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ONLINE - Data Science Tools Showcase: Spatial Analysis with R and Python

As part of Duke’s Coronavirus response, we have decided not to offer an in-person session of Data Science Tools Showcase: Spatial Analysis with R and Python. Instead, we will share a video recording of the presentation and workshop materials with everyone registered for the event. Please email askdata@duke.edu if you need further details.

Some people who want to do spatial (geographic) analysis don’t know ArcPro or QGIS, but do know R or Python. In this workshop, we will present a command-line, or code-driven, approach to working with geospatial data. This method offers several advantages over traditional GIS software that can make your work easier, faster, and more reproducible!

We will share a showcase of spatial data science workflows that demonstrate the possibilities opened up by tools available from the command-line and scripting for processing, analyzing and visualizing data. 

This workshop would be helpful for participants with some GIS experience who want to expand their data science toolkit, or for those who currently use R and/or Python and are curious about the geospatial capabilities of these languages. 

Some basic familiarity with programming in R and/or Python will make this workshop more useful. We encourage anyone without this background to attend (or watch a recording of) our Introduction to R or Python for Data Science workshops.

The content of the workshop may be recorded. Cameras will capture the instructor station and the display screen. Ambient audio, including verbal audience  participation, will be included on the recording. Registration for the workshop indicates consent. If you are uncomfortable with a recording being published please contact the instructor at anytime prior to the conclusion of the workshop.

Date:
Wednesday, March 18, 2020
Time:
1:00pm - 2:00pm
Location:
Bostock 127 (The Edge Workshop Room)
Campus:
West Campus
Categories:
Data and Visualization  
Registration has closed.

Event Organizer

Eric Monson
Drew Keener
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Center for Data and Visualization Sciences