The Master of Applied Statistics with Network Analysis is managed by the NRU HSE International Laboratory for Applied Network Research. The Scientific Supervisor of the programme is Anuška Ferligoj, Ph.D., Professor at the University of Ljubljana, the Academic Supervisor of the laboratory. The Academic Supervisor of the programme is Valentina Kuskova, Ph.D., University of Indiana.
The knowledge and skillset obtained by graduates of the programme will render them skilled practitioners, able to apply advanced complex data analysis techniques working in a range of organizations - both in commercial companies operating in various industries (banking, insurance, consulting, IT, medicine, pharmacy) and in research organizations (sociology, marketing).
Relevance and originality of the programme
There is a shortage of specialists in applied statistics, especially in the area of social network analysis. Training in the field of statistics is done in different ways: a lot of educational programmes in this area belong to the field of economics and mostly focus on mathematical methods; in the field of sociology, the study of statistics is limited to the study of probability theory and introductory courses.
This programme is unique because it is the first programme in Russia to offer a comprehensive data analysis approach in different areas. As part of the programme, students from other disciplines can come together to solve practical analytical problems—those mathematically inclined to understand sociology and the object of research. At the same time, those with a background in the humanities will build their skill set and gain a deeper understanding of statistical processes making up the data analysis that we teach. Also, the programme's particular focus will be the analysis of social networks, a direction of data science that is becoming increasingly popular in foreign and Russian research practice.
Another essential characteristic of the programme is its applied nature - students do not learn from abstract theoretical constructs but rather from dealing with specific applied research questions. Students will use their knowledge by solving practical problems, working at the International Laboratory for Applied Network Analysis, Russian analytical centers, and commercial companies.
One of the programme's main goals is to combine a modern approach to data analysis with the theory of social processes. It provides a holistic view of the theoretical and methodological basics, allowing students to select the method best fitted to particular research questions and giving them an understanding of the data structure to combine numerous statistical tools to solve practical problems.
Another important goal of the programme is to train students in the area of social network analysis. In recent years, this data science direction has become more popular and is used in various fields, including Psychology, Sociology, Political Science, Economics, Biology, Computer Science, and Energy.
In a letter to the editorial board of the first issue of the journal 'Network Science,' Stanley Wasserman and colleagues explained Network Studies as a distinct scientific area and cited another well-known network researcher Duncan Watts: 'Networks are important. Therefore, if we do not understand networks, we cannot understand the market's functions, the solution of organizational problems or changes in society' (Network Science, №1, p. 2).
Who will be interested in this programme
The programme is highly relevant to anyone interested in carrying out real-world data analysis.
Sociologists, political scientists, psychologists, and representatives of other social sciences areas will expand their knowledge of statistical tools. They will learn how to use them to solve applied problems in their areas.
For mathematicians, econometrists, and economists who already have a fundamental understanding of statistics and mathematical data analysis have an opportunity for growth. The programme will help develop skills in constructing a theory based on the data model and integrating complex issues and data into the model, enabling them to solve research problems.