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Python and R Basics for Environmental Data Sciences


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Created: Jul 01, 2020 at 7:11 p.m.
Last updated: Nov 01, 2020 at 3:40 p.m.
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Content types: Geographic Feature Content  Geographic Raster Content 
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Abstract

This resource collects teaching materials that are originally created for the in-person course 'GEOSC/GEOG 497 – Data Mining in Environmental Sciences' at Penn State University (co-taught by Tao Wen, Susan Brantley, and Alan Taylor) and then refined/revised by Tao Wen to be used in the online teaching module 'Data Science in Earth and Environmental Sciences' hosted on the NSF-sponsored HydroLearn platform.

This resource includes both R Notebooks and Python Jupyter Notebooks to teach the basics of R and Python coding, data analysis and data visualization, as well as building machine learning models in both programming languages by using authentic research data and questions. All of these R/Python scripts can be executed either on the CUAHSI JupyterHub or on your local machine.

This resource is shared under the CC-BY license. Please contact the creator Tao Wen at Syracuse University (twen08@syr.edu) for any questions you have about this resource. If you identify any errors in the files, please contact the creator.

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Coverage

Spatial

Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
North Latitude
38.1703°
East Longitude
-119.5975°
South Latitude
37.6448°
West Longitude
-120.3404°

Content

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How to Cite

Wen, T. (2020). Python and R Basics for Environmental Data Sciences, HydroShare, http://www.hydroshare.org/resource/114e5092ab684bd9beb9fc845a25a087

This resource is shared under the Creative Commons Attribution CC BY.

http://creativecommons.org/licenses/by/4.0/
CC-BY

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