Python Institute PCAD glossary of data science workflows, pandas, visualization, and analysis terms.
Use this glossary when Certified Associate Python for Data Science (PCAD) terms start to blur together. The goal is practical recognition, not encyclopedia coverage.
| Term | Exam meaning |
|---|---|
| DataFrame | Tabular data structure with rows and columns. |
| Feature | Input variable used by a model. |
| Label | Target value a supervised model learns to predict. |
| Overfitting | Model fits training data too closely and generalizes poorly. |
| Train/test split | Separating data for learning from data for evaluation. |
| Imputation | Replacing missing values with chosen estimates or defaults. |
| Pair | How to separate them |
|---|---|
| Python foundations vs Data handling | Ask which layer the scenario is testing, then match the answer to that layer only. |
| Control vs evidence | A control changes behavior; evidence proves behavior or supports investigation. |
| Managed service vs custom build | Managed services win for lower operational effort unless the requirement needs unsupported customization. |
| Prevention vs detection | Prevention blocks or reduces a bad event; detection finds or reports that it happened. |
Do not memorize terms in isolation. For each term, write one scenario where it is the best answer, one scenario where it is a distractor, and one signal that proves it worked.