main mechanisms that can be used to explain certain phenomena.

The course will introduce you to the concepts of digital transition, open data, and e-government. We will discuss the main rules for working with open data, systems for protecting personal data of any type. You will learn what you are (and are not) allowed to do with online data, how to store and manage data efficiently, how to retrieve it, and what typical challenges researchers face.

You will learn to formulate effective arguments for or against a particular policy. The course will begin with a discussion about how politics can influence social reality and how we can observe this influence. It then discusses the criteria for success and failure of policies, approaches to assessing specific policies, and the empirical basis for such judgments. You will learn how to analyze the strengths and weaknesses of planned measures based on existing evidence, and how to conduct an informed assessment of the implemented policy.

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.

The course introduces the development of experimental social sciences and behavioral decision-making models. It also aims to teach how to conduct experiments in the laboratory. The course will sequentially consider the main approaches to the experimental study of individual behavior, strategic interaction, alternative models of interactive strategic choice. Examples of the practical application of these methods to the analysis of organizational behavior, political movements, the negotiation process, etc. are considered.