Lykova K.G., PhD
Bunin Yelets State University, Yelets

Shchuchka T.A., PhD, Associate Professor
Bunin Yelets State University, Yelets

Gnezdilova N.A., PhD, Associate Professor
Bunin Yelets State University, Yelets

Objective of the study was to present a methodological justification for solving probabilistic problems in the professional training of students in the field of physical education and sports based on the use of the «Python» programming language.
Methods and structure of the study. The method for solving probability problems in the study of independent repeated tests (Bernoulli and Poisson formulas) is based on the use of Python programming language libraries: NumPy; SciPy; Matplotlib; Seaborn; Pandas; SymPy; Scikit-learn [5, 6]. The study was carried out as part of the study of the discipline «Methods of Mathematical Information Processing» by students of the training program 44.03.05 Pedagogical Education, focus (profile) Physical Education, Life Safety.
Results and conclusions. The result of the study was a method for solving probabilistic problems in the study of independent repeated tests (Bernoulli and Poisson formulas) using Python programming language libraries in the professional training of students in the field of physical education and sports. The inclusion of this study in the methodological support of the professional training of students in the field of physical education and sports will contribute to the expansion of the resource base in the implementation of the digital transformation of higher education and the formation of competencies in students in the field of physical education and sports: universal (to solve standard problems of professional activity based on the use of digital technologies) and professional (to apply the mathematical apparatus in integration with computer environments to create and study models of various levels of abstraction).

Keywords: professional training of students in the field of physical education and sports, solving probabilistic problems, Python programming language, methodological support, competence.

References

1. Burovskiy, E. A., & Grishunina, Y. B. (2022). Zadachi matematicheskoy statistiki i ikh resheniye s ispolzovaniyem yazyka programmirovaniya Python [Problems of mathematical statistics and their solution using the Python programming language]. Vysshaya shkola ekonomiki. ISBN 978-5-7598-2682-8 (print), ISBN 978-5-7598-2483-1 (e-book)
2. Davidson-Pilon, C. (2019). Veroyatnostnoye programmirovaniye na Python: bayyesovskiy vyvod i algoritmy [Probabilistic programming with Python: Bayesian inference and algorithms]. Piter. (Seriya «Biblioteka programmista»). ISBN 978-5-4461-1058-2
3. Krivolapov, S. Y. (2021). Ispolzovaniye yazyka Python v teorii veroyatnostey [Using Python in probability theory]. Prometey.
4. Popov, N. I., & Bolotin, E. S. (2023). Ispolzovaniye integrirovannoy sredy dlya razrabotki i obucheniya Python IDLE pri izuchenii studentami teorii veroyatnostey [Using the integrated environment for development and training Python IDLE in students study of probability theory]. Vestnik MGPU. Seriya «Informatika i informatizatsiya obrazovaniya», 1(63), 79-85. https://doi.org/10.25688/2072-9014.2023.63.1.07
5. Ceng, G. Y. (2023). Implementation of Face Mask Detection Using Python Programming Language. bit-Tech, 6(1), 51-58. https://doi.org/10.32877/bt.v6i1.893
6. Raducan, E., & Arhip, M. (2023). Quality issue classification by using dedicated data analysis software created in Python language. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 24, 10-20. https://doi.org/10.55549/epstem.1406198