В даній роботі на основі поліноміального регресійного аналізу побудовані адекватні математичні моделі виробництва труб на основних підприємствах гірничо-металургійного комплексу України. Це дозволило виконати прогноз розвитку відповідних ланок виробництва. При чому обробка експериментальних даних проводилася на основі використання сучасних засобів інформаційних технологій. Для побудови математичних моделей висувалися гіпотези про поліноміальну залежність експериментальних даних.
The paper considers peculiarities of the material teaching while learning algorithms of data sorting in terms of the “Algorithms and Data Structures” course and the ways to reduce possible complexities while its mastering. Active progress of software and information systems towards the formation of their convergent and hyper-convergent modifications results in rapid development of the amount of all types of interconnected data. Along with that, there is still a constantly reported burning problem as for catastrophic lack of high-skilled specialists in all computing spheres including the data processing one. A competence of IT-specialists is based on the ability of solving various algorithmic problems that they have not met before. One of the most widespread problems is the necessity in data sorting. Consequently, it is proposed to consider the ways to improve algorithmic understanding of sorting methods in terms of the “Algorithms and Data Structures” course and possible methods for reducing the complexity while its studying.
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