Data science, minus the course-seller fluff.
The version of the field that actually gets used: framing the problem, getting the data trustworthy, picking the simplest model that works, then explaining the result to people who don’t care about your architecture.
Where each track begins
The full reading order groups all 33 Foundations articles by what each one assumes you already know.
- The job is mostly cleaning data, and no one warns you Courses spend 5% of the time on the 80% of the work. What data cleaning actually involves, why it is the real skill, and how to get good at it deliberately.
- The Monty Hall problem, and why switching wins Three doors, one car, a host who knows where it is. Switching wins two thirds of the time — here is the intuition, the enumeration, and the lesson underneath it.
- Correlation is not causation, but The three things that can produce any correlation, why the famous slogan stops thought instead of starting it, and what actually establishes a causal claim.
- Overfitting and the bias-variance tradeoff What overfitting really is, what bias and variance each mean in concrete terms, how the tradeoff shows up in model choice, and where the classic curve stops holding.
- What a neural network actually is Layers, weights, biases and activations built up from scratch on the handwritten-digit problem, and why "learning" means nothing more exotic than adjusting a pile of numbers.
- Transformers explained from scratch Tokens, embeddings and a stack of identical blocks — how a transformer turns next-word prediction into a training objective, and why it trains in parallel where RNNs could not.
Latest articles
- Your model started dying the day you deployed it Data drift, concept drift, and delayed labels: a minimal monitoring setup that catches decay before the business does, without buying a platform.
- When you cannot randomise users Interference, marketplaces and features that leak between groups. Cluster and geo randomisation, switchback tests, and the observational fallbacks with their assumptions stated.
- Cohort retention in SQL, without the spreadsheet How to build a retention table that survives contact with real data: cohort definition, a period spine, and the bug that makes retention look like it is improving.