Data Scientist
davila7/claude-code-templates
Expert data scientist for advanced analytics, machine learning, and statistical modeling.
Data science and analytics expertise for statistical analysis, machine learning pipelines, data governance, business intelligence, predictive modeling, and analytics strategy.
Install Data Science
$ npx skills add travisjneuman/.claude --skill data-science -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install travisjneuman/.claude data-science --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/travisjneuman/.claude.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/data-science .claude/skills/data-science && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "data-science" agent skill from https://github.com/travisjneuman/.claude/tree/master/skills/data-science into .claude/skills/data-science/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-science", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/travisjneuman/.claude/tree/master/skills/data-scienceType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add travisjneuman/.claude --skill data-science -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install travisjneuman/.claude data-science --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/travisjneuman/.claude.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/data-science .agents/skills/data-science && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "data-science" agent skill from https://github.com/travisjneuman/.claude/tree/master/skills/data-science into .agents/skills/data-science/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-science", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add travisjneuman/.claude --skill data-science -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install travisjneuman/.claude data-science --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/travisjneuman/.claude.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/data-science .cursor/skills/data-science && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "data-science" agent skill from https://github.com/travisjneuman/.claude/tree/master/skills/data-science into .cursor/skills/data-science/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-science", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/travisjneuman/.claude.git --path skills/data-science--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add travisjneuman/.claude --skill data-science -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install travisjneuman/.claude data-science --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/travisjneuman/.claude.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/data-science .gemini/skills/data-science && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "data-science" agent skill from https://github.com/travisjneuman/.claude/tree/master/skills/data-science into .gemini/skills/data-science/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-science", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install travisjneuman/.claude data-scienceInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add travisjneuman/.claude --skill data-science -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/travisjneuman/.claude.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/data-science .github/skills/data-science && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "data-science" agent skill from https://github.com/travisjneuman/.claude/tree/master/skills/data-science into .github/skills/data-science/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-science", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add travisjneuman/.claude --skill data-science -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install travisjneuman/.claude data-science --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/travisjneuman/.claude.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/data-science .opencode/skills/data-science && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "data-science" agent skill from https://github.com/travisjneuman/.claude/tree/master/skills/data-science into .opencode/skills/data-science/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "data-science", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
data-science- GitHub stars
- 101
- Used in
- 1 other repo
- Token cost
- ~2.3k tokens
- SKILL.md length
- 382 words
- Files
- 4 (incl. references)
- Skills in repo
- 21
- Repo updated
- First seen
- Licence
- MIT
At a glance
Data science and analytics expertise for statistical analysis, machine learning pipelines, data governance, business intelligence, predictive modeling, and analytics strategy.
- Building ML models
- SKILL.md covers Data Strategy, Statistical Analysis, Machine Learning and Data Governance, plus 6 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Creating dashboards
What it does
Data Science is an agent skill from travisjneuman/.claude. 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.
Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/data-science-expert.md`, `references/ml-pipelines.md` and `references/statistical-methods.md`).
It sits in Data & Analytics, covering Machine learning, Statistics and Data governance. The repository describes itself as: The Ultimate Claude Code Toolkit: 180 skills, 10 agents, 29 commands, 7 hooks, and 81 marketplace repos (11,000+ community skills). Drop-in ~/.claude config with a generated… The licence is MIT.
When your agent uses it
- Building ML models
- Creating dashboards
- Designing data architectures
Example prompts
- “/data-science”
What it can do on your machine
Read from SKILL.md and the folder at commit 139c07b. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Data Science loads about 2.3k tokens when it runs, and up to ~13k if it reads all its reference files. Until then it costs about 72 tokens; SKILL.md has 382 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from travisjneuman/.claude at commit 139c07b, republished under its MIT licence (© travisjneuman). 382 words, ~2,267 tokens.
Download SKILL.mdSave it as
.claude/skills/data-science/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- data-science
- description
- 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.
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 systemsStatistical 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 timeData 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 analyticsDashboard 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 detailsMetric 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
Show full SKILL.md (166 more words)Show less
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 controlsBias 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 groupsAnalytics 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
© travisjneuman, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 3 other files (references) in skills/data-science of travisjneuman/.claude.
- SKILL.md
- references/data-science-expert.md
- references/ml-pipelines.md
- references/statistical-methods.md
Open the folder on GitHubat commit 139c07b
Used in 1 other repository
We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in travisjneuman/.claude, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Data Science next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Data Science this skilltravisjneuman/.claude | 101 | 1 repos | ~2.3k | Automated safety check: Pass | MIT | |
| Data Scientistdavila7/claude-code-templates | 32k | 9 repos | ~2.6k | Automated safety check: Pass | MIT | |
| Scientific Toolkit SkillzLanqing/codex-claude-academic-skills | 4.6k | — | ~1.2k | Automated safety check: Pass | MIT | |
| Data Scientistmajiayu000/claude-skill-registry | 666 | 1 repos | ~3.5k | Automated safety check: Pass | MIT | |
| Power Analysisgaasher/Agent-Loop-Skills | 174 | — | ~2.2k | Automated safety check: Pass | MIT | |
| Automl SkillLeoYeAI/openclaw-master-skills | 2.2k | — | ~3.6k | Automated safety check: Pass | MIT |
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Categories
Questions about Data Science
What does Data Science do?
Data science and analytics expertise for statistical analysis, machine learning pipelines, data governance, business intelligence, predictive modeling, and analytics strategy. claude. Data science and analytics expertise for statistical analysis, machine learning pipelines, data governance, business intelligence, predictive modeling, and analytics strategy.
When should I use Data Science?
Data Science fits situations like: building ML models; creating dashboards; designing data architectures.
How do I install Data Science in Claude Code?
Run `npx skills add travisjneuman/.claude --skill data-science -a claude-code`. Or copy the skill folder (skills/data-science in travisjneuman/.claude) into .claude/skills/data-science in your project. Claude Code loads it when a task matches its description.
How do I install Data Science in Codex?
Run `npx skills add travisjneuman/.claude --skill data-science -a codex`. Or copy the skill folder (skills/data-science in travisjneuman/.claude) into .agents/skills/data-science in your project. Codex loads it when a task matches its description.
Can I use Data Science in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add travisjneuman/.claude --skill data-science -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/data-science, .gemini/skills/data-science, .github/skills/data-science and .opencode/skills/data-science in your project.
What does Data Science need to run?
SKILL.md names no scripts, command-line tools or credentials: Data Science is instructions for the agent only.
Does Data Science access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Data Science safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Data Science use?
Data Science is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Data Science use?
About 2.3k tokens (SKILL.md is roughly 9.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 11k tokens, read only when the agent opens those files.
What are the alternatives to Data Science?
Skills that share tags, products or a category with Data Science: Data Scientist (davila7/claude-code-templates, 32k stars), Scientific Toolkit Skill (zLanqing/codex-claude-academic-skills, 4.6k stars), Data Scientist (majiayu000/claude-skill-registry, 666 stars) and Power Analysis (gaasher/Agent-Loop-Skills, 174 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Data Science?
travisjneuman (a GitHub user) maintains it in travisjneuman/.claude, which has 101 GitHub stars. The repository holds 21 skills in this directory. The repository was last updated on October 6, 2026.
Source: travisjneuman/.claude on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.