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基于AWS的数据科学实践
book

基于AWS的数据科学实践

by Chris Fregly, Antje Barth
September 2022
Intermediate to advanced
549 pages
11h 27m
Chinese
China Electric Power Press Ltd.
Content preview from 基于AWS的数据科学实践
146
|
第
5
章
让我们更深入探索数据集,并分析数据纠正、异常、偏移、失衡和有用的商业洞见。
这些从数据分析和探索得到的知识会为我们在第
6
章的数据偏移、特征选择、特征
工程和第
7
章、第
9
章的模型偏移、公平性和可解释性分析而做好准备。
5.1 AWS
上可用于浏览数据的工具
让我们介绍一些辅助数据探索任务的工具和服务。为了为正确的目的选择正确的工
具,我们会描述
AWS
中可用的工具的广度和深度,并使用这些工具来回答关于我
们
Amazon
客户评价数据集中的问题。
为了与
SageMaker Studio IDE
运行的
Jupyter
笔记本中的
AWS
资源交互,我们利用
AWS Python SDK Boto3
(
https://oreil.ly/byebi
)和
Python DB
客户端
PyAthena
(
https://
oreil.ly/DTQS8
)来连接到
Athena
,使用
Python SQL
工具包
SQLAlchemy
(
https://
oreil.ly/q0DC0
)来连接
Amazon Redshift
,并使用开源的
AWS Data Wrangler
库(
https://
oreil.ly/rUvry
)在
pandas
和
Amazon S3
,
Athena
,
Redshift
,
Glue
和
EMR
之间移动
数据。
开源的
AWS Data Wrangler
库(
https://oreil.ly/5Eq4H
)与
SageMaker
Data Wrangler
无关。这是一次不幸的命名冲突。
AWS Data Wrangler
专注于
AWS
存储服务间通用的数据接入 ...
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Publisher Resources

ISBN: 9787519869663