How to write a graduate data analyst CV, with a UK example

A graduate data analyst CV needs to show three things quickly: that you can use the core tools (SQL, Excel and a dashboard tool such as Power BI), that you’ve used them on real data to answer a question, and that you can explain what you found. Keep it to two A4 pages at most, put your education and a short projects section near the top, and describe each tool with what you did and what came of it. The fictional example below shows one way to lay it out.

What does a data analyst CV need to show?#

Start with what the job involves. Prospects’ data analyst job profile says data analysts need excellent numerical, data manipulation and analytical skills, and excellent communication skills to understand what people need and present data clearly. It lists tools such as Excel, SQL, Power BI, Tableau, Python and R. The civil service’s data analyst role profile adds cleaning and aggregating data, coding, checking that data is fit for purpose, presenting it with clear visualisations, and understanding the legal and ethical issues around data, such as privacy.

So your CV should give evidence for each of these:

Don’t forget the rest of you. King’s College London’s careers service warns that a CV that reads like a long list of programming languages is unlikely to engage a recruiter, and that many tech employers value communication, teamwork and leadership as much as code.

Read each advert’s essential criteria before you start. If an advert is vague, the free job ad decoder can pick out the must-haves and nice-to-haves for you.

How long should it be, and what goes first?#

Prospects’ guide to writing a CV recommends no more than two A4 pages, and says work experience should come before education only if you have plenty of relevant experience. Most graduates don’t, so education usually goes first. The National Careers Service’s CV guide says to include your name, phone number, email address and a link to a profile such as LinkedIn, and to leave out your age, date of birth, marital status and nationality.

For a graduate data analyst, this order works well:

1. Name and contact details, with links to your LinkedIn profile and GitHub.
2. Profile. Two or three lines naming the role you want, your main tools and what you’re looking for.
3. Education. Your degree, grade (or expected grade) and dates, plus relevant modules and your dissertation if it used data.
4. Data projects. Two or three, each with a link.
5. Experience. Part-time jobs, internships, volunteering and society roles, most recent first.
6. Skills. Your tools, grouped, with what you can do in each.
7. Training and certificates. Courses and exams, with dates.

Putting projects above experience is deliberate: unless you’ve had a data job, they’re likely to be your strongest evidence of data skills. If you already have a data job or internship, put that first instead.

Make sure your LinkedIn profile tells the same story as your CV. The free LinkedIn profile check reviews your headline, About section and experience for a data analyst role.

How do you show SQL, Excel and Power BI?#

Name the tool, say what you did with it and say what came of it. The Oxford careers service suggests using numbers, percentages and values to show your impact and starting bullet points with action verbs, and Prospects suggests active verbs such as “created”, “analysed” and “devised”.

The examples below are made up to show the idea. Use your own real details.

In your skills section, group the tools and say what you can do in each, for example “SQL (PostgreSQL): joins, grouping, CTEs, window functions”. The Prospects technical CV example does the same, listing the languages and packages the candidate can use.

Only claim what you can do in front of an interviewer. Prospects warns against exaggerating your abilities, because you’ll need to back up your claims at interview.

How do you list projects and training?#

Give each project a short title, the tools you used, one line on the question and one or two on what you did and found, then a link. The Prospects technical CV example includes academic projects and GitHub repositories, and King’s College London’s careers service points out that publishing your project work, for example on GitHub, shows your dedication.

• Choose projects that match the job. A retail insight role suits a project on sales or shopping data. A public sector role suits one on public services.
• Lead with the question, not the tool: “Which parts of the city centre are busiest on Saturdays?” says more than “Power BI project”.
• Include a result, such as a number, a recommendation or who used it.
• Count your dissertation if it used data. Describe the method and tools, not only the topic.

List courses and certificates briefly under a training heading, with dates or “in progress”. A certificate shows commitment; a project shows what you can do with the same tools.

If you need project ideas, our guide to building a data analyst portfolio has five that use free UK data, and explains how to present them on GitHub.

