[Project Title]

One sentence. What did you analyze, build, or solve - and why does it matter?

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⚙️ Project Type Flags

Check what applies. This helps reviewers and collaborators understand the nature of the work at a glance. Delete this block before publishing.

• Exploratory Data Analysis (EDA)
• SQL Analysis / Querying
• Dashboard / Data Visualization
• Data Pipeline / ETL
• Predictive Modelling / Machine Learning
• Data Cleaning / Wrangling
• End-to-End (multiple of the above)
• Other: ___________

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Table of Contents

1. Project Overview
2. Objectives
3. Project Scope & Tools
4. Repository Structure
5. Data Workflow
6. Data Model & Schema
7. ERD - Entity Relationship Diagram (SQL projects)
8. Analysis & Metrics
9. Key Insights
10. Recommendations
11. Assumptions & Limitations
12. Future Enhancements
13. Deliverables
14. Author

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1. Project Overview

Context: [The business, research, or personal situation that motivated this project.]

Problem Statement: [The specific question or challenge you were addressing.]

Approach: [In 1–2 sentences - how did you tackle it?]

Outcome: [What did you produce or discover?]

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2. Objectives

• Primary Objective: [The main thing you set out to do]
• Secondary Objective 1: [Supporting goal]
• Secondary Objective 2: [Supporting goal]
• Secondary Objective 3: [Remove if not applicable]

💡 Every analysis decision in this project traces back to one of these objectives.

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3. Project Scope & Tools

Scope

Dimension	Details
In Scope	[What is included - data sources, time periods, segments]
Out of Scope	[What you explicitly excluded - and a brief reason why]
Time Period	[Date range of the data or the project itself]
Granularity	[Unit of analysis - row-level, daily aggregates, per-user, etc.]

Tools & Technologies

Category	Tool(s) Used
Data Storage	[e.g., PostgreSQL, CSV files, BigQuery, S3]
Data Processing	[e.g., Python, R, SQL, Excel, dbt]
Analysis	[e.g., pandas, dplyr, custom SQL queries]
Visualization	[e.g., Matplotlib, Tableau, Power BI, Looker]
Version Control	[e.g., Git / GitHub]
Documentation	[e.g., Markdown, Notion]
Other	[Any additional tools]

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4. Repository Structure

[project-root]/
│
├── data/
│   ├── raw/                  # Original, unmodified source data - never edited
│   ├── processed/            # Cleaned and transformed data
│   └── external/             # Reference data, lookup tables, third-party files
│
├── notebooks/                # Jupyter, R Markdown, or Colab notebooks
│
├── scripts/                  # Reusable .py, .R, or .sh processing files
│
├── queries/                  # SQL files (retain this folder for SQL-heavy projects)
│   ├── exploratory/          # Ad-hoc or investigative queries
│   ├── transformations/      # Cleaning and reshaping logic
│   └── final/                # Production-ready or presentation queries
│
├── reports/                  # Final outputs: PDFs, slide decks, Word docs
│
├── visuals/                  # Exported charts, dashboard screenshots, ERD diagrams
│
├── docs/                     # Data dictionaries, schema notes, reference material
│
├── project_metadata.yml      # Machine-readable metadata (optional)
└── README.md                 # You are here

⚠️ Delete folders you didn't use. An empty folder is worse than no folder. SQL-heavy projects: keep queries/. Analysis-only projects: keep notebooks/. Both? Keep both.

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5. Data Workflow

[Data Source(s)]
      ↓
[Ingestion / Collection Method]
      ↓
[Cleaning & Transformation]
      ↓
[Analysis / Modelling / Querying]
      ↓
[Output / Visualisation / Reporting]

1. Source: [Where did the data come from? Format, size, access method.]
2. Ingestion: [How was it brought in?]
3. Cleaning: [What issues did you find and fix?]
4. Transformation: [What new fields, aggregations, or structures did you create?]
5. Analysis: [What methods - statistical, visual, query-based, model-based?]
6. Output: [What form do the results take?]

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6. Data Model & Schema

Dataset / Table: [name]

Field Name	Data Type	Description	Example Value
[field_1]	[string / int / date / float / boolean]	[What this field represents]	[Non-sensitive example]
[field_2]	[string / int / date / float / boolean]	[What this field represents]	[Non-sensitive example]
[field_3]	[string / int / date / float / boolean]	[What this field represents]	[Non-sensitive example]

Row count (approx.): [X rows] Date range: [Start] – [End] Key join / relationship: [e.g., orders.customer_id → customers.id]

Add additional table blocks as needed for multi-table projects.

