<p>I would like to reopen the discussion regarding CV and the Data Science beta. This question is related to this previous one: <a href="https://stats.meta.stackexchange.com/questions/2026/data-science-se">Data Science SE</a> but now with a better view of where Data Science seems to be going. I was inspired to make this post because of <a href="https://stats.stackexchange.com/questions/126403/crossvalidated-vs-datascience-what-is-different">https://stats.stackexchange.com/questions/126403/crossvalidated-vs-datascience-what-is-different</a>.</p>

<p>The difference between CV and data science appears to be that CV focuses on data analysis theory (statistics, machine learning and math to a lesser extent) while data science focuses on (big) data analysis in practice (software frameworks, databases, languages). At least on paper.</p>

<p>CV's mission statement:</p>

<blockquote>
  <p>Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization.</p>
</blockquote>

<p>Data Science's mission statement:</p>

<blockquote>
  <p>Data Science Stack Exchange is a question and answer site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field.</p>
</blockquote>

<p>These mission statements are pretty vague but already there one can immediately see tremendous overlap. I think historically the need for Data Science arose because CV rejected 'implementation-related' questions. That may have been a mistake.</p>

<hr>

<p>I am not convinced that these should be separate, especially considering the evolution of data science. Maybe CV is focusing on independence of statistics too much. </p>

<p>Data Science is getting <em>a lot</em> of theoretical questions which should probably have ended up here (most are in fact already answered here), some examples:</p>

<ul>
<li><a href="https://datascience.stackexchange.com/questions/2582/consequence-of-feature-scaling">Consequence of feature scaling</a></li>
<li><a href="https://datascience.stackexchange.com/questions/2513/please-enlighten-me-with-platts-smo-algorithm-for-svm">Please enlighten me with Platt's SMO algorithm for SVM</a></li>
<li><a href="https://datascience.stackexchange.com/questions/2258/where-to-start-on-neural-networks">Where to start on neural networks</a></li>
<li><a href="https://datascience.stackexchange.com/questions/736/skewed-multi-class-data">Skewed multi-class data</a></li>
<li><a href="https://datascience.stackexchange.com/questions/410/choosing-a-learning-rate">Choosing a learning rate</a></li>
<li><a href="https://datascience.stackexchange.com/questions/22/k-means-clustering-for-mixed-numeric-and-categorical-data">K-means clustering for mixed numeric and categorical data</a></li>
<li><a href="https://datascience.stackexchange.com/questions/806/advantages-of-auc-vs-standard-accuracy">Advantages of AUC vs standard accuracy</a></li>
</ul>

<p>The list goes on. If we consider the list given at <a href="https://datascience.meta.stackexchange.com/a/15/119">Data Science meta</a> to be used, such questions would fit on both venues. The idea that (a non-trivial amount of) questions may well end up on either site is in direct contradiction to the overall mission of StackExchange sites (e.g. to provide a single place to answer certain questions that cannot be found in other places).</p>

<p>On CV we are (fairly) consistently closing questions that belong on data science while they appear to not be doing the same. Essentially this boils down to '<em>when in doubt, ask at Data Science</em>'. This is just an observation, don't consider this to be a complaint or accusation. It seems to me that CV needs better PR at least. </p>

<hr>

<p>My question: <strong>is having two small, heavily intertwined sites better than one large one related to 'data analysis' in all its forms?</strong> StackOverflow has shown that a single go-to point for programming stuff has worked tremendously well, so maybe the equivalent for data analysis has its merits? From a new user's perspective, it would make a lot more sense.</p>
