Fri, 30 October 2015
The degree to which two variables change together can be calculated in the form of their covariance. This value can be normalized to the correlation coefficient, which has the advantage of transforming it to a unitless measure strictly bounded between -1 and 1. This episode discusses how we arrive at these values and why they are important.
Fri, 23 October 2015
Today's guest is Cameron Davidson-Pilon. Cameron has a masters degree in quantitative finance from the University of Waterloo. Think of it as statistics on stock markets. For the last two years he's been the team lead of data science at Shopify. He's the founder of dataoragami.net which produces screencasts teaching methods and techniques of applied data science. He's also the author of the just released in print book Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference, which you can also get in a digital form.
This episode focuses on the topic of Bayesian A/B Testing which spans just one chapter of the book. Related to today's discussion is the Data Origami post The class imbalance problem in A/B testing.
Lastly, Data Skeptic will be giving away a copy of the print version of the book to one lucky listener who has a US based delivery address. To participate, you'll need to write a review of any site, book, course, or podcast of your choice on datasciguide.com. After it goes live, tweet a link to it with the hashtag #WinDSBook to be given an entry in the contest. This contest will end November 20th, 2015, at which time I'll draw a single randomized winner and contact them for delivery details via direct message on Twitter.
Fri, 16 October 2015
The central limit theorem is an important statistical result which states that typically, the mean of a large enough set of independent trials is approximately normally distributed. This episode explores how this might be used to determine if an amazon parrot like Yoshi produces or or less waste than an African Grey, under the assumption that the individual distributions are not normal.
Fri, 9 October 2015
Today's guest is Chris Hofstader (@gonz_blinko), an accessibility researcher and advocate, as well as an activist for causes such as improving access to information for blind and vision impaired people. His background in computer programming enabled him to be the leader of JAWS, a Windows program that allowed people with a visual impairment to read their screen either through text-to-speech or a refreshable braille display. He's the Managing Member of 3 Mouse Technology. He's also a frequent blogger primarily at chrishofstader.com.
In a discussion of skepticism and favorite podcasts, Chris mentioned a number of great shows, most notably The Pod Delusion to which he was a contributor. Additionally, Chris has also appeared on The Atheist Nomads.
Fri, 2 October 2015
The multi-armed bandit problem is named with reference to slot machines (one armed bandits). Given the chance to play from a pool of slot machines, all with unknown payout frequencies, how can you maximize your reward? If you knew in advance which machine was best, you would play exclusively that machine. Any strategy less than this will, on average, earn less payout, and the difference can be called the "regret".
You can try each slot machine to learn about it, which we refer to as exploration. When you've spent enough time to be convinced you've identified the best machine, you can then double down and exploit that knowledge. But how do you best balance exploration and exploitation to minimize the regret of your play?
This mini-episode explores a few examples including restaurant selection and A/B testing to discuss the nature of this problem. In the end we touch briefly on Thompson sampling as a solution.