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7. ERD - Entity Relationship Diagram

(Primarily for SQL Projects - remove this section if not applicable)

Option A - Embedded Image

[Brief caption: e.g., "Three-table schema - orders, customers, and products joined on shared IDs."]

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Option B - dbdiagram.io Schema Definition

Table orders {
  order_id    int     [pk]
  customer_id int     [ref: > customers.customer_id]
  product_id  int     [ref: > products.product_id]
  order_date  date
  amount      float
}

Table customers {
  customer_id int  [pk]
  region_code string
  signup_date date
}

Table products {
  product_id   int    [pk]
  category     string
  unit_price   float
}

Paste this into dbdiagram.io to view the visual.

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Option C - Mermaid Diagram (renders on GitHub)

erDiagram
    ORDERS {
        int order_id PK
        int customer_id FK
        int product_id FK
        date order_date
        float amount
    }
    CUSTOMERS {
        int customer_id PK
        string region_code
        date signup_date
    }
    PRODUCTS {
        int product_id PK
        string category
        float unit_price
    }
    ORDERS ||--o{ CUSTOMERS : "placed by"
    ORDERS ||--o{ PRODUCTS : "contains"

Loading

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Table Relationships Summary:

Relationship	Join Key	Type
orders → customers	customer_id	Many-to-One
orders → products	product_id	Many-to-One
[Add rows as needed]

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8. Analysis & Metrics

Analytical Approach

[Describe how you approached the analysis. Were you exploring patterns? Testing a hypothesis? Building and validating a pipeline? Be honest about your method - exploratory work is valid, just call it that.]

Key Metrics Defined

Metric	Plain-Language Definition	Why It Matters
[Metric 1]	[What it measures, in one sentence]	[What decision or question it answers]
[Metric 2]	[What it measures, in one sentence]	[What decision or question it answers]
[Metric 3]	[What it measures, in one sentence]	[What decision or question it answers]

Methods Used

• [e.g., Descriptive statistics - distribution, central tendency, outlier detection]
• [e.g., Trend analysis across [time period]]
• [e.g., Segmentation / group comparison by [dimension]]
• [e.g., Correlation analysis between [variable A] and [variable B]]
• [e.g., SQL window functions for [specific aggregation]]
• [e.g., Custom aggregation or transformation logic in [tool]]

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9. Key Insights

Insight 1: [Short descriptive headline] [What you found + what it suggests. One short paragraph.]

Insight 2: [Short descriptive headline] [What you found + what it suggests.]

Insight 3: [Short descriptive headline] [What you found + what it suggests.]

Insight 4 (if applicable): [Short descriptive headline] [What you found + what it suggests.]

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10. Recommendations

Priority	Recommendation	Based On	Suggested Owner
High	[Specific, actionable step]	[Insight it comes from]	[Who should act]
Medium	[Specific, actionable step]	[Insight it comes from]	[Who should act]
Low	[Exploratory or longer-term suggestion]	[Insight it comes from]	[Who should act]

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11. Assumptions & Limitations

Assumptions

• [What did you treat as true without being able to verify?]
• [What simplifications did you make for scope or feasibility?]
• [What domain rules or definitions did you accept as given?]

Limitations

• [What gaps exist in the data?]
• [What analysis was out of scope but could affect interpretation?]
• [What would a more rigorous version of this project include?]
• [Are there known biases in the data source or collection method?]

The goal here is pre-emptive Q&A. What would a thoughtful skeptic push back on? Document the answer here, before they ask.

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12. Future Enhancements

• [Enhancement 1 - specific and traceable to a real gap in this project]
• [Enhancement 2]
• [Enhancement 3]
• [Enhancement 4]

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13. Deliverables

Deliverable	Description	Location
[Name]	[What it contains]	[/path/to/file]
[Name]	[What it contains]	[/path/to/file]
[Name]	[What it contains]	[/path/to/file]

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14. Author

[Your Name] [Your role or title - current or target]

• 🔗 [LinkedIn URL]
• 💼 [Portfolio or GitHub profile URL]
• 📧 [Email - optional]

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Last updated: [Month YYYY] If this template helped you, consider starring the repository.