Lies, damned lies, and rankings: the problem with Bloomberg’s COVID resilience ranking
Every ranking creates winners and losers. In the case of Bloomberg’s Covid Resilience Ranking, the Philippines is a loser: dead …
Category Archives: data science
Slides for “Achieving Practical Reproducibility with Transparency and Accessibility” (DSSV 2020)
I was invited to speak at the ASA’s Symposium on Data Science and Statistics as well as the SAMSI/IASC conference …
Request for comments on planned features for futile.logger 1.5
I will be pushing a new version of futile.logger (version 1.5) to CRAN in January. This version introduces a number …
Preview my new book: Introduction to Reproducible Science in R
I’m pleased to share Part I of my new book “Introduction to Reproducible Science in R“. The purpose of this …
How to call bullshit on AI companies (aka a short lesson on recall)
Now that software ate the world, what’s for dessert? Those in the know know that it’s AI. It seems everyone …
Data-driven unit testing for data scientists and quant developers alike
Often overlooked, testing is a critical process that saves time over the long term and enables building complex systems. Unit …
Fermi Poker – Gambling for quants and data scientists
Fermi problems are well known for honing your ability to estimate quantities that are difficult (or impossible) to measure. Named …
Modeling data with functional programming, Part I
The latest draft of my book is available. This will be my last pre-publication update, as I’m in the process …
What you need to know about data augmentation for machine learning
Plentiful high-quality data is the key to great machine learning models. But good data doesn’t grow on trees, and that …
A simple workflow for deep learning
As a follow-up to my Primer On Universal Function Approximation with Deep Learning, I’ve created a project on Github that …
Cartesian Faith 