Data Skeptic

Love and Data is the continued theme in this mini-episode as we discuss the game theory example of The Battle of the Sexes. In this textbook example, a couple must strategize about how to spend their Friday night. One partner prefers football games while the other partner prefers to attend the opera. Yet, each person would rather be at their non-preferred location so long as they are still with their spouse. So where should they decide to go?

Can algorithms help you find love? Many happy couples successfully brought together via online dating websites show us that data science can help you find love. I'm joined this week by Thomas Levi, Senior Data Scientist at Plenty of Fish, to discuss some of his work which helps people find one another as efficiently as possible.

Matchmaking is a truly non-trivial problem, and one that's dynamically changing all the time as new users join and leave the "pool of fish". This episode explores the aspects of what makes this a tough problem and some of the ways POF has been successfully using data science to solve it, and continues to try to innovate with new techniques like interest matching.

For his benevolent references, Thomas suggests readers check out All of Statistics as well as the caret library for R. And for a self serving recommendation, follow him on twitter (@tslevi) or connect with Thomas Levi on Linkedin.

Economist Peter Backus put forward "The Girlfriend Equation" while working on his PhD - a probabilistic model attempting to estimate the likelihood of him finding a girlfriend. In this mini episode we explore the soundness of his model and also share some stories about how Linhda and Kyle met.

I'm joined this week by Alex Boklin to explore the topic of magical thinking especially in the context of Rhonda Byrne's "The Secret", and the similarities it bears to The Global Consciousness Project (GCP). The GCP puts forward the hypothesis that random number generators elicit statistically significant changes as a result of major world events.

What is randomness? How can we determine if some results are randomly generated or not? Why are random numbers important to us in our everyday life? These topics and more are discussed in this mini-episode on random numbers.

Many readers will be vaguely familar with the idea of "X number of monkeys banging on Y number of typewriters for Z number of years" - the idea being that such a setup would produce random sequences of letters. The origin of this idea was the mathemetician Borel who was interested in whether or not 1,000,000 monkeys working for 10 hours per day might eventually reproduce the works of shakespeare.

We explore this topic and provide some further details in the show notes which you can find over at dataskeptic.com

This week's episode explores the possibilities of extracting novel insights from the many great social web APIs available. Matthew Russell's Mining the Social Web is a fantastic exploration of the tools and methods, and we explore a few related topics.

One helpful feature of the book is it's use of a Vagrant virtual machine. Using it, readers can easily reproduce the examples from the book, and there's a short video available that will walk you through setting up the Mining the Social Web virtual machine.

The book also has an accompanying github repository which can be found here.

A quote from Matthew that particularly reasonates for me was "The first commandment of Data Science is to 'Know thy data'." Take a listen for a little more context around this sage advice.

In addition to the book, we also discuss some of the work done by Digital Reasoning where Matthew serves as CTO. One of their products we spend some time discussing is Synthesys, a service that processes unstructured data and delivers knowledge and insight extracted from the data.

Some listeners might already be familiar with Digital Reasoning from recent coverage in Fortune Magazine on their cognitive computing efforts.

For his benevolent recommendation, Matthew recommends the Hardcore History Podcast, and for his self-serving recommendation, Matthew mentioned that they are currently hiring for Data Science job opportunities at Digital Reasoning if any listeners are looking for new opportunities.

This episode explores the basis of why we can trust encryption. Suprisingly, a discussion of looking up a word in the dictionary (binary search) and efficiently going wine tasting (the travelling salesman problem) help introduce computational complexity as well as the P ?= NP question, which is paramount to the trustworthiness RSA encryption.

With a high level foundation of computational theory, we talk about NP problems, and why prime factorization is a difficult problem, thus making it a great basis for the RSA encryption algorithm, which most of the internet uses to encrypt data. Unlike the encryption scheme Ray Romano used in "Everybody Loves Raymond", RSA has nice theoretical foundations.

It should be noted that although this episode gives good reason to trust that properly encrypted data, based on well choosen public/private keys where the private key is not compromised, is safe. However, having safe encryption doesn't necessarily mean that the Internet is secure. Topics like Man in the Middle attacks as well as the Snowden revelations are a topic for another day, not for this record length "mini" episode.

Jeff Stanton joins me in this episode to discuss his book An Introduction to Data Science, and some of the unique challenges and issues faced by someone doing applied data science. A challenge to any data scientist is making sure they have a good input data set and apply any necessary data munging steps before their analysis. We cover some good advise for how to approach such problems.

The t-test is this week's mini-episode topic. The t-test is a statistical testing procedure used to determine if the mean of two datasets differs by a statistically significant amount. We discuss how a wine manufacturer might apply a t-test to determine if the sweetness, acidity, or some other property of two separate grape vines might differ in a statistically meaningful way.

Check out more details and examiles found in the show notes linked below.

https://dataskeptic.com/blog/episodes/2014/t-test

This week I'm joined by Karl Mamer to discuss the data behind three well known urban legends. Did a large blackout in New York and surrounding areas result in a baby boom nine months later? Do subliminal messages affect our behavior? Is placing beer alongside diapers a recipe for generating more revenue than these products in separate locations? Listen as Karl and I explore these claims.