Using ICTs in education allows the effective integration of new technologies into the educational process. Developing information and communication technologies also require timely changes in knowledge evaluation and use. As a result, methods, techniques, and technologies used in learning are being updated. In this study, a method for developing an automated educational and methodical complex based on a semantic network is described. Requirements for such educational and methodological complex have been formulated. Structural and formal models of tools based on a semantic network have been presented. The purpose of developing the model is to make it easier for students to perceive the various educational and methodological complexes through its compilation on the basis of a holistic view of the subject area, as well as to optimize the work of the teacher.

Бұл мақалада Python және Pandas кітапханаларын деректерді тиімді өңдеу және талдау үшін, әсіресе Интернет заттар (IoT) контекстінде қолдану қарастырылады. Python икемді және танымал программалау тілі, Ол оқылуы мен бай кітапхана экожүйесінің арқасында деректерді талдау үшін сәтті қолданылады. Pandas кітапханасы деректерді басқаруды жеңілдететін DataFrame және Series сияқты деректер құрылымдарын ұсынады. Мақалада Python және Pandas қолданудың теориялық негіздері мен практикалық аспектілері қамтылған: деректерді жүктеу мен тазалаудан бастап оларды түрлендіруге және талдауға дейін. Нәтижелерді визуализациялауды қоса, деректермен жұмыс істеу әдістерін көрсететін код мысалдарына ерекше назар аударылады. Сондай-ақ, бұл құралдарды IoT контекстінде қолдану нәтижелері және олардың технологияның дамуына әсері талқыланып, болашақ зерттеулерге бағыттар ұсынады. Бұл мақала деректерді талдау процестерін жақсарту және IoT шешімдерін әзірлеу үшін Python және Pandas қолданбаларын пайдаланғысы келетін деректер мамандары, талдаушылар және әзірлеушілер үшін құндылық болып табылады.

Machine-learning surrogates predict composite properties but report no calibrated reliability and may violate physics. Bound-constrained conformal prediction confines intervals to physical bounds but assumes them exact. This paper keeps the corridor as a hard constraint and characterizes what a decision-support system guarantees when it is wrong. If a fraction $\eta$ of responses lies outside the corridor, no admissible interval covers more than $1-\eta$, so the nominal level $1-\alpha$ is attainable only if $\eta \le \alpha$. A projection-aware calibration scores out-of-corridor points as infinitely nonconforming. Its marginal coverage is at least $1-\alpha -\eta {\pi }_{\infty }$ at every calibration size (${\pi }_{\infty }$ the fallback probability); its coverage across calibrations follows an exact Beta law. In the limit it reaches the nominal level whenever attainable; calibrate-then-clip loses up to $\eta$. For $\eta <\alpha$ the system thus delivers admissible intervals at the nominal level for a small, quantified width premium, with a feasibility verdict and a Shapley explanation. Across eight datasets it recovers the nominal level on a woven laminate with a misspecified rule-of-mixtures bound (90.3% against 85.6% for naive projection) and tracks the frontier under normative bounds the data contradict. Under exact Voigt–Reuss bounds it removes all violations and narrows intervals thirteen-fold. Width, paid inside the corridor, is the price.

This article examines the latest developments in the field of storage, execution and implementation of applications based on the algorithms of the Internet of Things (IoT) and artificial intelligence (AI). Given the exponential growth in the number of sensors connected to networks and the generation of large amounts of data, the article discusses the main topics of artificial intelligence, including a brief history of the development of artificial intelligence, machine learning concepts, types of problem solving tasks, ensemble methods, deep learning, quality indicators and other fundamental areas. The second part of the article provides examples of computer programs demonstrating the implementation of various AI algorithms in the Google Colab interactive environment using the Python programming language. It includes code snippets for basic artificial intelligence tasks such as classification, regression, data normalization and scaling, pattern recognition, forecasting, and more.

Differential characteristics of survey are: the number of respondents, fairness and objectivity of received information; the purpose of the survey. The probabilistic mechanism of selection and special mathematical procedures, that provide the best objectivity, are rightful. Application of Likert scaling methods, factor analysis at determining the peculiarities of prospective students’ choice of higher educational establishments are studied in this article. The questionnaire was made according to received information. Respondent had to express the importance of each item on the six-point scale from “absolutely not important” to “very important”. Research objective was the description of the methodology, which allowsto consider the peculiarities of prospective students’ choice of higher educational establishments and analyzing actions of prospective students on the basis of beforehand included criteria. Research included preparatory, operational and resulting stages. Components that influence prospective student’s choice of higher educational establishment were found out at the first stage by means of factor analysis. In addition to these components as a result of factor analysis procedure it is now possible to develop a variety of research hypotheses. Their proof or refutation is an interesting application task that can be solved on closer detailed analysis of already gained statistic data or the pursuance of additional research. The feasibility of batch processing in SPSS was used to speed up the analysis process.