Learning Tracks

Learn the concept, then practise it against a real company. Five of these are full courses whose lessons run in order on one product's data, where every number is computed and the true answer is planted, so a method can be scored rather than asserted. Start with Probability & Statistics if the foundations are shaky; the other four assume them. The Concept Crash Course is a library rather than a course — one article per concept, read whichever one a question sends you to. The case bank is where you go once you know what you are looking at.

Product Analytics

Define a metric properly, then use it to work out what moved and why, on one product's real data.

20 lessons

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Experimentation

Design an A/B test, size it, and read it without fooling yourself. Twenty lessons on one platform's experiments, where the true answer is known.

20 lessons

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Advanced Experimentation

The experiment questions that separate a senior candidate from a competent one. Bayesian readouts, bandits, designs that buy precision, and measuring what a two-week test cannot see.

10 lessons

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Causal Inference

Measure an effect when you cannot randomise. Twenty-two lessons on one company's data, where the true answer is planted and every method is scored against it.

22 lessons

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Probability & Statistics

The machinery every other track leans on. Twenty-five lessons on one product's data, where the true parameter is known and every procedure is scored against it.

25 lessons

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Concept Crash Course

The standalone explainers. One concept per article, reachable from any practice answer that leans on it, in no particular order.

6 articles

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Product Sense & Metrics

The case bank. One deep guide per company product: its users, its money, its metrics.

41 companies

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