Guerilla Skepticism on Wikipedia with Susan Gerbic

Yusan Lin shares her research on using data science to explore the fashion industry in this episode. She has applied techniques from data mining, natural language processing, and social network analysis to explore who are the innovators in the fashion world and how their influence effects other designers. If you found this episode interesting and would like to read more, Yusan's papers Text-Generated Fashion Influence Model: An Empirical Study on Style.com and The Hidden Influence Network in the Fashion Industry are worth reading.

14 Aug 201524min

PageRank is the algorithm most famous for being one of the original innovations that made Google stand out as a search engine. It was defined in the classic paper The Anatomy of a Large-Scale Hypertextual Web Search Engine by Sergey Brin and Larry Page. While this algorithm clearly impacted web searching, it has also been useful in a variety of other applications. This episode presents a high level description of this algorithm and how it might apply when trying to establish who writes the most influencial academic papers.

7 Aug 20158min

In this episode, Benjamin Uminsky enlightens us about some of the ways the Los Angeles County Registrar-Recorder/County Clerk leverages data science and analysis to help be more effective and efficient with the services and expectations they provide citizens. Our topics range from forecasting to predicting the likelihood that people will volunteer to be poll workers. Benjamin recently spoke at Big Data Day LA. Videos have not yet been posted, but you can see the slides from his talk Data Mining Forecasting and BI at the RRCC if this episode has left you hungry to learn more. During the show, Benjamin encouraged any Los Angeles residents who have some time to serve their community consider becoming a pollworker.

29 Juli 201541min

This episode explores the k-nearest neighbors algorithm which is an unsupervised, non-parametric method that can be used for both classification and regression. The basica concept is that it leverages some distance function on your dataset to find the $k$ closests other observations of the dataset and averaging them to impute an unknown value or unlabelled datapoint.

24 Juli 20158min

How do people think rationally about small probability events? What is the optimal statistical process by which one can update their beliefs in light of new evidence? This episode of Data Skeptic explores questions like this as Kyle consults a cast of previous guests and experts to try and answer the question "What is the probability, however small, that Bigfoot is real?"

17 Juli 20151h 24min

This mini-episode is a high level explanation of the basic idea behind MapReduce, which is a fundamental concept in big data. The origin of the idea comes from a Google paper titled MapReduce: Simplified Data Processing on Large Clusters. This episode makes an analogy to tabulating paper voting ballets as a means of helping to explain how and why MapReduce is an important concept.

10 Juli 201512min

The Credible Hulk joins me in this episode to discuss a recent blog post he wrote about glyphosate and the data about how it's introduction changed the historical usage trends of other herbicides. Links to all the sources and references can be found in the blog post. In this discussion, we also mention the food babe and Last Thursdayism which may be worth some further reading. Kyle also mentioned the list of ingredients or chemical composition of a banana. Credible Hulk mentioned the Mommy PhD facebook page. An interesting article about Mommy PhD can be found here. Lastly, if you enjoyed the show, please "Like" the Credible Hulk facebook group.

3 Juli 201534min

More features are not always better! With an increasing number of features to consider, machine learning algorithms suffer from the curse of dimensionality, as they have a wider set and often sparser coverage of examples to consider. This episode explores a real life example of this as Kyle and Linhda discuss their thoughts on purchasing a home. The curse of dimensionality was defined by Richard Bellman, and applies in several slightly nuanced cases. This mini-episode discusses how it applies on machine learning. This episode does not, however, discuss a slightly different version of the curse of dimensionality which appears in decision theoretic situations. Consider the game of chess. One must think ahead several moves in order to execute a successful strategy. However, thinking ahead another move requires a consideration of every possible move of every piece controlled, and every possible response one's opponent may take. The space of possible future states of the board grows exponentially with the horizon one wants to look ahead to. This is present in the notably useful Bellman equation.

26 Juni 201510min