General Data Story Instructions

Overview

In Part II of the course (weeks 5–10), you will apply the tools and

skills you learned in Part I to create your own data stories. You'll

cycle through three stages repeatedly:

1. Pitching and brainstorming. Present your ideas to classmates,

form groups, and workshop story ideas.

2. Refinement. Workshop your ideas and code. Bring up new content,

tools, or techniques you'd like to learn more about.

3. Critiquing. As a class, we'll discuss your story and provide

constructive feedback. We'll also go through your code and discuss

relevant coding issues (challenges, clever hacks, etc.).

You should plan to make it through this cycle at least three times

during Part II — that is, you should produce at least 3 data stories.

What is a Data Story?

A data story is a short (5-minute) presentation that uses data to tell

a compelling narrative. It combines:

• A question you want to answer or explore
• Data that helps you investigate that question
• Analysis and visualizations that reveal patterns and insights
• A narrative that ties it all together for your audience

You can work individually or in groups of any size. Projects and

groups should form organically and remain flexible.

Deliverables

Each data story should be contained in a single sub-folder of

data-stories.

Your project should include the following files, based on the

project template:

1. README.md

Based on the

README template,

your README should contain:

• Project information: project name, authors, GitHub usernames
• Overview: main question, approach, data used, tools used, key findings
• YouTube link: a link to your 5-minute data story video
• Data links: where to find the data you analyzed
• Running the code: instructions for replicating your results
• Contributing: next steps, open questions, known issues
• Acknowledgements and citations

2. YouTube Video (5 minutes)

A video presentation of your data story. You can:

• Narrate over a screencast of your notebook
• Create slides (using Marp, PowerPoint, Keynote, etc.)
• Use any other creative format you like

The key is to tell a compelling story backed by data, not just

walk through code.

3. Code

Your project's code (Jupyter notebooks, Python scripts, etc.), based on

the notebook template.

Your code should be well-documented and reproducible.

4. Data

• If your data files are under 10 MB total, include them directly

in your project folder.

• If larger, host them on Google Drive, Dropbox, or another accessible

cloud source.

• Either way, your notebook must include code for downloading and

importing the data so anyone can reproduce your results.

Weekly Rhythm

Tips for Great Data Stories

• Start with a question, not a dataset. What do you want to know?
• Keep it focused. A 5-minute story should make one or two clear

points, not try to cover everything.

• Show, don't tell. Let your visualizations carry the narrative.
• Iterate. Your first attempt won't be perfect — use feedback from

classmates to improve.

• Be creative. The best stories surprise the audience or change how

they think about something.

• Collaborate. Build off each others' code, questions, and ideas.

Interdisciplinary teams often produce the most interesting work.

Finding Data

Need inspiration? Here are some places to find interesting datasets:

• Kaggle Datasets
• FiveThirtyEight Data
• Awesome Public Datasets
• World Bank Open Data
• World Health Organization
• Registry of Open Data on AWS
• Reddit: r/datasets
• Google Dataset Search

Evaluation

Each data story will be evaluated on:

Submitting Your Story

1. Create a folder in data-stories/ named descriptively (e.g.,

data-stories/climate-trends/)

2. Include your README.md, notebook(s), and data files
3. Upload your video to YouTube (unlisted is fine)
4. Submit a pull request to the course repository