Babenko, V., Kulczyk, Z., Perevozova, I., Syniavska, O., Davydova, O. (2019). Factors of Development of International e-Commerce in the Context of Globalization.
CEUR Workshop Proceedings, vol. 2422, pp. 345-356.
http://ceur-ws.org/Vol-2422/paper28.pdf
Babenko V., Panchyshyn A., Zomchak L., Nehrey M., Artym-Drohomyretska Z., Lahotskyi T. Classical Machine Learning Methods in Economics Research. Macro and Micro Level Example. WSEAS Transactions on Business and Economics 2021, 18, 209-217;
https://doi.org/10.37394/23207.2021.18.22
Bressert E. SciPy and NumPy 1st Edition; Publisher: O'Reilly Media, .USA, 2012; 57.
Coelho L. P. Building Machine Learning Systems with Python; Publisher: Packt Publishing, UK, 2018; 406.
Führer C., Solem J. E., Verdier O. Scientific Computing with Python: High-performance scientific computing with NumPy, SciPy, and pandas, 2nd Edition 2nd ed. Edition; Publisher: Packt Publishing, UK, 2021; 392 p.
Gontareva, I., Babenko, V., Shmatko, N., Litvinov, O., Hanna, O. (2020). The Model of Network Consulting Communication at the Early Stages of Entrepreneurship.
WSEAS Transactions on Environment and Development, Vol. 16, pp. 390-396.
https://doi.org/10.37394/232015.2020.16.39
Guryanova L., Yatsenko R., Dubrovina N., Babenko V. (2020). Machine learning methods and models, predictive analytics and applications. CEUR Workshop Proceedings, 2020, Available
online: http://ceur-ws.org/Vol-2649/
Guryanova L., Yatsenko R., Dubrovina N., Babenko V., Gvozditskyi V. Machine Learning Methods and Models, Predictive Analytics and Applications: Development Trends in the Post-crisis Syndrome Caused by COVID-19. CEUR Workshop Proceedings, 2021, Available online: http://ceur-ws.org/Vol-2927/paper1.pdf
Janssens J. Data Science at the Command Line: Facing the Future with Time-Tested Tools 1st Edition; Publisher: O'Reilly Media, USA, 2014; 212 p.
Johansson R. Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib 2nd ed. Edition; Publisher: Apress, USA, 2018; 723 p.
Harrison M. Effective Pandas: Patterns for Data Manipulation (Treading on Python); Publisher: Independently published, USA, 2021; 497 p.
Karau H., Konwinski A., Wendell P., Zaharia M. Learning Spark: Lightning-Fast Big Data Analysis 1st Edition; Publisher: O'Reilly Media, USA, 2020; 276 p.
Marianna Lepelaar, Adam Wahby, Martha Rossouw, Linda Nikitin, Kanewa Tibble, Peter J. Ryan, Richard B. Watson Sentiment Analysis of Social Survey Data for Local City Councils. J. Sens. Actuator Netw. 2022, 11(1), 7;
https://doi.org/10.3390/jsan11010007
Mavlutova, I., Babenko, V., Dykan, V., Prokopenko, N., Kalinichenko, S., Tokmakova, I. (2021). Business Restructuring as a Method of Strengtening Company’s Financial Position.
Journal of Optimization in Industrial Engineering, 14(1), 129-139.
http://dx.doi.org/10.22094/JOIE.2020.677839
Malyarets, L., Draskovic, M., Babenko, V., Kochuyeva, Z., Dorokhov, O. (2017). Theory and practice of controlling at enterprises in international business.
Economic Annals-ХХI, Vol. 165, Iss. 5-6, 90-96.
https://doi.org/10.21003/ea.V165-19
McKinne W. Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter 3rd Edition; Publisher: O'Reilly Media, USA, 2022; 579 p.
Molin S., Jee K. Hands-On Data Analysis with Pandas: A Python data science handbook for data collection, wrangling, analysis, and visualization, 2nd Edition; Publisher: Packt Publishing, UK, 2021; 788 p.
Naldi, M. A review of sentiment computation methods with R packages. arXiv Prepr. 2019, arXiv:1901.08319 2019. Available online:
https://arxiv.org/pdf/1901.08319.pdf (accessed on 2 June 2021).
Nelli F. Python Data Analytics: With Pandas, NumPy, and Matplotlib 2nd ed. Edition; Publisher: Apress, USA, 2018; 588 p.
Plas J., Vander P. J. Python for Complex Tasks: Data Science and Machine Learning; Publisher: O'Reilly Bestsellers, USA, 2021; 576 p.
Ramazanov, S., Babenko, V., Honcharenko, O., Moisieieva, N., Dykan, V. (2020). Integrated intelligent information and analytical system of management of a life cycle of products of transport companies.
Journal of Information Technology Management, 2020, 12(3), 26-33.
https://doi.org/10.22059/jitm.2020.76291
Ramirez, C.M.; Abrajano, M.A.; Alvarez, R.M. Using Machine Learning to Uncover Hidden Heterogeneities in Survey Data. Sci. Rep. 2019, 9, 16061.
White T. Hadoop: The Definitive Guide, Third Edition; Publisher: Yahoo Press, USA, 2012; 688 p.
Yigitcanlar, T.; Kankanamge, N.; Vella, K. How Are Smart City Concepts and Technologies Perceived and Utilized? A Systematic Geo-Twitter Analysis of Smart Cities in Australia? J. Urban Technol. 2021, 28, 135–154.