The Training Method for Digital Data Operation

The purpose of study is to develop the training method for operation with digital data. The article discusses the issues of training for mining and analyzing digital data on the example of social networks for higher education programs in the areas of “Management”, “Public administration”, “Human resources” and “Political Science”. The relevance of the study is justified by factors: digital transformation of economy; development of digital data sources; increasing the importance of digital data in management. Universities have a new task - to prepare students for working with digital data in their professional activities. A review of scientific sources has shown that programming skills are required to apply existing data mining methods. While the modern IT and data sources contain tools for working with data, which are available to a wide range of users without the need to write the code.
Materials and methods. The study is based on the theoretical materials and the practice of operation with digital data in management processes. The empirical studies were conducted to evaluate the effectiveness of the application of practical data manipulation techniques in higher education training.
Results. The method was developed for the practical training in data mining and analysis skills. The implementation of the author’s method in the educational process showed its effectiveness in the formation of practical skills in working with digital data, as well as a high level of assimilation of theoretical foundations due to the presentation of educational materials in an accessible form for non-core IT area. The method doesn’t require a specific complex of the material and technical support for training and labor intensity. The article highlights the areas of application of social network data in Economics and science: marketing research of consumers and competitive advantages of goods or services; formation of a data set for machine learning and usage of artificial intelligence technologies, political research of civil society and political preferences of citizens, scientific research on the organization and management of social media. Training for analytical work on the example of social networks highly motivates students due to the significant role of networks among young people. The use of effective pedagogical technologies such as project-oriented learning, social learning, and collaboration in an electronic educational environment supports the quality of training by the developed method. As a result, students better learn knowledge and practical skills that are also applicable to working with other types of social media and global data platforms.
Conclusion. The article reveals: the specifics of teaching materials; development of a workshop in the areas of training; modern pedagogical technologies, scheme, and teaching methods. The advantages and disadvantages of social networks as a data source are considered. The presented method is implemented in teaching the discipline “Informatics” of the basic training cycle at the faculty of public administration of Lomonosov Moscow State University.

1. Dneprovskaya N.V. A method for researching the competencies of subjects of the digital economy. Otkrytoye obrazovaniye = Open Education. 2020; 24 (1): 4-12. DOI: 10.21686/1818-4243-2020-1-4-12. (In Russ.)

2. Chuvgunova O.A. Information, communication and technological competence of a university teacher: diagnostics and development. Otkrytoye obrazovaniye = Open education. 2019; 3: 49-61. DOI: 10.21686/1818-4243-2019-3-49-61. (In Russ.)

3. Yershova T. V. Working with data - the basis of the digital economy. Trudy Vol’nogo ekonomicheskogo obshchestva Rossii = Proceedings of the Free Economic Society of Russia. 2020; 220: 152-168. (In Russ.)

4. Dneprovskaya, N. V. Requirements for an innovative environment in the transition to a digital economy. Statistika i Ekonomika = Statistics and Economics. 2018; 16; 6: 58–68. (In Russ.)

5. Dudina V. I. Digital data — the potential for the development of sociological knowledge. Sotsiologicheskiye issledovaniya = Sociological studies. 2016; 9; 9: 21-30. (In Russ.)

6. Yurchenko A. V. Towards the concept of an information-analytical system for supporting scientific research based on the intensive use of digital data. Vychislitel’nyye tekhnologii = Computational technologies. 2017; 22; 4: 105-120. (In Russ.)

7. Starodubtsev V.A., Frantsuzskaya Ye.O. Sustainable development of education: connection between technology and pedagogy. Otkrytoye obrazovaniye = Open education. 2017; 1: 34-43. DOI: 10.21686/1818-4243-2017-1-34-43. (In Russ.)

8. Mulyukova K.V., Kureychik V.M. The problem of analyzing big web data and the use of Data Mining technology for processing and searching for patterns in a large array of web data on a practical example. Otkrytoye obrazovaniye = Open Education. 2019; 23(2): 42-49. DOI: 10.21686/1818-4243-2019-2-42-49. (In Russ.)

9. Sukhomlin V.A., Zubareva Ye.V., Yakushin A.V. Methodological aspects of the concept of digital skills. Sovremennyye informatsionnyye tekhnologii i IT-obrazovaniye = Modern information technology and IT education. 2017. № 2 (13). S.146-152. (In Russ.)

10. Vasil’yeva Ye.V. Competence-based approach in public service: what knowledge and skills do civil servants choose? Voprosy gosudarstvennogo i munitsipal’nogo upravleniya = Issues of state and municipal management. 2018; 4: 120-144. (In Russ.)

11. Kalimullina O.V., Trotsenko I.V. Modern digital educational tools and digital competence: analysis of existing problems and trends. Otkrytoye obrazovaniye = Open Education. 2018; 22(3): 61- 73. DOI: 10.21686/1818-4243-2018-3-61-73. (In Russ.)

12. Dneprovskaya N. V. Digital transformation of interaction between public authorities and citizens. Gosudarstvennoye upravleniye. Elektronnyy vestnik = Public administration. Electronic bulletin. 2018; 67: 96–110. (In Russ.)

13. Krasil’shchikov V. V., Osetrov M. A. Analysis of student activity in a social network. Vyssheye obrazovaniye v Rossii = Higher education in Russia. 2017; 2: 52-62. (In Russ.)

14. More J. S., Lingam C. A SI model for social media influencer maximization. Applied Computing and Informatics. 2019; 15; 2: 102-108. DOI: 10.1016/j.aci.2017.11.001.

15. Sergeyev V.M., Artyushkin V.F. Indicators of the innovative potential of political and economic development. Polis. Politicheskiye issledovaniya = Polis. Political Studies. 2016; 6: 114-126. (In Russ.)

16. Colace F. et al. Sentiment detection in social networks and in collaborative learning environments. Computers in Human Behavior. 2015; 51: 1061- 1067.

17. Khamidulin V.S. Modernization of the model of project-oriented education in the university. Vyssheye obrazovaniye v Rossii = Higher education in Russia. 2020; 29; 1: 135-149. DOI: 10.31992/0869-3617-2020-29-1-135-149. (In Russ.)

18. Shirobokova S.N., Strel’tsov Ye.A. Comparative analysis of the capabilities of the API of social networks by the criterion of functional completeness [Internet]. Innovatsionnaya nauka = Innovative Science. 2016; 3: 147-151. Available from: https://cyberleninka.ru/article/n/sravnitelnyy-analiz-vozmozhnostey-apisotsialnyh-setey-po-kriteriyu-funktsionalnyy-polnoty. (cited 06.03.2020). (In Russ.)

19. Agayan G.M., Grigoryan A.A., Shikina G.Ye. Matematika = Mathematics. Moscow: ArgamakMedia; 2019. 280 p. (In Russ.)

20. Petrunin YU. YU. Informatsionnyye tekhnologii analiza dannykh. 3-ye izdaniye = Information technologies for data analysis. 3rd edition. Moscow: Book House University; 2018. 292 p. (In Russ.)