Here's how to become a data analyst in the UK: learn the core toolkit in order (Excel, then SQL, then a reporting tool such as Power BI or Tableau), build three small portfolio projects that answer real questions, and apply for junior analyst roles or a data analyst apprenticeship. You don't need a degree, although some employers still ask for one. What you do need is proof that you can take messy data and turn it into an answer somebody can act on.
That last sentence is the whole job, and it's why people arrive in this career from so many directions. A common route in is being the person in the team who was good with spreadsheets and kept getting asked to pull the numbers.
This guide covers what data analysts do day to day, the skills to learn and in what order, the routes in with and without a degree, the portfolio that gets you interviews, typical UK pay, and a six-step plan. We sell courses in several of these tools, so we'll also tell you where free resources are enough and when to keep your money.
What does a data analyst do?
A data analyst collects, cleans and analyses data to answer business questions, then presents the answer in a way people can act on, usually as a report, a dashboard or a short recommendation. Which products are selling, why customers leave, where costs are creeping up, whether last month's campaign worked: that's the kind of question that lands on an analyst's desk.
The Skills England data analyst standard, which sets out what an apprentice analyst must be able to do, describes the purpose of the role as working out how data can be used to answer questions and solve problems. In practice, a typical week looks something like this:
Finding out what people actually need to know, which is rarely exactly what they first asked for.
Pulling data out of spreadsheets, databases and business systems, often with SQL.
Cleaning it: fixing dates stored as text, removing duplicates, and working out why two systems disagree about the same customer.
Analysing it, from simple totals and trends to comparisons and basic statistics.
Building reports and dashboards that refresh on their own instead of being rebuilt by hand every Monday.
Explaining what the numbers mean, and what they don't, to people who don't live in spreadsheets.
Handling personal data within the law. The apprenticeship standard lists GDPR compliance among the core duties of the role.
The skills you need, in the order to learn them
Learn the tools in the order the work uses them, and finish each one properly before starting the next. Excel comes first because it's where most business data already lives. SQL comes second because it's how you get data out of the databases behind most business systems. A reporting tool comes third because that's how your work reaches everyone else.
Excel: tables, cleaning functions, lookups and pivot tables, and ideally Power Query, which turns a monthly cleaning job into a one-click refresh. Our guide to XLOOKUP vs VLOOKUP is a good place to check where you stand.
SQL: SELECT, filtering with WHERE, joining tables and totalling with GROUP BY. Get those four fluent before worrying about anything clever.
A business intelligence tool: Power BI or Tableau. Read the ads for the jobs you want and learn whichever one they name. Power BI is Microsoft's, so it's a natural fit in workplaces that already run on Microsoft 365.
Statistics basics: mean versus median, spread, percentages and percentage change, correlation versus causation, and why a small sample can mislead. You need sound judgement here, not a maths degree.
Communication: a clear written summary, the right chart for the question, and the confidence to say "the data can't tell us that". The National Careers Service lists excellent verbal communication alongside maths and analytical thinking.
Python, later: useful for larger datasets and analysis you want to rerun, through libraries such as pandas. Check whether the ads you're targeting mention it. If they don't, make it your fourth tool rather than your first, and Data Analysis with Python is there when you get to it.
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View courseDo you need a degree to become a data analyst?
No. A degree in maths, statistics, economics, operational research or psychology is one route in, and some graduate schemes still ask for one, but it isn't the only route. The National Careers Service lists university, college and apprenticeship routes, and moving across from a related job inside your own organisation is a well-worn path too.
An apprenticeship: the Data Analyst Level 4 apprenticeship in England is a paid job with structured training, built on the Skills England standard above. It typically takes 24 months.
College: the National Careers Service lists a T Level in Digital Data Analytics, alongside college courses in maths, statistics, computing and economics.
A degree: useful, especially for graduate schemes and for heading towards data science later, but not something every analyst job asks for.
Self-taught, backed by a portfolio: learn the toolkit, build projects and apply for junior roles. This route lives or dies on the portfolio, which is the next section.
An internal move: if you already work somewhere with a reporting or analytics team, offering to help them is often the quickest way in of all.
What projects do data analysts do? Build three for your portfolio
At work, analysts spend their time on recurring reports, one-off investigations and the occasional bigger piece of analysis. Your portfolio should show a bit of each, so aim for three solid projects rather than ten thin ones.
Size doesn't make a project good. A real question does, along with honest cleaning, a clear answer and a short write-up that says what you found, what you'd check next and what the data couldn't tell you. That last part matters more than it looks, because knowing the limits of your data is half the job.
Use public data rather than anything from your employer, and pick a subject you actually care about: football results, house prices, your local council's spending. Government and public bodies publish a huge amount of free open data, and enthusiasm shows in the questions you ask.
The pattern we see with career changers is that the first finished project matters more than the tenth lesson. It turns "I'm learning data analysis" into "here's the dashboard I built", which makes for a far easier interview, because the conversation moves from what you studied to what you made.
Here is a set of three that covers the ground:
A cleaning and analysis project in Excel: take a messy public dataset, document every fix you make, and answer one clear question with a pivot table and two or three charts.
A SQL project: load a few related tables into a free database on your own machine, write the queries that answer five realistic business questions, and show each query beside its answer.
A dashboard: an interactive report in Power BI or Tableau that a manager could open on a Monday and understand in a minute. Our guide to building a Power BI dashboard walks you through one.
Data analyst salary in the UK
The National Careers Service puts typical pay for a data analyst at £28,000 to £65,000 a year, from starter to experienced (figures checked September 2026). Its profile covers data analyst-statisticians, so read it as a range for the role rather than a promise for any single job.
