data-science

The companies famous for experimentation did not adopt it out of statistical enthusiasm: they adopted it because their own data showed most confident product ideas fail to improve the metrics they target. This lesson covers why observational product data misleads, what randomisation actually buys, the choice of randomisation unit, and the humbling base rates reported by the teams who measured.

Why prediction systems fail when deployed, how invariance across environments becomes a training signal, and where causal reasoning enters bandits, reinforcement learning, and language models.

The central question of causal inference has a precise answer. The backdoor criterion, the front-door criterion, instrumental variables, and what to do when no identification strategy exists.

The formal machinery that makes causal questions answerable: structural causal models, graphs as assumptions you can inspect, the three ways variables become associated, and why prediction and intervention are different problems.

Dive into the world of vector databases, specialized systems designed to store and query high-dimensional vector embeddings efficiently. Learn how these databases power semantic search, recommendation systems, and large language model applications by finding semantically similar data points at scale.