An earlier version of this post is on the RISELab blog. It is posted here with the permission of the authors.
Scraping the Turkey Accordion
I Spy with my Graphing Eye 📊 👁️
I Spy with my Graphing Eye 📊 ��
Distilled News
The Seductive Diversion of ‘Solving’ Bias in Artificial Intelligence
Reading List Faster With parallel, doParallel, and pbapply
Exploring the Gender Pay Gap with Publicly Available Data
There are plenty of studies that discuss the gender pay gap (here’s just one example). But as a data practitioner, I find those studies a bit frustrating. Not because I reject these pay gaps, but because the studies don’t allow people to dig into the data to better understand exactly how this trend manifests itself.
My introductory course on Bayesian statistics
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How to deploy a predictive service to Kubernetes with R and the AzureContainers package
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save as bos_rf_score.R
KDnuggets™ News 18：n47, Dec 12： Common mistakes when doing machine learning; Here are the most popular Python IDEs / Editors
This penultimate issue of 2018 has a review of common mistakes to avoid when doing Machine Learning; The most popular Python IDEs/editors, according to our poll; Machine Learning / AI Main Developments in 2018 and Key Trends for 2019; The Machine Learning Project Checklist; The difference between learning ML and learning DS; A very comprehensive list of ML resources, and more.
Single-Income Occupations
I initially approached this dataset with mostly high-income households in mind. The families I know who are single-income typically have the means. They can afford for one person to stay at home with kids or maintain the house. I expected a clear upwards trend that showed more income meant higher percentage of single-income.