{"id":30209148,"url":"https://github.com/g-schumacher44/analyst_resource_hub","last_synced_at":"2026-06-20T12:32:47.028Z","repository":{"id":309301669,"uuid":"1019695214","full_name":"G-Schumacher44/analyst_resource_hub","owner":"G-Schumacher44","description":"A collection of guidebooks, quickref, and resources for data analysis","archived":false,"fork":false,"pushed_at":"2025-08-11T06:21:35.000Z","size":4536,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-08-11T06:23:11.055Z","etag":null,"topics":["analytics","bigquery","data","lookerstudio","machine-learning","model","python","sql","yaml-configuration"],"latest_commit_sha":null,"homepage":"https://g-schumacher44.github.io/analyst_resource_hub/","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/G-Schumacher44.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-07-14T18:14:37.000Z","updated_at":"2025-08-11T06:21:38.000Z","dependencies_parsed_at":"2025-08-11T06:33:18.746Z","dependency_job_id":null,"html_url":"https://github.com/G-Schumacher44/analyst_resource_hub","commit_stats":null,"previous_names":["g-schumacher44/analyst_resource_hub"],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/G-Schumacher44/analyst_resource_hub","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/G-Schumacher44%2Fanalyst_resource_hub","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/G-Schumacher44%2Fanalyst_resource_hub/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/G-Schumacher44%2Fanalyst_resource_hub/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/G-Schumacher44%2Fanalyst_resource_hub/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/G-Schumacher44","download_url":"https://codeload.github.com/G-Schumacher44/analyst_resource_hub/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/G-Schumacher44%2Fanalyst_resource_hub/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":270295336,"owners_count":24560344,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-13T02:00:09.904Z","response_time":66,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["analytics","bigquery","data","lookerstudio","machine-learning","model","python","sql","yaml-configuration"],"created_at":"2025-08-13T19:01:21.412Z","updated_at":"2026-06-20T12:32:46.981Z","avatar_url":"https://github.com/G-Schumacher44.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cp align=\"center\"\u003e\n  \u003cimg src=\"dark_logo_banner.png\" width=\"1000\"/\u003e\n  \u003cbr\u003e\n  \u003cem\u003e Knowledge Base \u0026 Resource Center\u003c/em\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003cimg alt=\"MIT License\" src=\"https://img.shields.io/badge/license-MIT-blue\"\u003e\n  \u003cimg alt=\"Status\" src=\"https://img.shields.io/badge/status-alpha-lightgrey\"\u003e\n  \u003cimg alt=\"Version\" src=\"https://img.shields.io/badge/version-v0.1.0-blueviolet\"\u003e\n\u003c/p\u003e\n\n# 🗂️ Analyst Resource Hub: Reference Vault for Data Science \u0026 ML\n\nThis is my personal knowledge vault — a curated and structured collection of checklists, decision frameworks, modeling guides, and reusable scripts developed while studying and building skills in data science, machine learning, and analytics workflows.\n\nAlso published as a [MkDocs site](https://g-schumacher44.github.io/analyst_resource_hub/) for easy navigation and browsing.\n \n## 🧩 TLDR;\n- Built originally in Obsidian, published here as both a **quick-access reference** and a **public portfolio artifact**\n- Focuses on real-world execution: cleaning, modeling, diagnostics, and pipeline structuring\n- Includes:\n- Python, SQL, and workflow sections\n  - ✅ Checklists \u0026 QA routines\n  - 📋 Decision cards for strategy selection\n  - 📘 Guidebooks by topic area\n  - 🧭 QuickRefs \u0026 visual companions\n\n## 🧭 Orientation \u0026 Getting Started\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cstrong\u003e🧠 Notes from the Vault