What it does

HackerMath is a curated workshop in Jupyter Notebook form, targeting programmers who need the math behind data science but don’t want to wade back into theorem-proof territory. It covers three modules—Hypothesis Testing, Supervised Learning, and Unsupervised Learning—with roughly equal time split between explaining concepts and writing code.

The interesting bit

The “Hacker’s way” here means bootstrapping and direct simulation instead of memorizing distribution formulas, and solving Ax=λx to understand PCA rather than treating eigenvectors as magic. The authors explicitly borrowed 3Blue1Brown’s visual intuition for linear algebra and calculus, then grounded it in numpy and scikit-learn.

Key highlights

• Teaches A/B testing via shuffling and bootstrapping, not just p-value recipes
• Builds linear regression, logistic regression, and gradient descent from matrix operations up
• Covers PCA and clustering through eigenvectors and vector-space distance
• Includes Binder link for running notebooks without local setup
• Targets Python programmers comfortable with loops and functions; assumes you’ve forgotten most college math

Caveats

• Last updated for Python 3.5/Anaconda, so some library versions may need attention
• Not a reference or cookbook; scope is deliberately narrow (depth over breadth)

Verdict

Ideal if you’re a developer or Excel analyst who learns by doing and wants to stop treating ML math as a black box. Skip it if you need comprehensive coverage or a modern, maintained course—this is a focused workshop snapshot, not a living textbook.