https://github.com/g-schumacher44/analyst_resource_hub
A collection of guidebooks, quickref, and resources for data analysis
https://github.com/g-schumacher44/analyst_resource_hub
analytics bigquery data lookerstudio machine-learning model python sql yaml-configuration
Last synced: 2 months ago
JSON representation
A collection of guidebooks, quickref, and resources for data analysis
- Host: GitHub
- URL: https://github.com/g-schumacher44/analyst_resource_hub
- Owner: G-Schumacher44
- Created: 2025-07-14T18:14:37.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-08-11T06:21:35.000Z (2 months ago)
- Last Synced: 2025-08-11T06:23:11.055Z (2 months ago)
- Topics: analytics, bigquery, data, lookerstudio, machine-learning, model, python, sql, yaml-configuration
- Homepage: https://g-schumacher44.github.io/analyst_resource_hub/
- Size: 4.33 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
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](https://g-schumacher44.github.io/analyst_resource_hub/) 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`](https://github.com/G-Schumacher44/analyst_toolkit)
- [`model_evaluation_suite`](https://github.com/G-Schumacher44/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](emoji_codex.md).
___
# 🗺️ Resource Map
```txt
🐍 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
```
___
## 🤝 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.