<p>The official statement of what is <a href="https://stats.stackexchange.com/help/on-topic">on-topic</a> on CV is that we address questions about: </p>

<blockquote>
  <p><strong>statistical analysis</strong>, applied or theoretical<br>
      <strong>designing experiments</strong><br>
      <strong>collecting data</strong><br>
      <strong>data mining</strong><br>
      <strong>machine learning</strong><br>
      <strong>visualizing data</strong><br>
      <strong>probability theory</strong><br>
      <strong>mathematical statistics</strong><br>
      statistical and data-driven <strong>computing</strong></p>
</blockquote>

<p>It is OK if a question about these topics originated as a homework assignment (or is generally comparable to a homework assignment), but we do have a special <a href="https://stats.stackexchange.com/tags/self-study/info">protocol</a> for such questions.  At any rate, you can feel free to migrate such questions.  </p>

<p>The biggest trouble spot is questions about programming, or using statistical software.  Basically, I think about the issue by asking myself <em>what does the OP need explained?</em>  If the explanation would be about statistics, designing experiments, etc., then it is on topic here, but if it is about how the software works, or how to use it, etc., then it is off topic on CV.  Such questions are sometimes intrinsically ambiguous; more often such questions aren't really ambiguous, but people assume that since software is required for most such work nowadays, we 'ought' to answer these questions anyway.  The issue is that the SE system already has a site for answering coding questions (i.e., Stack Overflow), so coding questions should be sent there, not here.  I think it would be fine if you migrated those questions here that were truly ambiguous; we can always close them or migrate them onward.  </p>

<p>With respect to Data Science.SE's scope, you would do better to ask them than us (just as you are better off ask us about our scope rather than them).  Their scope is <a href="https://datascience.stackexchange.com/help/on-topic">here</a> (although my impression is that they are much looser with their requirements than we are).  With respect to how to distinguish between the sites, note that they also have a <a href="https://datascience.meta.stackexchange.com/q/13">meta.DS thread</a> dedicated to that issue.  The truth is that we rarely migrate questions to DS, and we rarely don't migrate but recommend someone check out DS instead of CV.  My (admittedly somewhat biased) opinion is that most questions are either (1) better asked here, (2) better asked on SO, or (3) poor questions that don't really belong anywhere on the SE system.  Many of the third type do seem to get a favorable reception on DS, because they seem to be looser with their standards&mdash;whether that is good or bad in the long run, I don't know.  The best I could say is that in the answer to their thread distinguishing DS, there are three questions listed as appropriate for DS but not other sites:  </p>

<blockquote>
  <ul>
  <li>Which Big Data technology stack is most suitable for processing tweets, extracting/expanding URLs and pushing (only) new links into 3rd party system?</li>
  <li>Horizontally scaling a distributed database, what should I use for a simple key-value store? Cassandra, HBase or Riak? what are the pros and cons?</li>
  <li>How important is data locality to Hadoop and Map/Reduce?</li>
  </ul>
</blockquote>

<p>I agree that these questions would be considered off topic on CV and SO.  </p>
