How many veterans are there? Memorial Day data

The short answer is there are about 21 million American Veterans as of 2012. As a total, that's about 6.8% of the US population.

For comparison, that's about...

Useful Asset Allocation Data Sources

Working on a project on asset allocation right now, and discovered two excellent references.  The first is a solid guide and introduction to implementing the Black-Litterman model: This reference provides useful implementations in both Excel and Matlab, as well as a discussion of the inputs.  The discussion of the controversial and elusive Tao term is particularly helpful.

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Data-driven foreign-language learning: by the numbers

This is the story of a foreign language data mashup, and how thinking about study-time as an asset with returns can make your language-learning more efficient---in theory.

I am not a linguist, a computational linguist, or a language teacher, but I travel internationally a fair amount and have had reason to half-study a few languages.  In the course of that, I've compared many different approaches and methods as an interested learner.  I've found that with simple audio tapes (like the FSI or Pimsleur) and 6 months of self-study, it's possible for a native English speaker to get to B1 or B2 conversational level in a European language---which isn't much: you can then order coffee and comment on newspaper headlines with some ease.  It takes diligence, but is doable.  However, beyond about that level, you begin to plateau.  At that point, you've learned the grammar, you've mastered the common words, you are confident that you can get around.  Before this, every single word, every single grammatical structure had comparatively large "returns" in the sense that each additional word or rudimentary grammar element came up all the time, and improved remarkably your ability to understand.

The problem is...

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Data Auralization - Listening to the Dow

I was pleased to see this posted in the Alumni Notes of my Alma Mater recently---Justin Joque, a Data Librarian at the University of Michigan, put together a Data "Auralization" of the Dow from 1928 to 2011.  He uses tick data to create two audio bands, one with closing price, and one with trade volume.  Here's the Vimeo Link:

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