<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>数据科学 on howaboutqiu</title><link>https://rexarski.com/%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6/</link><description>Recent content in 数据科学 on howaboutqiu</description><generator>Hugo</generator><language>zh-CN</language><copyright>© Qiū Ruì</copyright><lastBuildDate>Wed, 26 Aug 2026 16:06:41 -0400</lastBuildDate><atom:link href="https://rexarski.com/%E6%95%B0%E6%8D%AE%E7%A7%91%E5%AD%A6/index.xml" rel="self" type="application/rss+xml"/><item><title>做的不是数据科学</title><link>https://rexarski.com/posts/2026/08/not-doing-data-science/</link><pubDate>Tue, 25 Aug 2026 22:30:24 -0400</pubDate><guid>https://rexarski.com/posts/2026/08/not-doing-data-science/</guid><description>&lt;p&gt;&lt;a href="https://www.reddit.com/r/datascience/comments/1vx150x/im_a_data_scientist_but_i_dont_do_any_data_science/"&gt;reddit 上一则讨论&lt;/a&gt;：发帖人说自己工作虽然是所谓的 data scientist 但是自己从来做的都不是真正的数据科学。原帖内容已经在一天后删除了。为什么删除呢？耐人寻味。&lt;/p&gt;
&lt;p&gt;票数最高的回答：&lt;/p&gt;
&lt;blockquote&gt;
&lt;p&gt;Not so hot take, most companies problems are so large that you don’t need any data science to solve them. What you do need is a clean set of data, the right framing, and the ability to add and subtract. Nine times out of 10 that beats any fancy data science model.&lt;/p&gt;
&lt;p&gt;The impact of the insight is always more important than the method to derive the insight.&lt;/p&gt;</description></item></channel></rss>