68

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 ...


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 ...


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 ...


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

In your vote to close you can indicate where you think the post should be migrated. In processing that vote, diamond mods can select any site for migration and usually follow suggestions (because we're grateful for the thought that went into them in the first place). The system limits how many sites can appear as targets for redirection. I recall we are at ...


18

The official statement of what is on-topic on CV is that we address questions about: statistical analysis, applied or theoretical designing experiments collecting data data mining machine learning visualizing data probability theory mathematical statistics statistical and data-driven computing It is OK if ...


15

Spoiler: a highly opinionated answer follows. I am here with Peter Flom. I think questions on how to handle stuff in statistical packages deserve a site that is separate from CV and separate from SO. I would be the first to admit that I am behind on any of the Data Science issues involving Hadoop or Mahout -- I don't get to see the limits of my existing ...


13

I also think having another closely related site creates more problems than it solves and that the data science site is an ill-conceived project. Others have already outlined the main reasons for that. But this is at least the third attempt at creating another site (two “machine learning” attempts and now “data science”) in spite of warnings that the ...


12

Update September 18, 2014 (data are for public Beta) \begin{array}{| r | r | r |} \hline & \text {CV} & \text{Data Science} & \text{Data Science}\\ \hline \hline \text {Metric} & \text {end of Beta} & \text{80 days in Beta} & \text{128 days in Beta}\\ \hline ...


11

Edit: The site has now launched. Old post: I lurked around there and want to share my preliminary, biased impression: Judging from the first few questions there is a lot of overlap and redundancy with SO and CV. Case in point: the five most recent questions were R vs Python, sentiment analysis, calculating AUC, extract rows in R, number of layers in ...


11

Machine learning is on-topic here at Cross Validated—see What topics can I ask about here?, What is on topic on Cross Validated?, Are the “Machine Learning” questions on topic?, & Is machine learning a part of statistical analysis? & Should Machine Learning SE be merged with CrossValidated?. That doesn't seem to have ever been contested. (It's ...


11

I'm relatively new to CV, and have very little history with SE, but as a practitioner with feet in both statistics and data science (you can put those in quotes if you want, I usually do since among practitioners both can be so broad you could have "statisticians" talking better about SQL than they do about MLE and "data scientists" rabid about Hadoop and ...


11

I had not seen that the new Data Science site had been created. This is a huge mistake and should never have happened. This will cause major confusion among a huge number of users and will make me question my participation. Can this error be corrected before more damage is done? How do we reverse course? Data Science is at least 1/2 Statistics. It ...


11

The migration dialog provides a short list of target sites. These are the sites all users may choose as likely targets. Because it's important to get along with other sites, it is crucial that all our users with vote-to-migrate privileges well understand the on-topic criteria at those sites. For years the only options have been SO, Math, and our own Meta ...


9

It says so on StackOverflow, so it must be true!


9

This question & related ones have also been discussed on the Data Science & Stack Exchange Meta sites, & on Area 51: How is Data Science Different From Cross-Validated? How is this site different from CrossValidated What characterises the difference between data science and statistics? Difference between the Cross Validated and Data Science SE ...


9

I think my answer may have the advantage of seeing how things have played out in comparison to others who have answered earlier, but... I think the data science site is very necessary. It's true that there's plenty of overlap between statistics questions and data science questions. There's also plenty of overlap between statistics questions and mathematics, ...


8

It seems to be on-topic, as long as the question is well worded/asked. For example, based on this search, there are some examples which I think relate to what you are asking: Clustering algorithm for my situation? What is a good algorithm for estimating the median of a huge read-once data set? Online algorithm for mean absolute deviation and large data ...


6

I agree with @amoeba. I see no problem with the question, "How should I use < this algorithm > when the data is large?" If you are asking for code / packages, etc., that would be off topic. But how having so much data it won't fit on your computer affects the usage of a machine learning algorithm seems well within our mandate.


5

I am not sure what about "data science" would not be covered either here or at StackOverflow, but one thing I can think of is programming questions about R and SAS and the like.


4

I think that question, as currently framed, should be considered off topic. The way I think about this is really pretty simple: I ask myself what the OP needs explained. If the answer is something about code / software, then it's off topic; if it's something about statistics / machine learning, then it's on topic, even if the question is asked in terms ...


4

http://data.stackexchange.com/datascience/query/59302/questions-and-answers-per-month Based on the graphs below you might say that this decline in answers seems to be nicely filled up by the data stackexchange. But I do not think that this is like a Gondwanaland falling apart into an Africa and America that fit so nicely together. The answer rate curve of '...


3

This might be interesting given that this discussion was started long ago and some data has become available. Is the trend in stats.SE typical of large sites? Are the trends dependent? Query used: https://meta.stackoverflow.com/questions/350553/data-explorer-query-for-questions-per-year-2009-2016


3

I have initially thought that I agree with @gung (and even upvoted his answer) but the discussion in the comments showed that I actually disagree. I think this question (after the clarifying edits) is on-topic. @Gung writes: If the question were rephrased to ask about general computational strategies for large datasets, or about variable selection, then ...


3

I think that Data Science as a field is quickly sliding into obscurity in terms of applied statistics. It's becoming a hybrid of IT and business analytics. The data scientists are not able to answer AI and ML questions anymore, so they increasingly flow into CV. I see a steady stream of AI questions not only incoming, but also being properly answered. We ...


1

No. The particular question that you refer to should belong to either R or to the databases. The reason why it's not a generic high performance question is because the capabilities to deal with large data sets are very different in different platforms. What may work for R will not necessarily work fro SAS. This depends on the platform architecture, ...


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