Article

Received 21.02.2022

Revised 08.07.2022

Accepted 22.08.2022

Retrieved from Vol. 26, No. 3, 2022

Pages 337 -343

Sysoev, І., Gavrilenko, V., Shumeiko, O., Rudoman, N., & Donets, V. (2022). Prospects of python algorithmic language in students 'handling of machine learning disciplines. The National Transport University Bulletin, 26(3), 337-343. https://doi.org/10.33744/2308-6645-2022-3-53-337-343

Prospects of python algorithmic language in students 'handling of machine learning disciplines

І. Sysoev V. Gavrilenko Oleksii Shumeiko N. Rudoman Veronika Donets

The article discusses the prospects of using the python programming language to teach students disciplines related to machine learning. The situation in the field of programming languages that are related and can be used in solving machine learning problems is analyzed. The analysis showed that current trends show a rapid growth in popularity, both approaches and techniques that require solving machine learning problems, and the programming language python, which indicates great prospects for learning this programming language by students. An analysis of the advantages and disadvantages of python programming language, which allows to assess the viability of the language in comparison with competitors, in terms of qualitative characteristics that will be important in studying students within the university, and in applying students' knowledge in the future outside the university in a competitive environment. Comparative analysis of characteristics was carried out in comparison with the programming language R, which is also quite popular in solving machine learning problems. The analysis demonstrates the disadvantages and advantages of the python language, which are reflected in the conclusion of the article regarding the prospects of this language compared to others. There is also an analysis of the popularity of the python language in solving general programming problems, which is also important because it can give students an advantage in using the knowledge gained in a competitive environment. The analysis showed that python takes place in solving general programming problems, but still inferior in popularity to others in languages. Concluding from the study, we can assume that python due to its strengths is a good candidate for students to study in subjects related to the task of machine learning, but the popularity of this language to solve general programming problems does not provide significant advantages when used in a competitive environment. comparable to other programming languages.

 

програмування; машинне навчання; Python; аналіз; переваги
  1. Anisimov A.V. Prohramuvannia chyslovykh metodiv movoiu Python [Programming of numerical methods in Python] / Anisimov A.V., Doroshenko A.Yu., Pogoriliy S.D., Dorogiy Ya.Yu. Ed. A.V. Anisimov. – K .: Publishing and Printing Center "Kyiv University", 2014. – 640 p. [in Ukrainian].
  2. Reitynh mov prohramuvannia 2019 [Rating of programming languages 2019] – [Electronic resource]. – Access mode: https://dou.ua/lenta/articles/language-rating-jan-2019/ [in Ukrainian].
  3. Yakovenko A.V. Osnovy prohramuvannia. Python. Chastyna 1 [Fundamentals of programming. Python. Part 1] – Kyiv: KPI named after Igor Sikorivsky. – 2018. – 195 p. [in Ukrainian].
  4. Gabrusev V.E. Vykorystannia elementiv komp‘iuternoho modeliuvannia u protsesi navchannia fizyky [The use of elements of computer modeling in the process of teaching physics] // Scientific Journal of NPU named after MP Drahomanov. Series No. 2. Computer-based learning systems: Coll. scientific works / Redrad. – K .: NPU named after M.P. Dragomanov, 2018. – № 20 (27). – P. 77 [in Ukrainian].
  5. Suchasni tsyfrovi tekhnolohii ta innovatsiini metodyky navchannia: dosvid, tendentsii, perspektyvy. Materialy VII Mizhnarodnoi naukovo-praktychnoi internet-konferentsii [Modern digital technologies and innovative teaching methods: experience, trends, prospects. Proceedings of the VII International Scientific and Practical Internet Conference] (Ternopil, April 8, 2021), 164 p. [in Ukrainian]. 

https://doi.org/10.33744/2308-6645-2022-3-53-337-343