Data Skeptic

This podcast mini-episode discusses the technique of Cross Validation, which involves splitting a dataset into small partitions, training a model, and validating its predictive power. The hosts explore the importance of models, good fit, and the process of training. They highlight the significance of cross-validation in data science to avoid overfitting and improve predictive power, using examples such as predicting sales data and training a jazz music classifier. Finally, they explain the concept of cross-validation in machine learning and its usefulness for limited or new data.

This episode features a discussion with statistics PhD student Zach Seeskin about a project he was involved in as part of the Eric and Wendy Schmidt Data Science for Social Good Summer Fellowship.  The project involved exploring the relationship (if any) between streetlight outages and crime in the City of Chicago.  We discuss how the data was accessed via the City of Chicago data portal, how the analysis was done, and what correlations were discovered in the data.  Won't you listen and hear what was found? 

This episode loosely explores the topic of Experimental Design including hypothesis testing, the importance of statistical tests, and an everyday and business example.

In this week's episode, we discuss applied solutions to big data problem with big data engineer Jay Shankar.  The episode explores approaches and design philosophy to solving real world big data business problems, and the exploration of the wide array of tools available.  

This podcast discusses Bayesian Updating, exploring how beliefs change based on new evidence. It uses examples of searching for keys, discovering a pomegranate, and using bags of fruits to understand belief updates. The concept of Bayesian updating and probability is explored, along with the use of Bayes' theorem.

In the second full length episode of the podcast, we discuss the current state of personalized medicine and the advancements in genetics that have made it possible.

This podcast explores the concept of p-values in hypothesis testing using an experiment on plant flowering. It emphasizes the importance of large sample sizes and statistically significant results. The significance of p-values in scientific experiments and different alpha values for rejecting the null hypothesis are discussed. The podcast also covers controlling for multiple comparisons with the Bonferoni correction and the limitations of statistical significance using the movie 'Particle Field' as a reference.

A conversation with Convertro's Nathan Janos about methodologies used to help advertisers understand the affect each of their marketing efforts (print, SEM, display, skywriting, etc.) contributes to their overall return.

In this first mini-episode of the Data Skeptic Podcast, we define and discuss type i and type ii errors (a.k.a. false positives and false negatives).

The Data Skeptic Podcast features conversations with topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches. This first episode is a short discussion about what this podcast is all about.