The article discusses the sources and types of data used to create a digital student profile, as well as possible ways of using them in educational analytics. A digital profile is a comprehensive description of a student's academic, behavioral, and social characteristics collected from various sources. The data coming from educational institutions' information systems, social networks, instant messengers, mobile applications, video content platforms, questionnaires, and video cameras are analyzed. The importance of a digital profile is due to its ability to support personalization of learning and improve the efficiency of educational processes. The article highlights numeric, categorical, binary, ordinal, and unstructured data types, as well as metadata and derived data used for data analysis in DataScience and machine learning algorithms. Examples include grades, participation in educational events, social activity, preferences, text comments, and video recordings. Attention is also paid to the analysis of possible ways of using this data to predict academic performance, identify learning difficulties, and assess student engagement and motivation.

Keywords: digital student profile, educational analytics, data types, data sources, data analysis, personalization of learning, machine learning in education, datascience, educational data mining, crisp-dm, semma

The article deals with the design of experiments for dispersed particles capturing in elektrocyclone. The autors gives planning matrix calculation, the regression equation based on the aerosol velocity and the initial concentration of the aerosol and plot of function.

Keywords: gas cleaning; cyclone; elektrocyclon; fly ash