Use of Distributed Data Storage and Processing Systems to Support Predictive Analytics in a Task of Reducing Employee Turnover

Student: Chernov Aleksandr

Educational Programme: Big Data Systems (Master)

Year of Graduation: 2020

Master's thesis is devoted to the study of the relevant distributed storage and processing systems utilization with the aim of personnel turnover prediction. The first chapter of the study provides a detailed analysis of methodological approaches to the development and utilization of machine learning algorithms; problems of high data volume processing and storage are considered; theoretical basis for the proposed system architecture selection is provided. The study goes on to provide the description of the proposed architecture and its technological implementation in the second chapter. The study might be of interest for chief information officers, chief data officers, IT consulting companies, concerned about personnel turnover prediction.

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