About the experience of using the analytical platform Loginom in data analysis in the process of teaching computer science
DOI: 10.23951/2307-6127-2026-1-56-67
The article presents the experience of using the Loginom domestic low-code analytical platform in the context of teaching computer science to students of non-technical specialties, primarily economists and healthcare managers. The research purpose is the demonstration of the Loginom platform’s capabilities for in-depth analysis of complex datasets, including medical statistics, which traditionally presents certain difficulties due to the heterogeneity and unstructured information. On the example of real data from the 1C database: Healthcare (patient information: gender, age, diagnoses, dates of admission/discharge, etc.) it is demonstrated that the Loginom can be used for descriptive analysis, visualization, clustering and statistical hypothesis testing. The process of statistical data analysis is described in detail, including the identification of patterns related to hospital mortality, as well as an attempt to model the distribution of hospital stay in patients with fatal outcomes. The results of the application of various methods of analysis, such as summary analysis, statistical forecasting, and verification of nonparametric hypotheses, are presented. Special attention is paid to data visualization and clustering, which make complex results more visible and understandable. The authors compare Loginom with other analytical tools and focus on the advantages of using this domestic software using low-code technologies for specialists without programming skills. The article also discusses the problems that arise when using low-code platforms in the educational process, and suggests pedagogical methods for solving them. In conclusion, it is emphasized that the use of Loginom contributes to high motivation, developing students’ research skills and adapting them to their future profession.
Keywords: Loginom, Statistical analysis, import substitution, computer science
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Issue: 1, 2026
Series of issue: Issue 1
Rubric: THEORY AND METHODOLOGY OF TEACHING AND EDUCATION
Pages: 56 — 67
Downloads: 19