Architect\u003c/strong\u003e\u003c/summary\u003e\n\nThis vault was designed to be modular, navigable, and deeply practical — a living resource that reflects how I think, work, and solve problems. It serves as a:\n- Toolkit for day-to-day analysis\n- Teaching aid for others and for myself\n- Sandbox for workflows and automation ideas\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cstrong\u003e🫆 Version Release Notes\u003c/strong\u003e\u003c/summary\u003e\n\n**`v0.1.0` – Initial Public Release**\n\n- Obsidian vault ported to GitHub\n- Folder structure stabilized\n- Markdown files cleaned and organized for public browsing\n\n**`v0.2.0` – MkDocs site buildout**\n\n- Adopted MkDocs + Material theme\n- Added `docs/` site with section hubs: Python, SQL, Workflow \u0026 Projects\n- Custom landing page with hero + action buttons (`docs/index.md`)\n- Basic branding: logos, title, tagline, and skim-friendly emoji headers\n- Navigation + metadata wired up (`mkdocs.yml`)\n- Prepared for GitHub Pages deployment (local `mkdocs serve` ready)\n\n**`v0.2.1` – Content structure refresh** *(current)*\n\n- Tightened page hierarchy and filenames for clean URLs\n- Added QuickRef, Guidebooks, and Scripts lanes under Python\n- Consolidated BigQuery/Looker under SQL with patterns \u0026 dashboard guides\n- Created Workflow hub for scaffolds, checklists, and delivery templates\n\n\n**Upcoming Additions**\n\n- Add reusable templates and starter kits\n- Adding Screenshots and Visuals to Guidebooks and Visual Companions\n- Expand Python and SQL script collections\n- Incorporate references and workflows from related projects:\n  - [`analyst_toolkit`](https://github.com/G-Schumacher44/analyst_toolkit)\n  - [`model_evaluation_suite`](https://github.com/G-Schumacher44/model_evaluation_suite)\n\n \n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e📌 Emoji Codex\u003c/summary\u003e\n\nTo make the vault easier to skim and navigate, each document uses an emoji prefix to signal its purpose or category.\n\n- 📊 Visual Companions \u0026 Evaluation Guides\n- ✅ Execution Checklists\n- 📋 Decision Strategy Cards\n- 📘 Deep-Dive Guidebooks\n- 🧭 Quick Reference Sheets\n\nFor a full legend, see the [📚 Vault Emoji Codex](emoji_codex.md).\n\n\u003c/details\u003e\n\n___\n\n# 🗺️ Resource Map\n\n```txt\n🐍 Python Modules\n\nPython/01 - QuickRef/\n  ├── 01 - Checklists/               ✅ Execution workflows\n  ├── 02 - Decision Cards/          📋 Strategy selectors\n  └── 02 - Reference Guides/         🧭 Quick references\n\nPython/02 - Data Wrangling \u0026 EDA/\n  ├── Data Wrangling/               📘 Feature transformation \u0026 validation\n  └── EDA/                          📊 Exploratory workflows\n\nPython/03 - Cleaning/              🧼 Foundational and advanced cleaning guides\n\nPython/04 - Machine Learning Models/\n  ├── 01 - Regression/              📘 Linear \u0026 Logistic modeling resources\n  ├── 02 - Supervised/              📊 Classifier guidebooks and visuals\n  └── 03 - Unsupervised/            📋 Clustering diagnostics and workflows\n\nPython/05 - Scripts/\n  ├── 01 - Python/                  🧪 Cleaning, validation, modeling scripts\n  └── 02 - eda_toolkit/             🧰 Modular tools for EDA diagnostics\n\n🚛 SQL Modules\n\nSQL/01 - Guidebooks/               📘 SQL basics to advanced playbooks\n\nSQL/02 - BigQuery and Looker/\n  ├── 01 - BigQuery/                🧱 Patterns, optimization, and pipelines\n  └── 02 - Looker Studio/           📊 Dashboard UX and parameter guides\n\n🖇️ Workflow + Projects\n\nWorkFlow+Projects/\n  ├── ✅ Notebook readiness checklist\n  ├── 📘 Project pipeline templates\n  └── 🥇 Gold standard scaffolds\n```\n___\n\n## 🤝 On Generative AI Use\n\nGenerative AI tools (Gemini 2.5-PRO, ChatGPT 4o - 4.1) were used throughout this project as part of an integrated workflow — supporting code generation, documentation refinement, and idea testing. 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