<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>K.A.S. Mislan on The Carpentries</title><link>https://deploy-preview-705--carpentries-website.netlify.app/blog/author/k.a.s.-mislan/</link><description>Recent content in K.A.S. Mislan on The Carpentries</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Wed, 20 Nov 2024 10:51:28 -0500</lastBuildDate><atom:link href="https://deploy-preview-705--carpentries-website.netlify.app/blog/author/k.a.s.-mislan/index.xml" rel="self" type="application/rss+xml"/><item><title>Discovering the data science community, becoming part of it, and expanding it</title><link>https://deploy-preview-705--carpentries-website.netlify.app/blog/2016/12/discovering/</link><pubDate>Fri, 09 Dec 2016 00:00:00 +0000</pubDate><guid>https://deploy-preview-705--carpentries-website.netlify.app/blog/2016/12/discovering/</guid><description>&lt;p>&lt;strong>This post originally appeared on the &lt;a href="https://datacarpentry.org">Data Carpentry website&lt;/a>&lt;/strong>&lt;/p>
&lt;p>My path to becoming part of a data science community was happenstance. I started programming as a graduate student because I wanted to increase the spatial and temporal scales of my analyses. The scale increase was from one location for a few months to the entire coast of Western North America for many years. For larger scale analyses, I needed satellite data, which I downloaded from a data repository. I learned to use shell scripts to automatically download data because going through 5 to 10 internet links to manually download hundreds of files was tedious. The files with satellite data were large and in special file formats so I learned how to access and analyze them using R.&lt;/p></description></item></channel></rss>