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Data science: opportunities to transform education

Volkova, Nataliia P.; Rizun, Nina O.; Nehrey, Maryna V.

URI: http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/3241
https://doi.org/10.31812/123456789/3241

Date: 2019-09-09

Abstract:

The article concerns the issue of data science tools implementation, including the text mining and natural language processing algorithms for increasing the value of high education for development modern and technologically flexible society. Data science is the field of study that involves tools, algorithms, and knowledge of math and statistics to discover knowledge from the raw data. Data science is developing fast and penetrating all spheres of life. More people understand the importance of the science of data and the need for implementation in everyday life. Data science is used in business for business analytics and production, in sales for offerings and, for sales forecasting, in marketing for customizing customers, and recommendations on purchasing, digital marketing, in banking and insurance for risk assessment, fraud detection, scoring, and in medicine for disease forecasting, process automation and patient health monitoring, in tourism in the field of price analysis, flight safety, opinion mining etc. However, data science applications in education have been relatively limited, and many opportunities for advancing the fields still unexplored.

Description:

1. Blei, D.M. Probabilistic Topic Models. Communications of the ACM 55(4), 77–84 (2012). doi:10.1145/2133806.2133826 2. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet Allocation. Journal of Machine Learning Research 3, 993–1022. http://jmlr.org/papers/v3/blei03a.html (2003) 3. Brunner, R.J., Kim, E.J. Teaching data science. Procedia Computer Science 80, 1947–1956 (2016). doi:10.1016/j.procs.2016.05 4. Chen, H., Chiang, R.H.L., Storey, V.C.: Business intelligence and analytics: from big data to big impact. MIS quarterly: Management Information Systems 36(4), 1165–1188 (2012) 5. Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by Latent Semantic Analysis. Journal of the American Society for Information Science 41(6), 391–407 (1990). doi:10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 6. Deerwester, S.C., Dumais, S.T., Furnas, G.W., Harshman, R.A., Landauer, T.K., Lochbaum, K.E., Streeter, L.A.: Computer information retrieval using latent semantic structure. US Patent 4,839,853, 13 June 1989 7. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society. Series B (Methodological) 39(1), 1–38 (1977) 8. George, G., Osinga, E.C., Lavie, D., Scott, B.A.: Big data and data science methods for management research: From the Editors. Academy of Management Journal 59(5), 1493– 1507 (2016). doi:10.5465/amj.2016.4005 9. Hnot, T.V., Nehrey, M.V.: Alhorytmy Data Science u modeliuvanni biznes-protsesiv (Data Science Algorithms in Modeling Business Processes). Ekonomika i suspilstvo 12, 743–751 (2017) 10. James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning: with Application in R. Springer, New York (2013) 11. Kiv, A., Semerikov, S., Soloviev, V., Kibalnyk, L., Danylchuk, H., Matviychuk, A.: Experimental Economics and Machine Learning for Prediction of Emergent Economy Dynamics. In: Kiv, A., Semerikov, S., Soloviev, V., Kibalnyk, L., Danylchuk, H., Matviychuk, A. (eds.) Experimental Economics and Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the 8th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2 2019), Odessa, Ukraine, May 22-24, 2019. CEUR Workshop Proceedings 2422, 1–4. http://ceurws.org/Vol-2422/paper00.pdf (2019). Accessed 1 Aug 2019 12. Kucherov, D.P.: The Synthesis of Adaptive Terminal Control Algorithm