<p>I'm sorry, but I don't think this is a useful approach.</p>

<p>The questions you are referring to are essentially "please teach me data science". There are just far too many possible flavors. If you set up a general question and answer that future questions of this type can be directed to, this "generic" Q&amp;A will be either a full book-length treatment, or so vague as to be meaningless:</p>

<ul>
<li>"Plot your data" - yes, but what should I plot if I have a credit scoring task? Or am trying to forecast a million time series? Or want to cluster stuff in 37 dimensions?</li>
<li>"Clean your data" - how do I clean a time series? Categorical data with two levels? Categorical data with 42 levels? Should I remove? Impute?</li>
<li>"Distinguish between statistical significance, importance and predictive power" - what is this "statistical significance" thing I keep hearing about? Should I do stepwise variable selection? <a href="https://stats.stackexchange.com/q/312780/1352">What quality KPI should I be using for my classification task?</a> For my forecasts, should I use <a href="https://stats.stackexchange.com/questions/tagged/mape" class="post-tag" title="show questions tagged &#39;mape&#39;" rel="tag">mape</a> or <a href="https://stats.stackexchange.com/questions/tagged/mae" class="post-tag" title="show questions tagged &#39;mae&#39;" rel="tag">mae</a> or <a href="https://stats.stackexchange.com/questions/tagged/rmse" class="post-tag" title="show questions tagged &#39;rmse&#39;" rel="tag">rmse</a>?</li>
</ul>

<p>There is a reason why Chemistry.SE does not have a "generic" Q&amp;A on "How do I do a chemical analysis?"</p>

<p>And everything in-between "a full 1000-page tome" and "so vague to be meaningless" has been covered about 1e9 times in MOOCs, tutorials, blog post series and, yes, textbooks that are somewhat shorter than 1000 pages.</p>

<p>So, sorry, I will continue to VTC such questions as "too broad".</p>
