How to Become a Data Analyst: A Beginner’s Career Roadmap

Want to become a data analyst but not sure where to start? This guide covers the skills you need, what the career path looks like, salary expectations, and how to get real work experience, even with no background in data.

Data analytics is one of the most in-demand career paths today, and the demand is not slowing down. Microsoft projected that data analysis, machine learning and AI roles would grow by 20 million globally by 2025, including 350,000 in the UK, and that growth is now playing out across industries.

Yet most people considering it don’t come from a data background, and that’s completely fine. You might be a teacher, a marketer, an engineer, or working in operations. You’ve probably noticed that “data” keeps appearing in job descriptions and wondered: Is this for me? Where do I even start?

This guide is a practical, honest roadmap for anyone who wants to become a data analyst. Whether you’re starting from scratch or repositioning existing skills, you’ll learn what the role involves, the skills you need, what the career path and salary look like, and how to gain real experience that makes you employable.

If you are exploring switching career paths, this could be your moment. 

What does a data analyst actually do?

The role in plain English

A data analyst collects, cleans and interprets data to help organisations make better decisions. Think of it as translating numbers into insight: why are customers leaving? Which campaign is working? Where is the business losing money?

Data analysts sit at the intersection of business and technology. You don’t need to be a mathematician or a software engineer. You need curiosity, logical thinking and the ability to explain findings clearly.

What a typical day looks like

A typical day might include:

• Pulling data from databases using SQL
• Cleaning and organising data in Excel or Python
• Creating dashboards in Tableau or Power BI
• Presenting insights to stakeholders

The work is varied – and every industry needs it, from healthcare and finance to retail and government.

Data analyst vs data scientist vs business analyst

• Data analyst: interprets existing data to answer business questions
• Data scientist: builds predictive models and uses machine learning
• Business analyst: focuses on processes and strategy

For beginners, data analytics is the most accessible entry point, with clear progression into more advanced roles.

Is data analytics a good career?

Demand is growing, not shrinking

Data roles have proven resilient, with only modest declines compared to other tech jobs. Meanwhile, AI is projected to increase UK GDP by up to 22% by 2030 – fuelled by data.

Data analyst salary UK

Typical salary ranges (Glassdoor, 2023):

• Entry-level: £25k–£35k
• Mid-level: up to £49k
• Senior roles: up to £59k
• Executive level: £70k+

Career Accelerator graduates report an average salary uplift of 21.9% (FourthRev 2023/24 Completers’ Survey).

Flexibility across industries

Data analytics skills are transferable. Whether you’re interested in fintech, healthcare, education or climate, there’s a role for you. Many positions also offer remote or hybrid work.

Learn more about how to start and grow a career in data.

What skills do you need to become a data analyst?

Technical skills

• SQL: Used to extract data from databases; appears in up to 60% of data job postings
• Excel/Google Sheets: Essential for analysis, pivot tables and cleaning data
• Python: Widely used for data analysis and automation
• Data visualisation: Tools like Tableau or Power BI help communicate insights
• Statistics fundamentals: Understanding trends, correlations and reliability

Human skills that matter just as much

• Communication
• Problem-solving
• Attention to detail
• Curiosity
• Stakeholder management

These often differentiate good analysts from great ones. If you’re changing careers, this is where your existing experience matters most. 

Read more about why transferable skills matter.

Do you need a degree?

You don’t necessarily need a degree. Employers increasingly prioritise demonstrable skills and portfolios over formal qualifications, and structured programmes can help bridge that gap. 

Your step-by-step roadmap to becoming a data analyst

Step 1: Get clear on your starting point

Before you begin, take stock of where you are. Are you starting from zero, or do you already work with data in some form?

Many people underestimate the skills they already have. If you have ever built a report, analysed a spreadsheet, tracked performance metrics, or made decisions based on numbers, you already have a foundation. 

Data analytics is often less about starting over and more about reframing what you know. 

One of our learners, Emma Roberts, transitioned from chemical engineering into data analytics. Her background in manufacturing and operations gave her strong problem-solving skills and structured thinking, both of which translated directly into analytics. 

Elodie also went through a similar transition: read her story here.

Step 2: Build your technical foundation

Start with SQL and Excel to understand how data is structured and analysed. Then move on to Python and visualisation tools to deepen your skills.

A structured path helps you build confidence without feeling overwhelmed. The LSE Data Analytics Online Career Accelerator is designed this way. You begin with data analytics for business, progress to Python, and then move into more advanced analytics, with each stage building on the last over six months.

Step 3: Get real work experience

Certificates alone are rarely enough. Employers want to see how you apply your skills to real business problems.

Practical experience is key. In the Career Accelerator, the Employer Project gives you a six-week live brief from a real organisation. You work in a team, engage with stakeholders, and deliver a solution in conditions that reflect real work.

“[The practical experience] is probably the reason why I got through the job interview process. Having that tangible evidence… is something I could really talk about in my job interview.” – Jon Minto, LSE Data Analytics Career Accelerator Alumnus

Step 4: Build a portfolio

Aim to include two to four strong projects that clearly demonstrate your approach. Each should show the problem, your analysis process, the tools you used, and the business outcome.

Focus on real-world scenarios where possible. These stand out far more than generic exercises and help employers understand how you think and work.

Past learners like Eleonora Bacchi have used their programme portfolios to move into analyst roles, using project work as clear evidence of their capability.

Step 5: Position yourself for the job market

Once you have built skills and experience, focus on how you present them. Update your CV and LinkedIn profile to highlight outcomes, not just courses completed.

Career coaching can accelerate this step. Olga Sachura credits her FourthRev career coach with helping her “rethink her career” and polish her LinkedIn profile, which led directly to her next role.

“I’m very tempted to say that, for me, perhaps the career coaching was the most valuable part of the programme.”

With the right guidance, you can refine your story, position your experience clearly, and approach the job search with more confidence.

Learn more about how FourthRev’s programmes are structured.

You don’t need a data background to get started

Career changers succeed in data analytics every day:

• A former teacher transitioned into a business intelligence role and progressed rapidly into leadership
• A database assistant became a data analyst at a national charity
• A job seeker moved into a senior data engineering role mid-programme
• A chemical engineer transitioned into data consultancy

These stories highlight a key truth: your previous experience is not wasted – it’s an advantage.

Learn more: real-world results for LSE learners.

How to choose the right data analytics programme

What to look for:

• A structured curriculum
• Real-world projects
• Dedicated career support
• A recognised credential
• A flexible learning format

Red flags:

• No portfolio output
• No career coaching
• No industry connection
• No proven outcomes

Why a university-backed career accelerator is different

The LSE Data Analytics Online Career Accelerator combines academic rigour with practical application:

• Six months, part-time (15–20 hours/week)
• Tools: Excel, SQL, Python, R, Tableau
• Employer Project with a real company
• Certificate of Competence from LSE

It also includes:

• Subject expert facilitators
• A dedicated Career Coach
• A Success Manager

87.5% of learners achieved a career goal within six months (FourthRev 2023/24 Completers’ Survey).

Explore the programme here.

Frequently Asked Questions

How long does it take?
Self-teaching can take 12–18 months. Structured programmes can shorten this to around six months.

Can I start with no experience?
Yes. Focus on building practical skills and project experience.

Do I need a degree?
No. Skills and portfolio matter more.

What tools should I learn first?
Start with SQL and Excel, then add Python and visualisation tools.Is data analytics a good career in 2026?
Yes. Demand continues to grow globally. Read more here.