How to become a data analyst: skills, qualifications, salary and career paths

The short answer: what it takes to become a data analyst

The route into the job has five stages: choose a numerate course after school or college; reach degree level or complete a Level 4 apprenticeship; build the core skills of SQL, a programming language and data visualisation; gain experience through projects, placements or an apprenticeship; then apply for a junior analyst role.

Worth knowing before you start: most entry-level roles ask for a numerate qualification, either a degree or a Level 4 data analyst apprenticeship. According to the National Careers Service, salaries run from about £28,000 for a starter to £65,000 for an experienced analyst, and data analytics is among the most in-demand skill areas in the UK.

What a data analyst does

A data analyst gathers, cleans, and interprets an organisation's data, then presents the findings so teams can make better decisions. The job runs from raw data to a clear recommendation.

The day-to-day tasks

An analyst pulls data from different systems, checks its quality, and uses analytical tools to find patterns, trends and relationships. The output is usually a dashboard, a report or a short set of recommendations, refreshed on a regular cycle and shared with the teams that need it, from marketing to finance.

How the role supports decisions

A data analyst does more than produce figures: they explain what the figures mean. By tracking performance indicators and explaining changes or gaps, the analyst gives managers the evidence they need to choose between options. It is a decision-support role, sitting between the technical side and the business.

Data analyst, data scientist, data engineer and business analyst

These jobs sit close together but are not the same, and knowing the difference helps you choose the right course.

A data scientist builds predictive models and machine-learning algorithms. The profile is more mathematical and forward-looking. A data analyst explains what has happened and is happening; a data scientist predicts what comes next.

A data engineer builds and maintains the pipelines and infrastructure that collect, clean and store data. The engineer builds the plumbing that the analyst then uses: one prepares the data, the other analyses it.

A business analyst translates business teams' needs into requirements and metrics. The profile is less technical and more process-focused, and it is a common bridge into data analysis once you add SQL and programming.

The skills and tools you need

The job calls for a precise technical base and strong communication.

Technical skills

SQL is the baseline: it is the language used to query databases, and it appears on almost every job advert. A programming language, usually Python or sometimes R, lets you explore data, automate tasks and run statistics. Solid foundations in statistics and in data cleaning, validation and quality checks complete the technical base. Machine learning is a useful extra, but it sits closer to the data scientist role at the start.

Visualisation tools

Spreadsheets such as Excel remain handy for quick analysis and sharing, but most of the value now sits in data visualisation. Power BI and Tableau are the tools to know: they turn analysis into the dashboards that teams read. When you are starting out, the priority order is clear: SQL first, then a visualisation tool, then Python.

Business and communication skills

Accuracy and analytical thinking matter, because a poorly checked figure leads to a poor decision. Above all, an analyst has to communicate: explaining a result to non-technical people through a clear chart and a clear message. This is often called data storytelling, and it is what separates a competent analyst from a strong one. Business judgement and strong stakeholder skills count just as much as the tools.

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Do you need a degree to become a data analyst?

This is the central question, and in the UK there is more than one answer.

What employers usually expect

Most analyst roles are filled by graduates with a degree in a numerate subject such as statistics, mathematics, economics, operational research or computer science. Typical entry requirements are two or three A levels, including maths. A master's degree is not always required, but it strengthens access to the more qualified and better-paid roles.

The non-degree routes

A degree is not the only way in. The Level 4 data analyst apprenticeship lets you train with an employer while you earn, and college or T Level routes (typically four or five GCSEs at grades 9 to 4 including English and maths) provide another start. Conversion courses and self-teaching exist too, but they mainly suit career changers rather than students in initial education.

Study and training routes in the UK

There are four recognised routes into the job, and the right one depends on your starting point.

The university degree route

A numerate undergraduate degree is the most common path. At a management school, the bachelor in management and engineering sciences combines scientific foundations with management, which suits a data-leaning profile, while the Global BBA prepares you more broadly to run a business and to specialise towards data later. You can compare the options across SKEMA's undergraduate programmes.

The postgraduate route

At master's level, several degrees lead into the job. The Master in Management offers data- and AI-oriented tracks, while a specialised master's gives a more direct route: the MSc Business Consulting & Decision Intelligence pairs consulting with using data for decisions, the MSc Artificial Intelligence for Business Transformation builds skills in data analysis and AI projects in business, and the MSc in Digital Marketing and AI suits a marketing-data slant. The full MSc programmes hub shows the range.

Apprenticeships and graduate schemes

The Level 4 data analyst apprenticeship is a major UK route, with training delivered alongside a real job. Graduate schemes in data or business intelligence at larger employers are another structured entry point, and often the best paid at the start.

University, business school or apprenticeship: how to choose

A university degree offers academic depth at a lower cost. An apprenticeship offers paid, on-the-job training. A business school builds a dual skill set: data skills and the business judgement to turn analysis into decisions. SKEMA has invested in artificial intelligence since 2020, with a dedicated research centre, the SKEMA Centre for Artificial Intelligence, and teaches data across its programmes on its campuses in Paris, Lille and Sophia Antipolis.

How to get experience and build a portfolio

Employers want proof of skills, not just a qualification. Working with public datasets, taking a placement or an internship, and building two or three projects that you can talk through are the fastest ways to show you can do the work. Present each project as a real business case: the question, the data, the method and the decision it supported. A project carried out from start to finish often counts for more than another line on a CV.

What salary can a data analyst expect?

Pay is one of the job's strong points. It rises quickly with experience and depends on the sector, the employer and the location.

The National Careers Service puts the range at £28,000 for a starter to £65,000 for an experienced analyst. In practice, entry-level roles pay around £23,000 to £25,000, with graduate schemes at larger firms closer to £30,000. A junior analyst earns about £28,000 on average, rising to roughly £34,000 in London. Across all levels the average is about £38,700, a senior analyst earns around £41,000, and an analytics manager around £50,000.

Reaching senior level usually takes two to four years. Pay is higher in finance, healthcare and technology, and in cities such as London, Manchester and Edinburgh.

Career paths and progression

Data analysis exists in almost every sector, which makes the job secure. After a few years, a data analyst can move up to senior or lead analyst, then into related roles such as analytics engineer, data scientist, or product, marketing, or finance analytics, and on to data manager, consulting, or management. Progression follows technical skill as much as the ability to work with decision-makers.

How AI is changing the data analyst role

Artificial intelligence now automates parts of data cleaning and reporting. What it does not replace is the judgement to frame the right business question and to interpret what the numbers mean. That is why analysts who pair data skills with business understanding are well placed, and why the role is shifting towards interpretation and communication rather than disappearing. Building that double skill set early is the best way to keep an analytics career future-proof.