Data Science · Scholarship

How to write a data science student CV for a scholarship

A scholarship CV rewards merit and potential — committees look for academic strength, leadership and community impact, not job history. Here's how to do it as a data science student.

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What matters for a scholarship

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Academics first

Lead with grades, awards, honours and academic achievements — they anchor the decision.

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Leadership & service

Committees value initiative: society roles, mentoring, volunteering and organising.

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A clear goal

A short line on what the scholarship will help you achieve strengthens your case.

Data Science specifics

Data and analytics recruiters look for projects, statistical and programming skills, and the ability to turn data into insight. Frame it as a question and an insight: “Analysed 10k survey responses in Python to identify three churn drivers; visualised findings in a dashboard used by the society committee.”

Frequently asked questions

How is a scholarship CV different from a job CV?

It foregrounds academic merit, leadership and potential over work experience. See our scholarship CV guide.

How long should a scholarship CV be?

One page is ideal; up to two is acceptable if you have substantial achievements, publications or leadership to evidence.

What projects should a data science student CV include?

Two to four end-to-end projects that show the full pipeline — sourcing data, analysis, and a clear, communicated insight.

Data Science CV for other goals

Build your data science CV for a scholarship

Answer a few guided questions and download a tailored, ATS-friendly CV.

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