Data Skeptic

Kristian Lum (@KLdivergence) joins me this week to discuss her work at @hrdag on predictive policing. We also discuss Multiple Systems Estimation, a technique for inferring statistical information about a population from separate sources of observation. If you enjoy this discussion, check out the panel Tyranny of the Algorithm? Predictive Analytics & Human Rights which was mentioned in the episode.

Distributed computing cannot guarantee consistency, accuracy, and partition tolerance. Most system architects need to think carefully about how they should appropriately balance the needs of their application across these competing objectives. Linh Da and Kyle discuss the CAP Theorem using the analogy of a phone tree for alerting people about a school snow day.

A startup is claiming that they can detect terrorists purely through facial recognition. In this solo episode, Kyle explores the plausibility of these claims.

Goodhart's law states that "When a measure becomes a target, it ceases to be a good measure". In this mini-episode we discuss how this affects SEO, call centers, and Scrum.

I'm joined this week by Jon Morra, director of data science at eHarmony to discuss a variety of ways in which machine learning and data science are being applied to help connect people for successful long term relationships. Interesting open source projects mentioned in the interview include Face-parts, a web service for detecting faces and extracting a robust set of fiducial markers (features) from the image, and Aloha, a Scala based machine learning library. You can learn more about these and other interesting projects at the eHarmony github page. In the wrap up, Jon mentioned the LA Machine Learning meetup which he runs. This is a great resource for LA residents separate and complementary to datascience.la groups, so consider signing up for all of the above and I hope to see you there in the future.

Mystery shoppers and fruit cultivation help us discuss stationarity - a property of some time serieses that are invariant to time in several ways. Differencing is one approach that can often convert a non-stationary process into a stationary one. If you have a stationary process, you get the benefits of many known statistical properties that can enable you to do a significant amount of inferencing and prediction.

I'm joined by Wes McKinney (@wesmckinn) and Hadley Wickham (@hadleywickham) on this episode to discuss their joint project Feather. Feather is a file format for storing data frames along with some metadata, to help with interoperability between languages. At the time of recording, libraries are available for R and Python, making it easy for data scientists working in these languages to quickly and effectively share datasets and collaborate.

Bargaining is the process of two (or more) parties attempting to agree on the price for a transaction.  Game theoretic approaches attempt to find two strategies from which neither party is motivated to deviate.  These strategies are said to be in equilibrium with one another.  The equilibriums available in bargaining depend on the the transaction mechanism and the information of the parties.  Discounting (how long parties are willing to wait) has a significant effect in this process.  This episode discusses some of the choices Kyle and Linh Da made in deciding what offer to make on a house.

Deepjazz is a project from Ji-Sung Kim, a computer science student at Princeton University. It is built using Theano, Keras, music21, and Evan Chow's project jazzml. Deepjazz is a computational music project that creates original jazz compositions using recurrent neural networks trained on Pat Metheny's "And Then I Knew". You can hear some of deepjazz's original compositions on soundcloud.

When working with time series data, there are a number of important diagnostics one should consider to help understand more about the data. The auto-correlative function, plotted as a correlogram, helps explain how a given observations relates to recent preceding observations. A very random process (like lottery numbers) would show very low values, while temperature (our topic in this episode) does correlate highly with recent days.   See the show notes with details about Chapel Hill, NC weather data by visiting:   https://dataskeptic.com/blog/episodes/2016/acf-correlograms