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Abstract
This article examines computational social science (CSS) as one of the key projects transforming contemporary sociology in the context of digitalization and the exponential growth of available data. The authors trace the development of CSS, outlining its methodological foundations and core research tools, including social network analysis, natural language processing, causal inference, agent-based modeling, machine learning, and large language models.
Particular attention is given to the critique of the computational approach, including the risks of reducing social phenomena to data, the bias of research agendas toward available data sources, and the weakening of theoretical reflection and interpretive traditions. In response to these limitations, the article proposes to conceptualize CSS as a synthetic project that combines computational methods with traditional sociological problematics. The authors outline the principles for building such a disciplinary project in the context of Kazakhstan., emphasizing its potential to create a competitive research field and to contribute to global academic debates.