<story><title>Being a Data Scientist: My Experience and Toolset</title><url>https://jeffersonheard.github.io/2017/01/being-a-data-scientist-my-experience-and-toolset/</url></story><parent_chain><item><author>achompas</author><text>These types of posts validate my concern about the people entering my field right now.&lt;p&gt;Data science, as a line of work, is distinct from other technical roles in its focus on &lt;i&gt;creating business value using machine learning and statistics&lt;/i&gt;. This quality is easily observed in the most successful data scientists I&amp;#x27;ve worked with (whether at unicorn startups, big companies like my current employer, or &amp;quot;mission-driven&amp;quot; companies).&lt;p&gt;Implicit in this definition is &lt;i&gt;avoiding the destruction of business value by misapplying ML&amp;#x2F;statistics&lt;/i&gt;. In that sense, I am concerned about blog posts like these (which list 50 libraries and zero textbooks or papers) and those who comment arguing the relevance of &amp;quot;real math&amp;quot; in the era of computers.&lt;p&gt;Speaking bluntly: if you are a &amp;quot;data scientist&amp;quot; that can&amp;#x27;t derive a posterior distribution or explain the architecture of a neural network in rigorous detail, you&amp;#x27;re only going to solve easy problems amenable to black-box approaches. This is code for &amp;quot;toss things into pandas and throw sklearn at it&amp;quot;. I would look for a separate line of work.</text></item></parent_chain><comment><author>SatvikBeri</author><text>I think the &amp;quot;Data Scientist&amp;quot; job title is overloaded–I see several clusters of skills being useful, and in my ideal world they would have similar but slightly different job titles:&lt;p&gt;–Medium Stats&amp;#x2F;ML, medium Engineering (&amp;quot;Data Scientist&amp;quot; or &amp;quot;Data Engineer&amp;quot;)&lt;p&gt;–High Engineering on very large datasets, low&amp;#x2F;medium Stats&amp;#x2F;ML (&amp;quot;Data Engineer&amp;quot; or &amp;quot;Backend Engineer&amp;quot;)&lt;p&gt;–High Analysis, medium Stats&amp;#x2F;ML, low Engineering (&amp;quot;Analyst&amp;quot;)&lt;p&gt;–High traditional Stats, High Analysis, low ML&amp;#x2F;Engineering (&amp;quot;Statistician&amp;quot;)&lt;p&gt;–High ML, medium Stats, medium Analysis (&amp;quot;Data Scientist&amp;quot;)&lt;p&gt;–High ML, medium Engineering (&amp;quot;Machine Learning Engineer&amp;quot;)</text></comment>
<story><title>Being a Data Scientist: My Experience and Toolset</title><url>https://jeffersonheard.github.io/2017/01/being-a-data-scientist-my-experience-and-toolset/</url></story><parent_chain><item><author>achompas</author><text>These types of posts validate my concern about the people entering my field right now.&lt;p&gt;Data science, as a line of work, is distinct from other technical roles in its focus on &lt;i&gt;creating business value using machine learning and statistics&lt;/i&gt;. This quality is easily observed in the most successful data scientists I&amp;#x27;ve worked with (whether at unicorn startups, big companies like my current employer, or &amp;quot;mission-driven&amp;quot; companies).&lt;p&gt;Implicit in this definition is &lt;i&gt;avoiding the destruction of business value by misapplying ML&amp;#x2F;statistics&lt;/i&gt;. In that sense, I am concerned about blog posts like these (which list 50 libraries and zero textbooks or papers) and those who comment arguing the relevance of &amp;quot;real math&amp;quot; in the era of computers.&lt;p&gt;Speaking bluntly: if you are a &amp;quot;data scientist&amp;quot; that can&amp;#x27;t derive a posterior distribution or explain the architecture of a neural network in rigorous detail, you&amp;#x27;re only going to solve easy problems amenable to black-box approaches. This is code for &amp;quot;toss things into pandas and throw sklearn at it&amp;quot;. I would look for a separate line of work.</text></item></parent_chain><comment><author>teej</author><text>Or, just like software engineering or any other profession in the world, there&amp;#x27;s going to be a need for people to solve hard problems and people to solve easy problems. Data science isn&amp;#x27;t different.</text></comment>