Education in End-to-end Technologies in Russian Universities: Scale of Implementation and Features of Management

Marianna A. Lukashenko, Ekaterina A. Sharova, Aleksandr I. Sharov
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Abstract

The article is devoted to the issues of managing education in end-to-end technologies, in particular big data analytics and artificial intelligence, in Russian universities. The article presents statistical data on the scale of the implementation of big data and artificial intelligence training programs in Russian universities. The authors note that to process a significant amount of information, algorithms for working with big data were used, such as Google Chrome extensions for extracting data from Instant data scraper and Table Capture web pages. Based on the results of the study, the key features of managing the development and implementation of training programs for big data and artificial intelligence in the top 15 universities of the country were identified and analyzed. It is noted that most of the programs have been developed “at the intersection” of academic disciplines and are aimed at training universal specialists, which dictates the integration of university faculties during their creation and close interaction with representatives of the professional community. Judgments are given that the most dense integration of universities and business is the automatic employment of students during the period of study. It is revealed that the management of the development of training programs for big data and artificial intelligence involves collaboration with EdTech platforms and the implementation of programs in a remote form that combines the advantages of a classical university program and the convenience of online learning, in particular, communicative comfort. The study showed that learning management also involves the development of “soft skills” among specialists in the field of data analytics and artificial intelligence.
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俄罗斯大学端到端技术教育:实施规模与管理特点
本文致力于探讨俄罗斯大学在端到端技术(尤其是大数据分析和人工智能)方面的教育管理问题。本文介绍了俄罗斯大学实施大数据和人工智能培训项目规模的统计数据。作者指出,为了处理大量信息,使用了处理大数据的算法,例如用于从Instant data scraper和Table Capture网页提取数据的谷歌Chrome扩展。根据研究结果,确定并分析了全国排名前15位的大学在管理大数据和人工智能培训计划的开发和实施方面的关键特征。值得注意的是,大多数方案都是在学科的“交叉点”上制定的,旨在培养通用专家,这要求在创建过程中整合大学院系,并与专业团体的代表密切互动。判断大学与企业结合最密集的是学生在学习期间的自动就业。据透露,大数据和人工智能培训项目的开发管理涉及与EdTech平台的合作,并以远程形式实施项目,结合了经典大学课程的优势和在线学习的便利性,特别是交流的舒适性。研究表明,学习管理还涉及数据分析和人工智能领域专家“软技能”的发展。
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