I have up what I think is a really neat tutorial on how to plot multiple curves on a graph in Python, using seaborn and data_algebra. It is great way to show some data shaping theory convenience functions we have developed. Please check it out.

Estimated reading time: 23 seconds

Let’s please stop saying somebody isn’t a data scientist if they haven’t memorized the innards of one obscure machine learning algorithm, or blow the right smoke during an interoo (“Kangaroo interview”, thanks Jim Ruppert for this term!). Let us, instead, think of the data scientist as the bus driver. It […]

Estimated reading time: 1 minute

I am sharing a new free video where I work through a great common argument that bounds expected excess generalization error as a ratio of model complexity (in rows) over training set size (again in rows), independent of problem dimension. (link) For more of my notes on support vector machines […]

Estimated reading time: 34 seconds

I recently shared a bit of the history of The Science of Data Analysis. I thought I would follow that up with a quick chalk talk titled “What is Statistics?” (link)

Estimated reading time: 21 seconds

I am working on a promising new series of notes: common data science fallacies and pitfalls. (Probably still looking for a good name for the series!) I thought I would share a few thoughts on it, and hopefully not jinx it too badly.

Estimated reading time: 4 minutes

From the frontmatter: We recommend this book! Deep Learning for Coders with fastai and PyTorch uses advanced frameworks to move quickly through concrete, real-world artificial intelligence or automation tasks. This leaves time to cover usually neglected topics, like safely taking models to production and a much-needed chapter on data ethics. […]

Estimated reading time: 31 seconds