Large data processing with SQL for social science research Session II: Continuing SQL and project workflows in R

Monday, November 11, 4:30pm - 6:00pm (EST)

Time shown in-05:00 America, New York

This series introduces SQL for social scientists, with a focus on DuckDB -- a portable, fast, and scalable database for analytics. With only a few basics, researchers can easily scale projects to handle most larger-than-memory data processing tasks without need of a cluster. We will discuss how to conveniently integrate DuckDB's SQL dialect into existing R (and Python) workflows. We conclude with special topics, which may include geospatial data processing, databases on the cluster, and some SQL best practices.

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Add to Calendar 2024/11/11 16:30:00 2024/11/11 18:00:00 America/New_York Large data processing with SQL for social science research Session II: Continuing SQL and project workflows in R

This series introduces SQL for social scientists, with a focus on DuckDB -- a portable, fast, and scalable database for analytics. With only a few basics, researchers can easily scale projects to handle most larger-than-memory data processing tasks without need of a cluster. We will discuss how to conveniently integrate DuckDB's SQL dialect into existing R (and Python) workflows. We conclude with special topics, which may include geospatial data processing, databases on the cluster, and some SQL best practices.

Bendheim House, Room 103 false MM/DD/YYYY 180 OPAQUE aoDxTBFgIzerHDRhcmCF213266

You're interested in "Large data processing with SQL for social science research Session II: Continuing SQL and project workflows in R".

We've sent a confirmation email to your email address. Be sure to check your junk folder in case you haven't received the confirmation.

Add to Calendar 2024/11/11 16:30:00 2024/11/11 18:00:00 America/New_York Large data processing with SQL for social science research Session II: Continuing SQL and project workflows in R

This series introduces SQL for social scientists, with a focus on DuckDB -- a portable, fast, and scalable database for analytics. With only a few basics, researchers can easily scale projects to handle most larger-than-memory data processing tasks without need of a cluster. We will discuss how to conveniently integrate DuckDB's SQL dialect into existing R (and Python) workflows. We conclude with special topics, which may include geospatial data processing, databases on the cluster, and some SQL best practices.

Bendheim House, Room 103 false MM/DD/YYYY 180 OPAQUE aoDxTBFgIzerHDRhcmCF213266

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Bendheim House, Room 103

Initiative for Data-Driven Social Science, ddss@princeton.edu

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