Item Response Theory: How Smart ARE You?

Psychometrics is all about measuring the psychological characteristics of people; for example, scholastic aptitude.  How is this done?  Tests, of course!  But there's a chicken-and-egg problem here: you need to know both how hard a test is, and how smart the test-taker is, in order to get the results you want.  How to solve this problem, one equation with two unknowns?  Item response theory--the data science behind such tests and the GRE.

Relevant links: 
https://en.wikipedia.org/wiki/Item_response_theory

Great Social Networks in History

The Medici were one of the great ruling families of Europe during the Renaissance.  How did they come to rule?  Not power, or money, or armies, but through the strength of their social network.  And speaking of great historical social networks, analysis of the network of letter-writing during the Enlightenment is helping humanities scholars track the dispersion of great ideas across the world during that time, from Voltaire to Benjamin Franklin and everyone in between.

Relevant links:
https://www2.bc.edu/~jonescq/mb851/Mar12/PadgettAnsell_AJS_1993.pdf
http://republicofletters.stanford.edu/index.html

Going Once, Going Twice: Auctions Part 1

The Google AdWords algorithm is (famously) an auction system for allocating a massive amount of online ad space in real time--with that fascinating use case in mind, this episode is part one in a two-part series all about auctions.  We dive into the theory of auctions, and what makes a "good" auction.   

Relevant links:
https://en.wikipedia.org/wiki/English_auction
http://people.ischool.berkeley.edu/~hal/Papers/2006/position.pdf
http://www.benedelman.org/publications/gsp-060801.pdf

Zipf's Law

Zipf's law is related to the statistics of how word usage is distributed.  As it turns out, this is also strikingly reminiscent of how income is distributed, and populations of cities, and bug reports in software, as well as tons of other phenomena that we all interact with every day.

Relevant links:
http://economix.blogs.nytimes.com/2010/04/20/a-tale-of-many-cities/
http://arxiv.org/pdf/cond-mat/0412004.pdf
https://terrytao.wordpress.com/2009/07/03/benfords-law-zipfs-law-and-the-pareto-distribution/

A Criminally Short Introduction to Semi-Supervised Learning

Because there are more interesting problems than there are labeled datasets, semi-supervised learning provides a framework for getting feedback from the environment as a proxy for labels of what's "correct."  Of all the machine learning methodologies, it might also be the closest to how humans usually learn--we go through the world, getting (noisy) feedback on the choices we make and learn from the outcomes of our actions.  

Link: David Silver's Reinforcement Learning course

The State of Data Science

How many data scientists are there, where do they live, where do they work, what kind of tools do they use, and how do they describe themselves?  RJMetrics wanted to know the answers to these questions, so they decided to find out and share their analysis with the world.  In this very special interview episode, we welcome Tristan Handy, VP of Marketing at RJMetrics, who will talk about "The State of Data Science Report."

Data Science for Making the World a Better Place

There's a good chance that great data science is going on close to you, and that it's going toward making your city, state, country, and planet a better place.  Not all the data science questions being tackled out there are about finding the sleekest new algorithm or billion-dollar company idea--there's a whole world of social data science that just wants to make the world a better place to live in.

Kalman Runners

The Kalman Filter is an algorithm for taking noisy measurements of dynamic systems and using them to get a better idea of the underlying dynamics than you could get from a simple extrapolation.  If you've ever run a marathon, or been a nuclear missile, you probably know all about these challenges already.  By the way, we neglected to mention in the episode: Katie's marathon time was 3:54:27!