Data Science Expert — stats, ML pipelines, governance, BI, analytics strategy
Broad data-science expert skill: analytics value chain, stats, ML algorithm selection, governance, BI dashboards, predictive modeling, ethics — with references to bundled ML-pipeline and statistical-methods playbooks
- What
- Broad data-science expert skill: analytics value chain, stats, ML algorithm selection, governance, BI dashboards, predictive modeling, ethics — with references to bundled ML-pipeline and statistical-methods playbooks
- Cost
- Free
- Needs
- a data-science or analytics initiative to frame — the skill is an expert playbook the agent follows, not software; deep content lives in the bundled references/
- Install
- Copy the installer prompt below into your Muse — your agent does the rest.
Curated by Skill Harbor — @travisjneuman's data-science: a comprehensive data-science expert skill covering data strategy (5-level data maturity model, analytics value chain from descriptive to autonomous), statistical analysis (descriptive statistics with robustness notes), machine learning (algorithm-selection table by task: classification, regression, clustering, anomaly detection, time series, recommendations, NLP), data governance (policies, roles, data-quality dimensions with measurement definitions), business intelligence (layered BI architecture, dashboard design principles, metric-definition template), predictive modeling use-case framework (churn, demand forecasting, fraud, LTV…), data ethics and privacy (fairness, bias types, fairness metrics), and analytics team structure (roles, centralized vs hub-and-spoke vs federated operating models). Honest caveats: broad rather than deep on any single topic — it's a reference playbook of frameworks and checklists, not tutorials or code; deep detail lives in the bundled `references/ml-pipelines.md` and `references/statistical-methods.md` inside the repo, and the file links sibling skills (`../product-management`, `../business-strategy`, `../finance`) that are not included. The skill's frontmatter states no license; the catalog manifest records MIT — manifest takes precedence. MIT licensed per the manifest. Skill Harbor never reviews the code, review it yourself before use. Discovered via skills.sh.
Version:
Install
Prerequisites: a data-science or analytics initiative to frame — the skill is an expert playbook the agent follows, not software; deep content lives in the bundled references/ Install "Data Science Expert — stats, ML pipelines, governance, BI, analytics strategy" for me. It gives my agent @travisjneuman's comprehensive data-science playbook: data maturity model, analytics value chain (descriptive → diagnostic → predictive → prescriptive → autonomous), statistical analysis, ML algorithm selection by task, data governance framework and quality dimensions, BI architecture with dashboard design principles and metric template, predictive-modeling use-case framework, ethical AI and bias-detection guidance, and analytics team roles/operating models — with pointers to references/ml-pipelines.md and references/statistical-methods.md for depth. License note: the skill's frontmatter states no license; the catalog manifest records MIT — manifest takes precedence. Repository: https://github.com/travisjneuman/.claude/blob/main/skills/data-science/SKILL.md 1. Fetch the SKILL.md file (and the bundled references/) from the repository path into a temporary folder and summarize what it does in one or two sentences. 2. Safety check: review the SKILL.md and scripts for anything suspicious (unexpected network calls, shell commands, credential harvesting). This repo should contain zero secrets in code. Verify that holds here; STOP on any red flag and tell me. 3. Install it as a skill: copy SKILL.md and its helper files into the agent's skills directory, in a folder named "data-science". 4. Verify with no network calls: frontmatter valid, files in place. 5. Report what was installed, where, and what I still need to do myself (nothing — it's an expert playbook the agent applies when framing analytics, ML or BI work). GitHub is optional: if I have a GitHub account or the gh CLI, you may use it; otherwise public access is fine. Never require it unless it's in the prerequisites above. Rules: don't touch anything outside the temp folder and the install target. If anything looks off, stop and ask me.
Questions
How do I install a build?
Every product page includes a copy-paste install prompt. Paste it into your Muse and it sets the build up for you — no manual configuration.
Where does my money go?
Straight to the seller. Skill Harbor never processes payments: checkout happens on the seller’s own page, usually Stripe.
What does the ✓ next to a creator’s name mean?
It means we confirmed the identity of the person behind the listing. It says nothing about the code itself — always check a build before installing it.