<story><title>Data science is different now</title><url>https://veekaybee.github.io/2019/02/13/data-science-is-different/</url></story><parent_chain></parent_chain><comment><author>lordnacho</author><text>There&amp;#x27;s something very relevant in this story. Data Science is too glamorous a term. There&amp;#x27;s an implication that the DS person is some sort of magician who maybe isn&amp;#x27;t as good at general coding but has special data magic skills, making them more valuable than your average grunt.&lt;p&gt;In my years in finance, there was a similar problem. One guy in particular I worked with reckoned himself an &amp;quot;ideas guy&amp;quot; and would simply spout out gibberish that he expected the rest of us to implement. He could barely use excel himself, let alone code.&lt;p&gt;The fact is the best coders I met never fancied themselves as specialists. They could certainly fit some models for you, but they could also write some SQL, set up replication and other maintenance, write cron jobs, set up ssh keys, merge some git branches, and write front and back end code in several different languages, declarative and imperative. I always put it down to a mix of curiosity and humility, giving these people a very good grasp of the fundamentals plus a foothold in almost every area of coding that I could think of.</text></comment>
<story><title>Data science is different now</title><url>https://veekaybee.github.io/2019/02/13/data-science-is-different/</url></story><parent_chain></parent_chain><comment><author>perturbation</author><text>I have been a data scientist for the last 4 years.&lt;p&gt;I think (one of) the problems with the data science career field is that there are a lot of juniors who want to run sklearn and call it a day, following the tutorials that seem to &amp;#x27;just work&amp;#x27; that real-world data doesn&amp;#x27;t without a fight.&lt;p&gt;To get value out of the work, you have to be methodical, careful, and really dig into the data.  The observation that 85% of the time is cleaning doesn&amp;#x27;t eliminate the need to know what you&amp;#x27;re doing, what approaches to use, how to judge success, how to communicate results, etc.&lt;p&gt;Another thing to consider:
I&amp;#x27;ve found big, boring companies are usually better to do DS at than small ones.  Big, boring companies have better discipline in collecting and managing data.  Also, a 1% improvement to an existing process matters a lot at BigCo, and very little at a startup - and a lot of DS models are that sort of incremental progress over rules engines or heuristics.</text></comment>