For comparison, the National Careers Service profile for data scientists gives £32,000 to £83,000. Data science pays more at the top because it asks for more: deeper statistics and serious programming. That's why analyst work is such a common first step towards it.
Where you land within a range depends on location, sector, seniority and how technical the role is. The salaries in live ads for the exact titles you're targeting will tell you more about your local market than any national figure.
One more honest point: none of these numbers are what a course gets you. They're what the job pays people who can do it, which is a different and more useful thing to know.
Is being a data analyst a good career?
For the right person, yes. Analysts work in almost every sector, and the skills travel: SQL in a retailer is SQL in a hospital trust. It's also a natural base for moving into data science, data engineering or a business-facing role later.
It suits you if you enjoy puzzles, notice when a number looks wrong, and don't mind explaining the same finding three different ways until it lands. It suits you less if you'd rather build software all day.
Be ready for cleaning, too. It's a bigger part of the job than most people expect, and it isn't glamorous, but it's where good analysts quietly earn their reputation, because a beautiful chart built on duplicated rows is just a confident mistake.
AI tools can now draft a query, suggest a formula or summarise a table in seconds. Somebody still has to know whether the result is right and what it means for the business, and that judgement is the part of the job worth learning properly.
How to become a data analyst: a 6-step plan
Here's the plan we'd give a friend. It assumes you're comfortable with a computer and basic spreadsheets. If you're not there yet, add a step zero and get there first.
Step 1: get properly good at Excel for analysis. Tables, cleaning, lookups and pivot tables, until you can turn a messy export into a clean summary without looking anything up.
Step 2: learn SQL to the point of joins and GROUP BY, practising on a free database on your own machine.
Step 3: learn one BI tool, chosen by reading ten job ads in your area and counting which one they name.
Step 4: pick up the statistics you'll actually use, alongside the tools rather than before them.
Step 5: build your three portfolio projects, publish them somewhere you can link to, and write a short summary of each.
Step 6: rewrite your CV around the projects and apply widely: junior, reporting, MI (management information) and insight analyst roles, apprenticeships, and any reporting work going inside your current employer.
How long does it take to become a data analyst?
It depends on where you start and how many hours a week you have, and nobody can give you a fixed number of weeks that holds for everyone. Employers don't hire "fast learners" anyway. They hire people who can already do the specific Tuesday-morning tasks of the role: pull last week's numbers, clean them and explain what changed.
As a rough shape rather than a promise: if you already use spreadsheets at work and can give it a few focused hours a week, learning the core toolkit is a matter of months rather than years. From a standing start, plan for longer. Then add time for the portfolio and the job search, which runs on its own clock.
For a sense of scale, the Level 4 apprenticeship typically runs for 24 months, with a paid analytics job alongside the training. You won't need two years before you can apply for junior roles, but it's a useful reminder that the learning keeps going after you're hired.
Where the Data Analyst Bundle fits, and when to skip it
If the plan above fits you, our Data Analyst Bundle covers the first three steps in order: Excel for Data Analysis, then SQL, then Power BI. You work at your own pace, you pay once and keep lifetime access, and the OBA Tutor sits beside every lesson to explain and give hints when you're stuck, without doing the work for you.
Each course ends with an OBA certificate that employers can verify online. It's a certificate of completion, not a Microsoft certification or a formal qualification. If you later want Microsoft's own credential, the Power BI Data Analyst Associate certification (exam PL-300) is a paid, proctored exam that's renewed every 12 months through a free online assessment, and it's best taken once you can already build reports. Either way, the certificate shows you finished. Your three projects show you can do the work.
Two situations where we'd tell you to wait. If you already use Excel every day at work, start by volunteering for reporting tasks in your current job before you buy anything: offer to build the monthly summary, ask the reporting team what they'd hand over, and find out whether you enjoy working with real data and real deadlines. And if your goal is machine learning research rather than business analysis, a degree in statistics, maths or computer science is the better route, and no short course replaces it.
Still weighing it up? The free info pack sets out what each course covers and what you need before you start, so you can decide before spending anything.
Good to know
Common questions
A data analyst collects, cleans and analyses data to answer business questions, then presents the answer as a report, dashboard or recommendation. Day to day that means pulling data with tools such as Excel and SQL, fixing errors in it, finding trends and explaining what they mean to the people who need to act on them.
Yes. Some employers ask for a degree, but apprenticeships, internal moves and self-taught routes backed by a portfolio all lead into the job. In England, the Data Analyst Level 4 apprenticeship is a paid job with structured training that typically takes 24 months.
The National Careers Service puts typical data analyst pay at £28,000 to £65,000 a year, from starter to experienced. Location, sector, seniority and how technical the role is all move you within that range, so check live ads for the titles you're targeting.
For people who enjoy puzzles and explaining what numbers mean, yes. Analysts work in almost every sector, the core skills transfer between employers, and the role is a common base for moving into data science or data engineering later.
It depends on your starting point and the hours you can give it. If you already work with spreadsheets, learning Excel for analysis, SQL and a BI tool is realistic within months rather than years of steady study, plus time to build a portfolio and apply. From a standing start, plan for longer.
Not always. Plenty of analyst work runs on Excel, SQL and a BI tool such as Power BI, and Python earns its place with larger datasets and analysis you want to rerun. Check the ads you're targeting: if they don't mention Python, learn it fourth rather than first.
There's no single required qualification. Employers look for evidence of skill, which can be a relevant degree, an apprenticeship, a vendor certification such as Microsoft's PL-300 for Power BI, or a portfolio of projects that shows you can do the work.