A full example for a fictional graduate#

Jordan Ellis is not a real person. The name, contact details, jobs and results below are invented to show a layout you can adapt. Jordan’s projects use real, free UK datasets, which are described in our data analyst portfolio guide. At the top of the page, Jordan gives their name, “Leeds”, the phone number 07700 900123, the email address jordan.ellis@example.com and links to their LinkedIn profile and GitHub.

Profile

Economics graduate (2:1) looking for a graduate or junior data analyst role in Yorkshire or remote. I use SQL, Excel and Power BI to turn public data into clear recommendations, and I’ve published three projects on UK open data on GitHub.

Education

BSc Economics, 2:1, a UK university, 2023 to 2026

• Relevant modules: Econometrics (68%), Applied Statistics (71%), Economics of Housing (66%).
• Dissertation (70%): “Did house prices rise faster near new railway stations?” Cleaned and matched ten years of HM Land Registry sales data in Python (pandas) and tested the difference with a regression.

A levels: Maths (A), Economics (A), Geography (B).

Data projects

• Greener electricity tracker (Python, SQLite, GitHub Actions). Wrote a Python pipeline that collects half-hourly carbon intensity forecasts for one region from the National Energy System Operator’s Carbon Intensity API every day and stores them in SQLite. A chart in the README shows the greenest times to use electricity.
• Road collisions by time of day (SQL, PostgreSQL, Power BI). Loaded five years of Department for Transport road safety data, joined the collision, vehicle and casualty tables, and used window functions to rank hours and weekdays. Wrote a two-page summary with three recommendations for a council road safety team.
• Online shopping since 2019 (Excel, Power BI). Built a Power BI report from Office for National Statistics (ONS) retail sales data on the share of sales made online, with DAX measures for year-on-year change and notes on what the data can’t show.

Experience

Customer assistant (part-time), a supermarket in Leeds, 2023 to 2026

• Served customers and handled the tills on weekend shifts, and trained four new starters.
• Suggested and built an Excel stock count sheet with XLOOKUP that cut the weekly count from three hours to two. The store still uses it.

Treasurer, university hiking society, 2024 to 2025

• Managed a £3,500 budget in Excel and built a Power BI dashboard of spending by trip for committee meetings.
• Used the dashboard to propose a new membership price, which members approved at the annual meeting.

Skills

• SQL (PostgreSQL, SQLite): joins, grouping, CTEs, window functions.
• Excel: pivot tables, XLOOKUP, Power Query, charts.
• Power BI: data modelling, DAX measures, report design.
• Python: pandas, matplotlib, reading data from APIs.
• Git and GitHub: version control, READMEs, scheduled workflows.

Training

• Microsoft PL-300 (Power BI Data Analyst), studying, exam booked for November 2026.
• Free online SQL course, completed 2025.

Why this example works

• The projects come before the jobs, because they’re Jordan’s strongest evidence of data skills.
• Every tool comes with a task and a result, so an employer can see what Jordan can actually do.
• The part-time jobs still count. They show reliability and teamwork, and each one includes a data example.
• The links let an employer check the work, and every project is one Jordan could explain line by line.

What are the common mistakes?#

• A skills list with no evidence. Every tool on your CV should appear again in a project or job.
• Skill bars, star ratings and graphics. They don’t tell an employer what you can do, and the University of Bristol’s careers service warns that columns, tables, text boxes and graphics can confuse applicant tracking systems. LSE Careers also suggests a single-column layout with standard section headings.
• Jargon and unexplained abbreviations. Oxford’s careers service advises plain language without jargon or acronyms. Write “Power BI” rather than “PBI”, and explain a method in a few words.
• Personal details a UK CV doesn’t need. Prospects advises against a photo, and the National Careers Service says to leave out your date of birth and nationality.
• Only well-known practice datasets. Projects that appear in many courses tell an employer less about you than a question you chose yourself.
• The same CV for every job. Reorder your projects and bullet points so the ones closest to each advert come first.
• Claims you can’t explain. Expect to be asked about anything on your CV, so leave out tools you’ve only watched a video about.

For layout, length and wording that apply to any graduate job, read our guide on how to write a graduate CV.