dmoreq Data Science Skill
Analyze a dataset to answer a defined question with appropriate methods and uncertainty.
When to Use
- EDA, hypothesis testing, A/B testing
- Predictive / ML model build or evaluation
- Time series forecasting, causal inference, segmentation
- Experiment design or impact measurement
When Not to Use
- Plotly chart polish (
data-visualization) - Dash UI (
plotly-dash) - Stakeholder QBR copy (
data-storytelling) - External SOTA survey (
tech-research) - Quality KPI loop on labeled cases (
algorithm-optimization) - Pure backend feature work
Related Skills
- Use data-visualization when results need charts.
- Use data-storytelling when a non-technical decision narrative is required (after validation).
- Use algorithm-optimization when findings should drive a KPI loop on real cases.
Method Card
- Objective: business question, success metric, constraints, available data.
- Data checks: missingness, outliers, leakage, unit of analysis, train/serve skew.
- Method class: inference/experiment vs predictive vs causal vs forecast — pick one primary.
- Validation: holdout / CV / residual checks / robustness; match the method class.
- Uncertainty: inference → effect size + interval (not p-values alone). Predictive → holdout metric + error bars or calibration. Do not force CIs onto every ML score.
- Recommendations: feasible next actions; assumptions and limitations explicit.
Prefer simpler interpretable methods when they perform adequately. Separate exploratory findings from confirmatory results. Flag data quality issues before modeling.
Output Expectations
- Short summary of the key finding or recommendation
- Relevant metrics/tables (charts via
data-visualization) - Assumptions, limitations, next verification step
Final Checklist
- Objective and success metric defined
- Data checks done before modeling
- Method class matches the question
- Validation matches the method class
- Uncertainty reported appropriately
- Recommendations are actionable