data-science
Data science and analytics expertise for statistical analysis, machine learning pipelines, data governance, business intelligence, predictive modeling, and analytics strategy. Use when building ML models, analyzing data, creating dashboards, or designing data architectures.
npx skills add majiayu000/claude-skill-registry --skill data-science --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Data Science Expert Comprehensive data science frameworks for analytics, machine learning, and data-driven decision making. ## Data Strategy ### Data Maturity Model | Level | Name | Characteristics | | ----- | ------------------- | ----------------------------------------- | | 1 | **Ad Hoc** | Manual, inconsistent, siloed | | 2 | **Opportunistic** | Some automation, point solutions | | 3 | **Systematic** | Defined processes, governance emerging | | 4 | **Differentiating** | Data-driven decisions, advanced analytics | | 5 | **Transformative** | AI-first, competitive advantage | ### Analytics Value Chain ``` DATA → INFORMATION → INSIGHT → ACTION → VALUE PROGRESSION: Descriptive: What happened? Diagnostic: Why did it happen? Predictive: What will happen? Prescriptive: What should we do? Autonomous: Self-optimizing systems ``` ## Statistical Analysis ### Descriptive Statistics ``` CENTRAL TENDENCY: - Mean: Sum / Count (sensitive to outliers) - Median: Middle value (robust to outliers) - Mode: Most frequent value DISPERSION: - Range: Max - Min - Variance: Average squared deviation - Standard Deviation: √Variance - IQR: Q3 - Q1 (robust) DISTRIBUTION SHAPE: - Skewness: Asymmetry (0 = sy
- Data Strategy
- Data Maturity Model
- Analytics Value Chain
- Statistical Analysis
- Descriptive Statistics
- Machine Learning
- Algorithm Selection
- Data Governance
- Data Governance Framework
- Data Quality Dimensions
- Business Intelligence
- BI Architecture
- Dashboard Design Principles
- Metric Design
What does the data-science skill do?
Data science and analytics expertise for statistical analysis, machine learning pipelines, data governance, business intelligence, predictive modeling, and analytics strategy. Use when building ML models, analyzing data, creating dashboards, or designing data architectures.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill data-science --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.