69

My question: is having two small, heavily intertwined sites better than one large one related to 'data analysis' in all its forms? For what it's worth, my answer is 'no'. If anything, I think Data Science should be merged into Cross Validated. I can respect that some people would want to keep the engineering and theory separate (I'll let them make that ...


30

To my mind, elementary is too slippery a criterion for closing. I don't doubt there are many questions that are elementary for @MichaelChernick that would be over my head, and I don't necessarily think they should be closed. Below the pictured comment, he replies, Elementary questions are likely to have been answered many times in possible duplicates. ...


29

I would lean towards allowing such questions. For example, I answered such a question here: Difference in output between SAS's proc genmod and R's glm. My general position is that whether a question should be viewed as off topic should be determined by what the OP needs explained: if the required explanation is statistical, it's best here, but if the ...


28

I downvoted your question because the part about job-related questions doesn't make sense, and it's unrelated to the site policy: this is a Statistics Q&A - nowhere in the list of questions one should avoid asking is it mentioned that questions related to the OP's job must not be asked. The only valid criterion to judge a question is whether it is a ...


28

TL;DR Machine learning, deep learning and reinforcement learning are all on-topic here, but we ask that questions not be primarily concerned with programming. Preamble I'm not active on DSCI.SE or AI.SE so I have no deep understanding of what is on-topic on those fora. However, I have been active on stats.SE for several years and I regularly participate ...


27

Many of the good questions on DS.SE would be well-suited for CrossValidated. The rest are either a) suited just fine for either StackOverflow or SciComp, or b) just plain bad questions anyway. Fact (read: strong opinion) is, the data science site shouldn't exist as a separate entity. The way I see it, its existence can affect CrossValidated somewhere along ...


27

A question should be judged based on whether it is a good statistics problem. Statistics are used in business, in academic research, in politics, in volunteer activities, and by some really weird people (ahem...) for fun and recreation. Whether a question posted to CV concerns a specific one of these use cases should not matter. What should matter is ...


25

Someone raised this issue in Area 51, Overlap with existing sites, with only a little discussion. I would think Cross Validated would be suitable for most Data Science topics, but apparently there is a perception that CV is for theoretical stats questions. The CV description is "a question and answer site for people interested in statistics, machine ...


25

At what point is a question concerning probability abstract enough that it is more appropriate on the math stackexchange instead of crossvalidated? I don't think there's necessarily a point at which this occurs. Probability is on topic and theoretical questions are on topic. Since it will almost certainly be on topic at either, it's a matter of judgement ...


25

I'm sorry, but I don't think this is a useful approach. 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&A will be either a full book-length treatment, or so ...


23

Besides the questions on our site, there's already Computer science, Artificial Intelligence and Data Science (all of which have questions on neural networks - and that's not even counting the neural networks questions on StackOverflow and on Theoretical Computer Science ...). I'm not sure that adding a fifth (or seventh ...) site fielding questions on the ...


23

It is on-topic. Because every comment so far could be an interesting answer to this question I am pasting them here: Nick Cox: The tag or term data-preprocessing isn't universal across statistical science (minimally, it's not a term I meet around the place or in literature), but whether and how to deal with outliers, work on transformed scales, etc. seem ...


22

The answer is, of course, "it depends", but I think you're asking about something different from what you think you're asking about. I believe we can all agree that a question like Is there an R package for fitting Random Forests? not only is off-topic but also shows a lack of research effort. And likewise we can all agree that randomForest::...


21

On-topic: Terminology As long as it's statistical terminology, I say it's on topic. Lots of statistical ideas have multiple names.


21

On-topic: Notation Notation questions should be on-topic. Notation is critical; it is also often hard to search for. I would be generous with notation that is used in multiple fields such as math and statistics.


21

I don't see any necessary problem with asking a question like that. Here are some things to be sure you do: Provide a complete and correct citation for the paper. Provide a link to the paper. Paste into the body of your question the equation / text you need help understanding (i.e., don't just say 'I need help with equation 5'). Paste in enough of the ...


20

Elementary questions are on topic, and in no way form a basis for closure. It doesn't matter how elementary a question might seem, as long as it fulfills our other criteria for being on topic. Elementary questions are somewhat more likely to fail some of those other criteria - for example they're more likely to be duplicates they're more likely to have not ...


19

On-topic: Machine learning questions Note: This criterion is currently accepted per this post and this post unless it falls below 0 votes. No special criteria regarding this topic have been specified. No criteria have been excluded for this topic


19

I view data cleaning as on-topic here: it is a fundamental, if somewhat unglamorous (and underappreciated) aspect of data analysis. But, in keeping with this site's approach and philosophy, we would want to welcome answers that focus on principles and generalities which would apply regardless of one's computing platform, and migrate any threads that ...


19

I agree with @ssdecontrol. I am generally skeptical of "data science" as the big new thing. I do see that there is a place for the discussion / development of some new issues that have arisen in the internet age (pertaining to how to implement analyses when the amount of data is so large that it cannot be fit on a computer), but much of the buzz about DS ...


19

Questions about the machine learning algorithms (as long as they're actually about the algorithms and not about specific implementations) are clearly on topic here. In this case the fact that the data is coming from vision will probably be relevant, but may not be central to the question. Questions about what image manipulations make sense to perform are ...


18

The endless variations on "How do I analyze these data?" are not answerable questions. Full stop. When I first started my career, I worked as a consultant specializing in data science. I didn't know much about math and machine learning, but I learned. (I was a "unicorn" that had some statistics background and some subject matter knowledge which made me ...


17

I think that's a good idea. Perhaps we can go one better and migrate their questions to OpenData.SE.


17

Technical statistical usage sometimes departs from colloquial English usage. (Examples abound, such as the transitive use of "to learn" in the ML community and the use of singular nouns among statisticians to refer to entire families of distributions. And that's not even touching on the severely technical meanings of "confidence," "error," "fit," "...


17

Yes. Questions on how to simulate data should be on-topic.


17

As a beginning user, I would like to give my opinion on this and why I strongly favour (3) if it is possible to write such an answer clearly. An example where I learned a lot from reading a "wrong" question is this one by @gung. Write an answer that essentially explains why the question itself is mistaken, even if such an answer does not answer the ...


17

SEMNET should just consider posting questions on stats.stackexchange.com, tag them sem or latent-variable or whatever, and their experts should register and answer these questions.


17

Questions about notation have been traditionally considered within our scope, provided the notation is ultimately about statistics / machine learning. In addition, the question of why the exponential is used seems to be a meaningful ML question. I don't see that it needs to be closed. I just reopened it.


16

Optimization per se isn't part of our site's interests. Like Calculus, Linear Algebra, Computer Science, and other disciplines, it is a tool we use, respect, and enjoy, but is not otherwise of interest in its own right. So, just as we tend to send pure Calculus, pure Linear Algebra, etc. questions (which do not have any explicit connection to statistics or ...


16

I like the idea, and I like the current phrasing. I think we'll end up with a system that operates more smoothly with this as a default option. I think having to type a custom comment is sometimes discouraging for people, and so they either go with migration (when it will just have to be closed on SO), or the "not about statistics..." option (which often ...


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