<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Using Rust on DataThrillz</title><link>https://datathrillz.com/tags/using-rust/</link><description>Recent content in Using Rust on DataThrillz</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Sat, 10 Jul 2021 19:05:33 +0000</lastBuildDate><atom:link href="https://datathrillz.com/tags/using-rust/index.xml" rel="self" type="application/rss+xml"/><item><title>The next coding frontier- comparing about Julia, Go &amp; Rust with Python</title><link>https://datathrillz.com/posts/2021-07-10-julia-go-rust-vs-python/</link><pubDate>Sat, 10 Jul 2021 19:05:33 +0000</pubDate><guid>https://datathrillz.com/posts/2021-07-10-julia-go-rust-vs-python/</guid><description>&lt;p&gt;Currently, Python is the dominant programming language of data science and machine learning and is popular for more general scripting. It’s pretty awesome compared to its predecessors like C/C++, FORTRAN due to its ease of use, flexibility and readability. Python also has an active and robust library culture after over 30 years of existence. However, Python has some weaknesses that newer languages like Julia, Go and Rust readily address.&lt;/p&gt;
&lt;h1 id="pythons-challenge-areas"&gt;&lt;strong&gt;Pythons Challenge Areas&lt;/strong&gt;&lt;/h1&gt;
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