<p>I have been on this stackexchange long enough now to see there is quite a lot of questions along the lines of:</p>

<blockquote>
  <p>Help, I'm a data science intern/amateur statistician and I've been given a load of data from my boss/professor and told to look for insights/trends, and I've spent half a day browsing wikipedia/stackexchange and now i'm really confused. What do I do?"</p>
</blockquote>

<p>General features of these questions:</p>

<ul>
<li>The asker is a first time user with 1 rep point.</li>
<li>The asker has been tasked with this by someone high up who has noticed the buzz surrounding Machine Learning and thinks this is definitely something their company ought to be doing, but doesn't know how to go about it.</li>
<li>The asker is not a professional statistician and previously had no idea how many different techniques there were.</li>
</ul>

<p><strong>As "Machine learning" as a corporate buzzword continues to gain widespread use and as neural networks and other AI techniques continue to produce miraculous and highly visible results for big tech companies, we will continue to see an ever-increasing influx of these questions, no matter how punitively we downvote or close them.</strong> </p>

<p><em>As such, I think there should be at least one example of a well-worded question of this nature that has a good answer that we can point to</em>, involving some general rules of thumb about how to do data analysis - stuff about where to start, checking the data is clean, graphing it to get a sense of what's going on, a couple of basic pitfalls, etc.</p>

<p>After that, a series of links on specific topics, or where to go to get help. The trickiest part to answer is the dreaded "what projects should I try with this data?". This part can probably be answered only anecdotally, with specific examples/links of worthwhile DS undertakings at other companies, along with general platitudes about trying to understand the company's needs and difficulties. </p>

<p>Downvoting and closing as "too broad" without giving any useful feedback to the asker will not curtail the influx of these sorts of questions. It merely discourages the new user from asking questions here in future. </p>

<p>There <a href="https://stats.stackexchange.com/questions/355647/what-data-science-problems-can-be-solved-with-bookings-billings-and-backlog-data?noredirect=1#comment669848_355647">is a specific question I have in mind</a> that I want to have a go at answering, but I can't because it is put on hold. I am happy to have a look for a good "What do I do?" question and develop a generic answer that will hopefully be useful for future searchers.</p>

<p><strong>My general point is, these questions will be asked whether we want them to or not, so we ought to at least make some minimally helpful generic answer that we can use to point them in the right direction.</strong></p>
