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Project Structuring for Analysts — Modular Blueprint

🎯 Purpose

This guidebook provides a clean, repeatable folder + file architecture for professional data analytics projects. It is designed to support workflows in SQL, Python, notebooks, and dashboards with audit-ready organization and documentation.

project_name/
├── 📁 data/                # Raw, cleaned, or synthetic datasets
│   ├── raw/
│   ├── interim/
│   └── final/
├── 📁 notebooks/           # EDA, modeling, delivery-ready notebooks
├── 📁 scripts/             # Modular Python, SQL, or ETL logic
├── 📁 outputs/             # Model predictions, plots, summary exports
├── 📁 reports/             # PDFs, slides, executive summaries
├── 📁 dashboard/           # Looker Studio links, JSON configs, embed notes
├── 📁 docs/                # Markdown docs, guidebooks, config guides
└── README.md              # Project overview and setup instructions

✔️ Add .gitkeep files or .gitignore rules to manage each folder

📊 2. Notebook Workflow Expectations

Cell Group Purpose
Imports All packages, function definitions
Load & Check Pull in data, show schema, quick null check
EDA Visuals, describe(), value_counts()
Cleaning Missing handling, re-encoding, outlier flags
Feature Engineering New columns, transformations
Modeling Train/test split, metrics, model explanation
Reporting Plots, markdown summary, export/logging

🔁 3. SQL Integration Tips

  • Store production queries in /scripts/sql/
  • Use views or saved queries in BigQuery, referenced from Python
  • Log assumptions in markdown or .sql doc headers

💾 4. Versioning & Exports

  • Save .csv, .pkl, or .json into /outputs/
  • Use joblib, pickle, or feather to preserve modeling objects
  • Final dashboards or exports should be copied into /reports/

📁 5. README.md Template

# 🗂️ Project: Churn Forecasting v1

## Overview
Predict user churn using event and usage logs from Jan–Mar 2024.

## Folder Guide
- `/data/raw/`: Original CSVs
- `/notebooks/`: One notebook per stage: EDA, modeling, presentation
- `/outputs/`: Cleaned datasets, predictions, plots

## Tools Used
- BigQuery, Pandas, Scikit-learn, Looker Studio

## Author
Garrett Schumacher — 2025

✅ Project Structuring Checklist

  • [ ] Folder system initialized and committed
  • [ ] README created with project summary
  • [ ] Scripts modularized and stored in /scripts/
  • [ ] Notebook(s) follow standard cell progression
  • [ ] Dashboard and reports saved to /reports/

💡 Tip

“A structured project isn’t just easier to share — it’s easier to revisit, debug, and scale.”