Building Statistics and Data Science Capacity for Development
Building Statistics and Data Science Capacity for Development
复制标题
建设统计和数据科学发展能力
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Kim Love
中科院分区：
文献类型：
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作者：
Eric A. Vance;Kim Love
数据驱动的可持续发展决策需要领域专业知识来提出正确的问题；高质量、相关的数据；适当、细致的统计分析；以及制定和执行决策的权力。统计数据支持并加速了所有这些方面。我们提出了建立统计和数据科学能力的新模式，以参与数据驱动的发展。统计学家和数据科学家必须能够在深度和广泛的层面上理解他们正在处理的数据和项目，并能够以提供可操作证据的方式向那些可以使用它对社会产生积极影响的人传达统计方法和分析工作的结果。我们建立统计和数据科学能力的模式是创建统计和数据科学合作实验室（“统计实验室”），通过与数据生产者和数据决策者合作，将证据转化为行动，在数据驱动发展的交叉点工作。我们介绍了从LISA 2020网络中吸取的经验教训，该网络利用了发展中国家30多个新成立的统计实验室的集体经验，通过关注数据驱动发展的交叉点来建设此类统计和数据科学能力。
Data-driven decision making for sustainable development requires domain expertise to ask the right questions; high-quality, relevant data; appropriate, nuanced statistical analyses; and the power to make and implement a decision. Statistics enables and accelerates all of these aspects. We propose a new model for building statistics and data science capacity to engage in data-driven development. Statisticians and data scientists must be able to understand the data and projects they are working with on both a deep and broad level and be able to communicate the results of statistical methods and analytical work in ways that provide actionable evidence to those who can use it to positively impact society. Our model for building statistics and data science capacity is to create statistics and data science collaboration laboratories (“stat labs”) that work in the intersections of data-driven development by collaborating with data producers and data decision makers to transform evidence into action. We present lessons learned from the LISA 2020 Network, which has leveraged the collective experiences of more than 30 newly created stat labs in developing countries to build such statistics and data science capacity by focusing on the intersections of data-driven development.