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15
2019-05-22T11:20:21.162-04:00
Jesse.Luebbert Radhikha.Myneni
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692049
"SAS, Open Source, Python, R, Machine Learning, Artificial Intelligence, Data Mining"
<p>Data scientists use a variety of tools, both commercial and open-source, to achieve key goals for their organization. For enterprise applications of analytics and artificial intelligence, it is crucial that teams can collaborate no matter which tools they are using. SAS® software provides a platform on which all users in the enterprise can create intelligence from data and operationalize the results easily. Data scientists and developers whose core programming competence is in languages such as Python and R can efficiently use SAS through a variety of APIs to increase productivity and improve time-to-value. This paper describes and demonstrates a variety of best-practice use cases to show how SAS software provides integration with open-source tools to support end-to-end analytical workflows.</p>
<p>Jesse Luebbert and Radhikha Myneni, SAS Institute Inc. </p>
<p style="font-family: Arial,Helvetica,sans-serif; font-size: 90%; line-height: 300%; margin: -3em 0em 2em 0em;"><a href="https://github.com/sascommunities/sas-global-forum-2019">Access sample code</a></p>
Session 3415
en
regis.white@sas.com
<p>Data scientists use a variety of tools, both commercial and open-source, to achieve key goals for their organization. For enterprise applications of analytics and artificial intelligence, it is crucial that teams can collaborate no matter which tools they are using. SAS® software provides a platform on which all users in the enterprise can create intelligence from data and operationalize the results easily. Data scientists and developers whose core programming competence is in languages such as Python and R can efficiently use SAS through a variety of APIs to increase productivity and improve time-to-value. This paper describes and demonstrates a variety of best-practice use cases to show how SAS software provides integration with open-source tools to support end-to-end analytical workflows.</p>
<p>Jesse Luebbert and Radhikha Myneni, SAS Institute Inc. <br>
</p>
Microsoft Word
2019-05-06T15:42:26.000-04:00
2019-05-06T15:42:26.000-04:00
2019-04-02T11:32:00.000-04:00
application/pdf
SAS, Open Source, Python, R, Machine Learning, Artificial Intelligence, Data Mining
2019-05-22T11:20:21.201-04:00
Jesse.Luebbert Radhikha.Myneni
Data scientists use a variety of tools, both commercial and open-source, to achieve key goals for their organization. For enterprise applications of analytics and artificial intelligence, it is crucial that teams can collaborate no matter which tools they are using. SAS® software provides a platform on which all users in the enterprise can create intelligence from data and operationalize the results easily. Data scientists and developers whose core programming competence is in languages such as Python and R can efficiently use SAS through a variety of APIs to increase productivity and improve time-to-value. This paper describes and demonstrates a variety of best-practice use cases to show how SAS software provides integration with open-source tools to support end-to-end analytical workflows.
Jesse Luebbert and Radhikha Myneni, SAS Institute Inc.
SAS® and Open Source: Two Integrated Worlds
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event-type:180/session-type/breakout
support:sgf-papers/topic/analytics/open-source-integration
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support:skill-level/intermediate
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