Knowledge Base & Resource Center

🗂️ Analyst Resource Hub: Reference Vault for Data Science & ML

This 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.

Also published as a MkDocs site for easy navigation and browsing.

🧩 TLDR;

• Built originally in Obsidian, published here as both a quick-access reference and a public portfolio artifact
• Focuses on real-world execution: cleaning, modeling, diagnostics, and pipeline structuring
• Includes:
• Python, SQL, and workflow sections
  • ✅ Checklists & QA routines
  • 📋 Decision cards for strategy selection
  • 📘 Guidebooks by topic area
  • 🧭 QuickRefs & visual companions

🧭 Orientation & Getting Started

🧠 Notes from the Vault Architect

This 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:

• Toolkit for day-to-day analysis
• Teaching aid for others and for myself
• Sandbox for workflows and automation ideas

🫆 Version Release Notes

v0.1.0 – Initial Public Release

• Obsidian vault ported to GitHub
• Folder structure stabilized
• Markdown files cleaned and organized for public browsing

v0.2.0 – MkDocs site buildout

• Adopted MkDocs + Material theme
• Added docs/ site with section hubs: Python, SQL, Workflow & Projects
• Custom landing page with hero + action buttons (docs/index.md)
• Basic branding: logos, title, tagline, and skim-friendly emoji headers
• Navigation + metadata wired up (mkdocs.yml)
• Prepared for GitHub Pages deployment (local mkdocs serve ready)

v0.2.1 – Content structure refresh (current)

• Tightened page hierarchy and filenames for clean URLs
• Added QuickRef, Guidebooks, and Scripts lanes under Python
• Consolidated BigQuery/Looker under SQL with patterns & dashboard guides
• Created Workflow hub for scaffolds, checklists, and delivery templates

Upcoming Additions

• Add reusable templates and starter kits
• Adding Screenshots and Visuals to Guidebooks and Visual Companions
• Expand Python and SQL script collections
• Incorporate references and workflows from related projects:
  • analyst_toolkit
  • model_evaluation_suite

📌 Emoji Codex

To make the vault easier to skim and navigate, each document uses an emoji prefix to signal its purpose or category.

• 📊 Visual Companions & Evaluation Guides
• ✅ Execution Checklists
• 📋 Decision Strategy Cards
• 📘 Deep-Dive Guidebooks
• 🧭 Quick Reference Sheets

For a full legend, see the 📚 Vault Emoji Codex.

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🗺️ Resource Map

🐍 Python Modules

Python/01 - QuickRef/
  ├── 01 - Checklists/               ✅ Execution workflows
  ├── 02 - Decision Cards/          📋 Strategy selectors
  └── 02 - Reference Guides/         🧭 Quick references

Python/02 - Data Wrangling & EDA/
  ├── Data Wrangling/               📘 Feature transformation & validation
  └── EDA/                          📊 Exploratory workflows

Python/03 - Cleaning/              🧼 Foundational and advanced cleaning guides

Python/04 - Machine Learning Models/
  ├── 01 - Regression/              📘 Linear & Logistic modeling resources
  ├── 02 - Supervised/              📊 Classifier guidebooks and visuals
  └── 03 - Unsupervised/            📋 Clustering diagnostics and workflows

Python/05 - Scripts/
  ├── 01 - Python/                  🧪 Cleaning, validation, modeling scripts
  └── 02 - eda_toolkit/             🧰 Modular tools for EDA diagnostics

🚛 SQL Modules

SQL/01 - Guidebooks/               📘 SQL basics to advanced playbooks

SQL/02 - BigQuery and Looker/
  ├── 01 - BigQuery/                🧱 Patterns, optimization, and pipelines
  └── 02 - Looker Studio/           📊 Dashboard UX and parameter guides

🖇️ Workflow + Projects

WorkFlow+Projects/
  ├── ✅ Notebook readiness checklist
  ├── 📘 Project pipeline templates
  └── 🥇 Gold standard scaffolds

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🤝 On Generative AI Use

Generative 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. These tools accelerated development, but the logic, structure, and documentation reflect intentional, human-led design. This repository reflects a collaborative process: where automation supports clarity, and iteration deepens understanding.