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.
- Installs
- 183
- Last 24h
- —
- Trending rank
- —
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/travisjneuman/.claude --skill data-science- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads data-science automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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 = symmetric)
- Kurtosis: Tail heaviness (3 = normal)
For detailed inferential statistics and hypothesis testing, see Statistical Methods Reference.
Machine Learning
Algorithm Selection
| Task | Algorithms | When to Use |
|---|---|---|
| Classification | Logistic Regression, Random Forest, XGBoost, Neural Networks | Categorical outcomes |
| Regression | Linear Regression, Ridge/Lasso, Random Forest, XGBoost | Continuous outcomes |
| Clustering | K-Means, Hierarchical, DBSCAN | Group discovery |
| Dimensionality Reduction | PCA, t-SNE, UMAP | Feature reduction, visualization |
| Anomaly Detection | Isolation Forest, One-Class SVM, Autoencoders | Outlier detection |
| Time Series | ARIMA, Prophet, LSTM | Sequential data |
| Recommendation | Collaborative Filtering, Content-Based, Matrix Factorization | Personalization |
| NLP | Transformers, BERT, GPT | Text understanding/generation |
For detailed ML pipelines, feature engineering, and model monitoring, see ML Pipelines Reference.
Data Governance
Data Governance Framework
GOVERNANCE PILLARS:
POLICIES:
- Data ownership
- Data classification
- Data retention
- Data access
- Data quality standards
ROLES:
- Data Owner: Accountable for data domain
- Data Steward: Day-to-day quality management
- Data Custodian: Technical implementation
- Data Consumer: End user
PROCESSES:
- Data cataloging
- Metadata management
- Data lineage
- Issue resolution
- Change management
METRICS:
- Data quality scores
- Policy compliance
- Data access requests
- Issue resolution time
Data Quality Dimensions
| Dimension | Definition | Measurement |
|---|---|---|
| Accuracy | Correct representation of reality | % records matching source |
| Completeness | All required data present | % non-null values |
| Consistency | Same across systems | % matching across sources |
| Timeliness | Available when needed | Latency, freshness |
| Validity | Conforms to format/rules | % passing validation |
| Uniqueness | No unwanted duplicates | Duplicate rate |
Business Intelligence
BI Architecture
ARCHITECTURE LAYERS:
DATA SOURCES:
- Operational systems
- External data
- IoT/streaming
DATA INTEGRATION:
- ETL/ELT pipelines
- Data lakes
- Data warehouses
SEMANTIC LAYER:
- Business definitions
- Calculated metrics
- Hierarchies
- Relationships
PRESENTATION:
- Dashboards
- Reports
- Ad-hoc analysis
- Embedded analytics
Dashboard Design Principles
DESIGN PRINCIPLES:
PURPOSE:
- One clear objective per dashboard
- Know your audience
- Enable decisions
LAYOUT:
- Most important top-left
- Related items grouped
- Progressive disclosure
- Whitespace for clarity
VISUALS:
- Right chart for data type
- Consistent formatting
- Minimal decoration
- Color with purpose
INTERACTIVITY:
- Filters for exploration
- Drill-down capability
- Cross-filtering
- Tooltip details
Metric Design
METRIC DEFINITION TEMPLATE:
NAME: [Metric name]
DEFINITION: [Clear business definition]
FORMULA: [Precise calculation]
OWNER: [Responsible person]
DATA SOURCE: [Where it comes from]
GRAIN: [Level of detail]
FREQUENCY: [Update cadence]
DIMENSIONS: [Slicing attributes]
TARGETS: [Goals/benchmarks]
RELATED: [Related metrics]
Predictive Modeling
Use Case Framework
| Use Case | Business Application | Approach |
|---|---|---|
| Churn Prediction | Retention programs | Classification |
| Demand Forecasting | Inventory planning | Time series |
| Lead Scoring | Sales prioritization | Classification |
| Price Optimization | Revenue management | Regression/RL |
| Fraud Detection | Risk mitigation | Anomaly detection |
| Recommendation | Personalization | Collaborative filtering |
| Customer Segmentation | Marketing targeting | Clustering |
| Lifetime Value | Customer investment | Regression |
Data Ethics & Privacy
Ethical AI Framework
PRINCIPLES:
FAIRNESS:
- No discriminatory outcomes
- Bias testing across groups
- Regular auditing
ACCOUNTABILITY:
- Clear ownership
- Decision audit trails
- Escalation process
TRANSPARENCY:
- Explainable decisions
- Clear documentation
- User communication
PRIVACY:
- Data minimization
- Consent management
- Security controls
Bias Detection
BIAS TYPES:
HISTORICAL: Reflects past discrimination
REPRESENTATION: Training data not representative
MEASUREMENT: Proxy variables correlate with protected attributes
AGGREGATION: Single model for diverse populations
EVALUATION: Inappropriate benchmarks
FAIRNESS METRICS:
- Demographic Parity: Equal positive rates
