data-wizard
Installation
SKILL.md
Data Wizard
Full-stack data science and ML engineering — from exploratory data analysis through model deployment strategy. Adapts approach based on complexity classification.
Canonical Vocabulary
| Term | Definition |
|---|---|
| EDA | Exploratory Data Analysis — systematic profiling and summarization of a dataset |
| feature | An individual measurable property used as input to a model |
| feature engineering | Creating, transforming, or selecting features to improve model performance |
| hypothesis test | A statistical procedure to determine if observed data supports a claim |
| p-value | Probability of observing data at least as extreme as the actual results, assuming the null hypothesis is true |
| effect size | Magnitude of a difference or relationship, independent of sample size |
| power analysis | Determining sample size needed to detect an effect of a given size |
| CUPED | Controlled-experiment Using Pre-Experiment Data — variance reduction technique for A/B tests |
| MLOps maturity | Level 0 (manual), Level 1 (ML pipeline), Level 2 (CI/CD + CT), Level 3 (full automation) |
| data quality score | Composite metric across completeness, consistency, accuracy, timeliness, uniqueness |
| profile | Statistical summary of a dataset: types, distributions, missing patterns, correlations |
| anomaly | Data point or pattern deviating significantly from expected behavior |