理论研究

国外数据科学家能力体系研究现状与启示

  • 秦小燕 ,
  • 初景利
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  • 1. 中国科学院文献情报中心 北京 100190;
    2. 中国科学院大学 北京 100049;
    3. 北京航空航天大学图书馆 北京 100191
秦小燕(ORCID:0000-0002-8177-0255),北京航空航天大学图书馆馆长助理,中国科学院大学、中国科学院文献情报中心博士研究生

收稿日期: 2017-07-20

  修回日期: 2017-08-26

  网络出版日期: 2017-12-05

基金资助

本文系国家社会科学基金重点项目"新型出版模式对学术图书馆的影响研究"(项目编号:15ATQ001)与北京航空航天大学研究生教育与发展研究专项基金项目"科学数据素养教育对提升研究生创新能力的研究"(项目编号:412953)研究成果之一。

Research and Enlightenment on Data Scientist Competency Systems Abroad

  • Qin Xiaoyan ,
  • Chu Jingli
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  • 1. National Science Library, Chinese Academy of Sciences, Beijing 100190;
    2. University of Chinese Academy of Sciences, Beijing, 100049;
    3. Library of Beihang University, Beijing 100191

Received date: 2017-07-20

  Revised date: 2017-08-26

  Online published: 2017-12-05

摘要

[目的/意义]梳理并分析国外关于数据科学家能力体系的相关研究,为我国构建数据科学家能力体系提供参考借鉴,既有利于提高数据科学人才培养效率,也有益于满足数据科学家职业发展的需要。[方法/过程]选取主要国家(地区)的典型数据科学家能力研究成果,解读分析其中的能力框架与要素,探讨目前国外数据科学家能力体系的研究方法、数据科学家职业准入条件以及信息环境变化对数据科学家能力的影响。[结果/结论]国外数据科学家能力体系的建设值得借鉴。我国应该尽快构建数据科学家能力框架,明确数据科学家的培养目标与职业发展路径;通过顶层设计、多方协同,加强数据科学专业人才培养;强调理论知识与实践能力并重,注重数据科学家的在职技能拓展。

本文引用格式

秦小燕 , 初景利 . 国外数据科学家能力体系研究现状与启示[J]. 图书情报工作, 2017 , 61(23) : 40 -50 . DOI: 10.13266/j.issn.0252-3116.2017.23.005

Abstract

[Purpose/significance] This paper combed and analyzed the relevant research on the data scientist competency systems abroad to provide reference for building the competency system of data scientists, and be helpful to improve the efficiency of data science personnel cultivation and to meet the needs of data scientist's career development. [Method/process] The paper selected the typical data science research results of the major countries (regions), analyzed the competency elements, discussed the current research methods of the data scientist competency systems abroad, data scientist career access conditions, and the impact of information environment changes on data scientist competencies. [Result/conclusion] The construction of foreign data scientist competency system is worth learning from.It is suggested that China should build data scientist competency framework as soon as possible to clarify the training objectives and career development path. Through the top-level design, multi-party cooperation, strengthen the data science professional personnel training; emphasis both on theoretical knowledge and practical ability, pay attention to data scientists skills expansion.

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