Data Scientist

The journey

1. What you learn

   1. Statistical reasoning strong enough to know when a result is real
   2. Machine learning applied to problems that actually warrant it
   3. Experiments designed so their conclusions hold up
   4. Findings communicated so a decision maker can act on them

2. What you will understand

   1. Correlation, causation, and the confounder nobody controlled for
   2. Why a model that scores well can still be useless in production
   3. Overfitting, leakage, and the validation that catches both
   4. Models as instruments for understanding rather than as products

3. What you will build

   1. An analysis with its assumptions, limitations, and confidence made explicit
   2. A model with a validation story that survives someone trying to break it
4. 1. The proof you build

      Evidence that you can turn a real question into a defensible answer, and say plainly what the answer does not cover.

      See how proof works