for Inertial Secondary Order System with Bounded Noises. Journal of Automation and Information Sciences 39(9), 16–25 (2007). doi:10.1615/JAutomatInfScien.v39.i9.20 13. Nehrey, M., Hnot, T.: Data Science Tools Application for Business Processes Modelling in Aviation. In: Shmelova, T., Sikirda, Yu., Rizun, N., Kucherov, D. (eds.) Cases on Modern Computer Systems in Aviation, 176-190. IGI Global, Hershey (2019). doi:10.4018/978-1- 5225-7588-7.ch006 14. Nehrey, M., Hnot, T.: Using recommendation approaches for ratings matrixes in online marketing. Studia Ekonomiczne 342, 115–130 (2017) 15. Nokel, M.A., Loukachevitch, N.V.: Tematicheskie modeli: dobavlenie bigramm i uchet skhodstva mezhdu unigrammami i bigrammami (Topic models: adding bigrams and taking account of the similarity between unigrams and bigrams). Computational methods and programming 16(2), 215–234 (2015). doi:10.26089/NumMet.v16r222 16. Parish, E.J., Duraisamy, K.: A paradigm for data-driven predictive modeling using field inversion and machine learning. Journal of Computational Physics 305, 758–774 (2016). doi:10.1016/j.jcp.2015.11.012 17. Patriarca, R., Di Gravio, G., Costantino, F.: A Monte Carlo evolution of the Functional Resonance Analysis Method (FRAM) to assess performance variability in complex systems. Safety science 91, 49-60 (2017). doi:10.1016/j.ssci.2016.07.016 18. Périaux, J., Chen, H.Q., Mantel, B., Sefrioui, M., Sui, H.T.: Combining game theory and genetic algorithms with application to DDM-nozzle optimization problems. Finite elements in analysis and design 37(5), 417–429 (2001). doi:10.1016/S0168-874X(00)00055-X 19. Rizun, N., Shmelova, T.: Decision-Making Models of the Human-Operator as an Element of the Socio-Technical Systems. In: Batko, R., Szopa, A. (eds.) Strategic Imperatives and Core Competencies in the Era of Robotics and Artificial Intelligence, pp. 167–204. IGI Global, Hershey (2017). doi:10.4018/978-1-5225-1656-9.ch009 20. Salton, G., Wong, A., Yang, C.S.: A Vector Space Model for Automatic Indexing. Communications of the ACM 18(11), 613–620 (1975) 21. Semerikov, S.O., Teplytskyi, I.O., Yechkalo, Yu.V., Kiv, A.E.: Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot. In: Kiv, A.E., Soloviev, V.N. (eds.) Proceedings of the 1st International Workshop on Augmented Reality in Education (AREdu 2018), Kryvyi Rih, Ukraine, October 2, 2018. CEUR Workshop Proceedings 2257, 122–147. http://ceur-ws.org/Vol-2257/paper14.pdf (2018). Accessed 30 Nov 2018 22. Semerikov, S.O., Teplytskyi, I.O., Yechkalo, Yu.V., Markova, O.M., Soloviev, V.N., Kiv, A.E.: Computer Simulation of Neural Networks Using Spreadsheets: Dr. Anderson, Welcome Back. In: Ermolayev, V., Mallet, F., Yakovyna, V., Kharchenko, V., Kobets, V., Korniłowicz, A., Kravtsov, H., Nikitchenko, M., Semerikov, S., Spivakovsky, A. (eds.) Proceedings of the 15th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer (ICTERI, 2019), Kherson, Ukraine, June 12-15 2019, vol. II: Workshops. CEUR Workshop Proceedings 2393, 833–848. http://ceur-ws.org/Vol-2393/paper_348.pdf (2019). Accessed 30 Jun 2019 23. Shoro, A.G., Soomro, T.R.: Big Data Analysis: Apache Spark Perspective. Global Journal of Computer Science and Technology 15(1-C). https://computerresearch.org/index.php/computer/article/view/1137 (2015) 24. Vorontsov, K.V., Potapenko, A.A.: Modifikatcii EM-algoritma dlia veroiatnostnogo tematicheskogo modelirovaniia (EM-like algorithms for probabilistic topic modeling). Machine Learning and Data Analysis 1(6), 657–686 (2013) 25. Xiong, J., Yu, G., Zhang, X.: Research on Governance Structure of Big Data of Civil Aviation. Journal of Computer and Communications 5(5), 112–118 (2017). doi:10.4236/jcc.2017.55009

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