Data Science Working Vocabulary
These terms describe the reasoning process. Tool names belong in implementation guides.
| Term | Working meaning |
|---|---|
| Question | The decision, comparison, estimate, or explanation the analysis is meant to support. |
| Observation | One recorded unit, event, or measurement in a dataset. |
| Feature | A measured or derived variable used as model input. |
| Target / label | The outcome a supervised model is asked to predict. |
| Population | The full set of cases the conclusion is intended to describe. |
| Sample | The observed subset used to learn about that population. |
| Schema | The names, types, constraints, and relationships that define the structure of stored data. |
| Provenance | Where data came from, how it was collected or transformed, and who is responsible for it. |
| Baseline | A simple reference method that a more complex analysis should improve upon. |
| Metric | A specified rule for measuring error, utility, fit, or another property of interest. |
| Training set | Data used to estimate model parameters. |
| Validation set | Data used during development to compare choices without fitting on the final test set. |
| Test set | Held-out data used for a final estimate of generalization under the chosen evaluation design. |
| Data leakage | Information reaches training or feature construction that would not legitimately be available when the model is used. |
| Confounder | A variable related to both an explanatory variable and an outcome that can distort a causal interpretation. |
| Calibration | Agreement between predicted probabilities and observed frequencies under a stated evaluation setting. |
| Reproducibility | Another run can recover the analysis from recorded data, code, environment, parameters, and steps. |
Words That Need Qualification
- Average: name the statistic—mean, median, weighted mean, or something else.
- Accuracy: name the metric and class distribution; accuracy alone can hide important errors.
- Representative: state which population and sampling mechanism justify the claim.
- Significant: distinguish statistical evidence from practical importance.
- Correlation: do not silently turn association into a causal conclusion.
Prefer a precise sentence over a larger glossary. If a term cannot change how the work is performed or interpreted, it probably does not need a canonical definition here.