☕ Data Visualization Ethics Challenge

This repository contains a fictional coffee chain sales dataset and a starter Jupyter Notebook for the Data Visualization Ethics Challenge.

📌 Overview

In this challenge, you’ll practice applying ethical design principles by fixing two intentionally bad charts.
The goal is to identify misleading or unclear design choices and create clear, accurate, and honest visualizations that follow best practices.

You will:

1. Identify problems with each bad chart.
2. Redesign them to follow ethical visualization guidelines.
3. Document your changes and explain why they improve the chart.

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📂 Repository Contents

• coffee_sales.csv – Fictional dataset containing multiple years of sales data for a coffee chain.
• ethics_challenge.ipynb – Starter notebook containing:
  • The dataset import code
  • Two intentionally bad charts
  • Prompts for you to analyze and improve each chart
  • Cells for creating your improved visualizations and writing explanations

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📝 Instructions

1. Clone this repository

git clone https://github.com/CodeYouOrg/DA_M3_DataVizChallenge.git

2. Open the notebook

You can open directly into VS Code via the terminal:

code DA_M3_DataVizChallenge

3. Complete the challenge

• Review each bad chart.
• Write down everything you find wrong with it in the provided Markdown cell.
• Create an improved chart using the dataset.
• Explain what you changed and why.

4. Save and submit

Save your updated .ipynb file, then commit and push your changes:

git add data_viz_ethics_challenge.ipynb
git commit -m "Completed Data Visualization Ethics Challenge"
git push

Submit the GitHub repository link as your final project.

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📊 Dataset Details

The dataset contains fictional sales data for a coffee chain across multiple years and locations.

Columns include:

• Year
• Month
• Region
• Store
• Product
• Sales (in millions)
• Marketing Spend (in millions)

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✅ Best Practices Reminder

When redesigning your charts:

• Use the correct variables for the message you want to communicate.
• Start the y-axis at zero unless you have a transparent reason not to.
• Use neutral colors unless highlighting a legitimate difference.
• Add clear axis labels, a descriptive (neutral) title, and the data source.
• Avoid “chartjunk” - unnecessary 3D effects, heavy gridlines, or distracting colors.
• Always provide context and avoid misleading design choices.

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📬 Submission

Once you’ve committed and pushed your changes, submit your repository link in OpenClass.

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Have fun, and remember: Good charts tell the truth.