A curated collection of reusable analytic templates, checklists, code snippets and runbooks to speed workflows and reduce rework.
A curated, practical index of ready-to-use analytics templates, checklists, code patterns, and runbooks to speed routine work, standardize handoffs, and make analytic practices repeatable. Each item includes when to use it, expected outputs, suggested file formats, adaptation tips, and quick examples to help teams copy-and-tailor safely.
A practical, ready-to-copy set of analytic templates: prioritization matrix (with example scoring), quick-problem diagnostic worksheet, stakeholder mapping + communication plan, and a delivery tracker with an acceptance checklist. Each template includes clear instructions, example values, and guidance for adapting to local governance and handoffs.
A compact, practical collection of reusable analytics artifacts — intake form, prioritization matrix, impact/ROI calculator, stakeholder map, retrospective checklist, and a 'what good looks like' one‑pager — with examples, usage guidance, and adaptation notes so teams can apply them reliably.
A practical, copyable runbook template that helps data product teams define ownership, SLAs/SLOs, health metrics, monitoring queries, incident playbooks, deployment checks, communication templates, and a repeatable post-incident review process. Includes concrete examples and tailoring notes so teams can copy, test, and adapt the runbook to their environment.
Practical, role-focused guidance that helps non-expert analysts run safe, reproducible, and decision-focused analyses. Includes quick-check guardrails, reusable analysis templates, experiment limits, a lightweight approval flow for publishing dashboards, documentation standards, and suggested training paths.
A curated, practical collection of ready-to-use SQL, Python (pandas) and R (dplyr/data.table) patterns for common data cleaning, transformation, sampling, aggregation, cohorting, and reporting tasks. Each snippet includes a short description, required inputs, expected outputs, and a minimal example to adapt to your schema and dialect.
A practical, opinionated starter library of vetted SQL, Python, and R snippets for common analytics tasks (ETL patterns, dedupe, windowing, metric templates, feature engineering, model evaluation) with usage notes, simple tests, and packaging/versioning guidance so teams can adapt, review, and safely reuse code.
A curated, practical library of vetted SQL, Python, and R snippets for common data tasks (cleaning, validation, transformation, aggregation, sampling, and reporting). Each snippet includes usage notes, inputs/outputs, performance cautions, and adaptation guidance so analysts produce reliable, reproducible results faster.