- Equalized Odds: Equal TPR and FPR
- Individual Fairness: Similar inputs, similar outputs
- Calibration: Equal accuracy across groups
Analytics Team Structure
Team Roles
| Role | Focus | Skills |
|---|---|---|
| Data Engineer | Pipelines, infrastructure | SQL, Python, Spark, Cloud |
| Data Analyst | Reporting, ad-hoc analysis | SQL, BI tools, Statistics |
| Data Scientist | Modeling, ML | Python/R, ML, Statistics |
| ML Engineer | Model deployment | MLOps, Software Engineering |
| Analytics Engineer | Data modeling | dbt, SQL, Data Modeling |
Operating Models
| Model | Description | Best For |
|---|---|---|
| Centralized | Single analytics team | Consistency, efficiency |
| Decentralized | Embedded in business units | Business alignment |
| Hub & Spoke | Central CoE + embedded | Balance of both |
| Federated | Shared platform, domain teams | Scale with autonomy |
References
- ML Pipelines Reference - Detailed ML pipeline, feature engineering, model development
- Statistical Methods Reference - Inferential statistics, hypothesis testing, evaluation metrics
See Also
- Product Management
- Business Strategy
- Finance
FAQ
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 data-science?
Run npx skills add https://github.com/travisjneuman/.claude --skill data-science in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/data-science/ (personal) or .claude/skills/data-science/ (project).
Which AI agents can use data-science?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is data-science safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains data-science?
It is published by travisjneuman in travisjneuman/.claude. It has 183 recorded installs.
More from travisjneuman/.claude
document-skills
travisjneuman/.claude
Professional document creation, editing, and analysis for Office formats (docx, pdf, pptx, xlsx). Use when working with Word documents, PDFs, PowerPoint presentations, or Excel spreadsheets.
brand-identity
travisjneuman/.claude
Brand strategy and identity design for businesses and products. Use when creating brand guidelines, developing visual identity systems, or defining brand positioning.
travel-planner
travisjneuman/.claude
Travel destination research and daily itinerary creation with logistics planning, budget tracking, and experience optimization. Use when planning trips, creating travel itineraries, comparing destinations, or organizing travel logistics.
business-strategy
travisjneuman/.claude
Business strategy expertise for strategic planning, competitive analysis, market entry, M&A strategy, portfolio management, and strategic decision-making. Use when analyzing competitive positioning, planning growth strategies, or making strategic decisions.
graphic-design
travisjneuman/.claude
Professional graphic design principles for digital and print media. Use when creating visual designs, choosing color palettes, typography, layouts, or providing design feedback.
real-estate-analyzer
travisjneuman/.claude
Open the skill to read its SKILL.md instructions.
Popular education & science skills
teach
mattpocock/skills
Teach the user a new skill or concept, within this workspace.
scaffold-exercises
mattpocock/skills
Create exercise directory structures with sections, problems, solutions, and explainers that pass linting. Use when user wants to scaffold exercises, create exercise stubs, or set up a new course section.
paper-context-resolver
lllllllama/rigorpilot-skills
Rigor Paper Context helper for README-first deep learning repo reproduction. Use only when the README and repository files leave a narrow reproduction-critical gap and the task is to resolve a specific paper detail such as dataset split, preprocessing, evaluation protocol, checkpoint mapping, or runtime assumption from primary paper sources while recording conflicts. Do not use for general paper summary, repo scanning, environment setup, command execution, title-only paper lookup, or replacing README guidance by default.
caveman-learn
juliusbrussee/caveman
Act on a Caveman learn report - review the ranked token sinks, apply cost-lowering fixes with per-edit consent, and report what those fixes returned. Use when asked to lower an agent's token cost, what caveman has saved, to trim a heavy CLAUDE.md, or to offload re-pasted context into cavemem.
gsap-plugins
greensock/gsap-skills
Official GSAP skill for GSAP plugins — registration, ScrollToPlugin, ScrollSmoother, Flip, Draggable, Inertia, Observer, SplitText, ScrambleText, SVG and physics plugins, CustomEase, EasePack, CustomWiggle, CustomBounce, GSDevTools. Use when the user asks about a GSAP plugin, scroll-to, flip animations, draggable, SVG drawing, or plugin registration.
teach-impeccable
pbakaus/impeccable
DEPRECATED: Use /impeccable teach instead. This command has been folded into the impeccable skill.