infovore + datamining   5

The Digital Humanities and Interpretation - NYTimes.com
"When another scholar worries that if one begins with data, one can “go anywhere,” Ramsay makes it clear that going anywhere is exactly what he wants to encourage. The critical acts he values are not directed at achieving closure by arriving at a meaning; they are, he says, “ludic” and they are “distinguished … by a refusal to declare meaning in any form.” The right question to propose “is not ‘What does the text mean?’ but, rather, ‘How do we ensure that it keeps on meaning’ — how … can we ensure that our engagement with the text is deep, multifaceted, and prolonged?”" Which is interesting, as is the whole article - the author is not convinced by the 'digital humanities', but he still links to some very interesting stuff about algorithmic criticism.
humanities  literature  criticism  literarycriticism  algorithms  data  datamining 
january 2012 by infovore
The Seven Secrets of Successful Data Scientists : Dataspora Blog
"...don’t confuse this kind of data exploration, where the goal is to size up the data, with building proper data plumbing, where you want robustness and maintainability. Perl and bash scripts are nice for the former, but can be a nightmare for building data pipelines." Lots of good stuff in this article; this was a highlight.
bigdata  data  datamining  statistics  machinelearning 
september 2010 by infovore
Guide to Getting Started in Machine Learning | A Beautiful WWW
"Someone at work recently asked how he should go about studying machine learning on his own. So I’m putting together a little guide." Ooh, useful. Lots of starting points for machine learning in R.
r  datamining  programming  machinelearning  statistics 
october 2009 by infovore
The Three Sexy Skills of Data Geeks : Dataspora Blog
"Statisticians’ sex appeal has little to do with their lascivious leanings ... and more with the scarcity of their skills. I believe that the folks to whom Hal Varian is referring are not statisticians in the narrow sense, but rather people who possess skills in three key, yet independent areas: statistics, data munging, and data visualization. (In parentheses next to each, I’ve put the salient character trait needed to acquire it)."
data  analytics  visualization  statistics  datamining  maths  analysis  trends 
june 2009 by infovore

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