My 3-Step Process for Project Write-Ups

Hey there 👋

Having a data portfolio is essential, but what’s inside is even more essential.

What good is a nice project if no one actually looks at it or understands it?

This is why presentation is so important.

And this is why your project write-ups for the work in your portfolio is important.

Often times I see project write-ups that are either way too long or are focusing on the wrong information. They’re putting people to sleep and potential employers are quickly closing their browsers as a result.

Sometimes presentation is everything, and there’s nothing worse than bombing a presentation you worked REALLY hard one.

So let’s look at my 3-step framework for awesome project write-ups.

1 | Questions

What are you trying to uncover or solve?

This is your project's thesis. Start by jotting down 5-10 questions related to your dataset. Imagine you're a stakeholder with a vested interest in the data you’re working with.

For instance, if you’re using a dataset on crime rates, consider what a city leader might want to know. These questions will guide your analysis and form the foundation of your write-up.

You might not answer all questions you start with, but they'll give you plenty of material to work with.

In your write-up, clearly state these questions at the beginning. This sets expectations and direction, providing a strong start to both your project and its presentation.

2 | Process

How did you arrive at your results?

What SQL queries, charts, or methods did you use?

This is where we answer the questions we started with. Aim to answer all of them, but only stick with the 3 or so that are most relevant and interesting.

Be concise yet informative in your explanations. Include screenshots of your code or charts to visually break up the text and engage your audience.

Avoid lengthy paragraphs - keep sentences short and use visuals liberally.

This section should walk the reader through how you created the project, focusing on the most relevant and compelling observations.

3 | Findings

This is the conclusion of your project write-up.

Restate your findings, adding necessary details to each. Utilize callouts or sub-headings for clarity.

The aim is to make your results immediately apparent to readers. Adhere to the 5-second rule for data viz which states that someone should be able to draw conclusions from your visualization within 5 seconds. The quicker they grasp your conclusions, the better, even if you have to spell it out for them in the project.

This section ties back to your initial questions and wraps up the write-up neatly.

Conclusion

By following this structured approach, you'll not only create a meaningful project but also a write-up that's clear, concise, and impactful. Your work will be easy to follow and understand, making your portfolio stand out.

This week’s YouTube video:

Not a new video, but here’s a popular one on how to optimize your LinkedIn profile. Expect more consistent uploads this year after this week.

That’s it for this week.

See you next time

Matt ✌️ 

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