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Problems of Big Data Analysis Education and Their Solutions

빅데이터 분석 교육의 문제점과 개선 방안 -학생 과제 보고서를 중심으로

  • Received : 2017.10.11
  • Accepted : 2017.12.20
  • Published : 2017.12.28

Abstract

This paper examines the problems of big data analysis education and suggests ways to solve them. Big data is a trend that the characteristic of big data is evolving from V3 to V5. For this reason, big data analysis education must take V5 into account. Because increased uncertainty can increase the risk of data analysis, internal and external structured/semi-structured data as well as disturbance factors should be analyzed to improve the reliability of the data. And when using opinion mining, error that is easy to perceive is variability and veracity. The veracity of the data can be increased when data analysis is performed against uncertain situations created by various variables and options. It is the node analysis of the textom(텍스톰) and NodeXL that students and researchers mainly use in the analysis of the association network. Social network analysis should be able to get meaningful results and predict future by analyzing the current situation based on dark data gained.

Keywords

Big data analysis;Convergence education;Variability;Veracity;Opinion mining;Social network analytics

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