G2TT
来源类型Report
规范类型报告
DOIhttps://doi.org/10.7249/PEA150-1
来源IDPE-A150-1
Decoding Data Science: The U.S. Coast Guard's Evolving Needs and Their Implications
Aaron C. Davenport; Michelle D. Ziegler; Abbie Tingstad; Katherine Anania; Daniel Ish; Nidhi Kalra; Scott Savitz; Rachel Liang; Melissa Bauman
发表日期2020-06-24
出版年2020
页码24
语种英语
摘要

Like many large organizations, the U.S. Coast Guard has vast amounts of data that it could use to identify, predict, and solve pressing challenges. Data science could be valuable to the Coast Guard in a variety of domains, such as forecasting the resources needed for future trends in search-and-rescue missions, further automating aids to navigation, or automating fishery observations. In personnel areas, data science could help improve billet assignments, determine where to focus recruiting efforts, and boost employee retention.

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The Coast Guard has an opportunity to plot the path to determine service-specific uses, identify the strategy and driving mechanisms, and begin laying out a plan for the use of data science, which includes data collection, analysis, and management; artificial intelligence; and machine learning. This Perspective outlines the role that data science can play in decisionmaking processes and provides a selected set of key questions and sensitivities for the Coast Guard to consider in developing its future usage of data science.

主题Data Science ; United States Coast Guard
URLhttps://www.rand.org/pubs/perspectives/PEA150-1.html
来源智库RAND Corporation (United States)
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资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/524970
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GB/T 7714
Aaron C. Davenport,Michelle D. Ziegler,Abbie Tingstad,et al. Decoding Data Science: The U.S. Coast Guard's Evolving Needs and Their Implications. 2020.
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RAND_PEA150-1.pdf(501KB)智库出版物 限制开放CC BY-NC-SA浏览
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