I would like to reopen the discussion regarding CV and the Data Science beta. This question is related to this previous one: Data Science SE but now with a better view of where Data Science seems to be going. I was inspired to make this post because of https://stats.stackexchange.com/questions/126403/crossvalidated-vs-datascience-what-is-different.
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.
CV's mission statement:
Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization.
Data Science's mission statement:
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.
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.
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.
Data Science is getting a lot of theoretical questions which should probably have ended up here (most are in fact already answered here), some examples:
- Consequence of feature scaling
- Please enlighten me with Platt's SMO algorithm for SVM
- Where to start on neural networks
- Skewed multi-class data
- Choosing a learning rate
- K-means clustering for mixed numeric and categorical data
- Advantages of AUC vs standard accuracy
The list goes on. If we consider the list given at Data Science meta 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).
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 'when in doubt, ask at Data Science'. 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.
My question: is having two small, heavily intertwined sites better than one large one related to 'data analysis' in all its forms? 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.