Data Visualisation

MLOps is the engineering practice of keeping a trained model useful in production: recording what produced it, serving it, watching for the day its answers stop matching the world, and retraining or reverting before anyone downstream is harmed by the gap.

Responsible AI is the practice of building and running systems whose decisions you could defend to the person on the receiving end of them, covering fairness, transparency, privacy and accountability.

Machine learning is the branch of computing in which a program works out its rule from examples instead of being handed that rule by a programmer, and is judged on how well the rule holds on cases it was never shown.

Data modelling is the design of how data is structured for a given use: which entities are recorded, which attributes describe them, how the tables relate through keys, and, for analytical use, which table holds the measurements and which hold the context, decided before the data is loaded so that the questions the model must answer run correctly and fast.

Hypothesis testing is the statistical procedure for deciding whether an observed difference, between two groups, two periods or a sample and a claimed value, is larger than chance variation would produce if there were no real difference, by computing how probable a result at least that extreme would be under that assumption.

Data analysis is the practice of interrogating data to answer a specific question, and of establishing how much weight the answer can bear.