For explanations about the methodology, including source code and possible improvements, read our main article on this subject. It also provides links to our other three listings.

The field between parentheses represents the year when the website in question was first mentioned - it does not represent when the website was created, thought it's a good proxy to tell how old the website is. The member database goes as far back as 2007. The list of keywords attached to each website represents which seed keywords were found on the front page, when crawling the website. The number of stars (1, 2 or 3) represents how popular the website is: it's an indicator of how many members mentioned it. Of course, brand new websites might not have 3 stars yet. 

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Comment by Ayushi Mishra on November 9, 2016 at 8:20pm

I want to ask a question that what type of organisations prefers data science students for jobs as i am interested big data training in noidato learn it so want to know about its jobs

Comment by Theodore Omtzigt on April 5, 2015 at 4:50am

What would be a methodology that could organize these sites into purpose, influence, and impact in progressing data science?

I can postulate a hundred hypothesis of what would go into purpose, influence, and impact, but in pure data science fashion, is there a mechanism to have the data do the talking?

A purpose score feels like the easiest as each website can be dissected in messaging silos, but resolving the myriad activities of broad organizations such as SAS and IBM will be rive with complications. The more interesting questions would be around influence and impact. That clearly will require mashing up additional data sets that measure economic impact and social networking cliques.

What would be a good methodology to peel the next layer of the onion?

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