为数据分析应用程序新手选择编程语言的分析层次过程模型

A. Abdelnabi
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引用次数: 2

摘要

本研究提出一种层次分析法(AHP)模型,以供初学程式设计人员选择最佳的资料分析程式设计语言。因为这将积极地减少新手程序员的时间和精力。此外,这将为他提供良好和稳健的选择。提出的模型使用了八个标准,包括:流行度、数据分析支持、可处理的数据量、编译速度、表达能力、可怕性、程序员的建议和平均合理的财务成本。Python, R, Java, SQL, Scala和C编程语言被用作替代。该模型的结果表明,在测试的备选方案中,python语言是数据分析应用程序的最佳编程语言。进行了不一致性和敏感性分析,结果表明该模型具有较好的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Analytical Hierarchical Process Model to Select Programming Language for Novice Programmers for Data Analytics Applications
This study proposes an analytical hierarchy process (AHP) model to select the best programming language to be Learned by novice programmers for Data Analytics Applications. as this will positively reduce the time and efforts of novice programmers. Furthermore, this will give him good and robust choice. The proposed model uses eight criteria, including: Popularity, data analytics support, volume of data can handle, speed of compiling, expressiveness, dreadfulness, programmers’ recommendations and average reasonable financial cost. Python, R, Java, SQL, Scala and C programming languages are used as alternatives. The results of this model show that python language is the best programming language for data analytics applications among the tested alternatives. Both inconsistency and sensitivity analysis are done and show that the model is robust.
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