Brief reminder: the weekly Data Links article is the place where I gather all the links involving data science (and some other unrelated topics) that I found interesting during last week. Unless there is some major disruption, you can expect to find it every Monday (time might vary) here.
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Kimono + MonkeyLearn: sentiment analysis with machine learning and web scraped data.
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It is always a pleasure to watch Peter Norvig coding, or read his code. Here he simulates how different wealth redistribution methods work for the overwall economy. Bottomline: if we don't redistribute properly, a Third World-like situation is waiting for us, and soon.
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[PDF] Reading About the Financial Crisis: A 21-Book Review. From the article: The recent financial crisis has generated many distinct perspectives from various quarters. In this article, I review a diverse set of 21 books on the crisis, 11 written by academics, and 10 written by journalists and one former Treasury Secretary. No single narrative emerges from this broad and often contradictory collection of interpretations, but the sheer variety of conclusions is informative, and underscores the desperate need for the economics profession to establish a single set of facts from which more accurate inferences and narratives can be constructed
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[Book, PDF] Fundamental Statistical Concepts in Presenting Data. Principles for Constructing Better Graphics.
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It's already ten years since the Sony rootkit fiasco!
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Automating big-data analysis. From the article: MIT researchers aim to take the human element out of big-data analysis, with a new system that not only searches for patterns but designs the feature set, too. To test the first prototype of their system, they enrolled it in three data science competitions, in which it competed against human teams to find predictive patterns in unfamiliar data sets. Of the 906 teams participating in the three competitions, the researchers’ “Data Science Machine” finished ahead of 615. Here you can find the PDF to the scientific paper. I'd say humans are still needed for these tasks, but may be your data analyst job won't be there for much longer.