<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Christie Bahlai on The Carpentries</title><link>https://deploy-preview-705--carpentries-website.netlify.app/blog/author/christie-bahlai/</link><description>Recent content in Christie Bahlai 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/christie-bahlai/index.xml" rel="self" type="application/rss+xml"/><item><title>Soft(ware) Skills</title><link>https://deploy-preview-705--carpentries-website.netlify.app/blog/2017/01/soft-skills/</link><pubDate>Thu, 19 Jan 2017 00:00:00 +0000</pubDate><guid>https://deploy-preview-705--carpentries-website.netlify.app/blog/2017/01/soft-skills/</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>I think, in the end, technology and data science are about communication.&lt;/p>
&lt;p>As a biologist with a physics degree, I’ve always been called on by my peers to help out with the more ‘mathy’ parts of their work.
As early as my masters, my friends and collaborators would send me their data, here and there, to have a look and make suggestions for
how to proceed. More often than not, I’d either send it back to them asking lots of questions, or spend hours of my own time, cleaning,
manually manipulating data until it was in a form that I could query it and looks for patterns.&lt;/p></description></item></channel></rss>