Tag: Data Analytics

From Spreadsheets to Strategy: How to Move Beyond Excel and Into Real Data Analytics

Posted on by Marisa Crous

If you’re the person everyone comes to when the numbers need untangling, you’re already closer to a data analytics career than you think.

Maybe you’ve built five-tab Excel dashboards for your team. Maybe you live inside pivot tables, VLOOKUPs and forecasting spreadsheets. Maybe your role title says “operations manager”, “finance lead” or “marketing executive”, but half your job already revolves around data.

And yet, when organisations hire “data analysts”, the role often sits elsewhere.

That gap is becoming more visible as businesses shift from spreadsheets to scalable analytics, AI-assisted workflows and real-time decision-making. Data roles have grown significantly faster than the wider job market, while employers increasingly expect analysts to work across SQL, visualisation tools, Python and GenAI-enabled workflows.

The good news is this: moving beyond Excel does not mean starting your career over. In fact, your existing business experience may be your biggest advantage.

As Luis Cantu, an alumnus of the LSE Data Analytics Online Career Accelerator, puts it:

“What I was looking for was to reposition myself, leveraging my commercial background, but building a set of tools that would help me provide commercial teams with better insights.”

This article walks through a practical four-step path to make that transition – from diagnosing the Excel ceiling to building a portfolio and positioning yourself for a real analytics role.

The Excel ceiling – and why it holds so many careers back

Excel is still one of the most important business tools in the world. It teaches people how data is structured, how analysis works and how decisions get made.

But there comes a point where Excel stops scaling.

You feel it when spreadsheets start crashing under large datasets. When version control turns into “Final_v7_REALfinal.xlsx”. When every weekly report depends on you manually refreshing formulas. When teams can’t collaborate cleanly across systems. When repetitive work eats up hours that should be spent solving problems.

That’s the operational ceiling. Then there’s the career ceiling.

Today, “advanced Excel skills” alone rarely signal analyst-level capability to employers. SQL now appears in up to 60% of data analyst job postings, while Python and visualisation platforms increasingly sit alongside it in role requirements.

This matters especially for professionals trying to make a data analyst career change without “official” analyst experience. Many people already do analytical work every day – they just lack the technical stack and portfolio evidence that hiring managers recognise.

As Harry Kelleher, Course Facilitator on the LSE Data Analytics Online Career Accelerator, explained during a recent webinar:

“The analyst role has become a lot broader… not just doing your technical job very well, but having a very good understanding of the business problems that you’re investigating.”

Your Excel experience already gives you the business foundation. Modern analytics tools help you scale that impact across larger datasets, faster workflows and more strategic decisions. 

For a broader beginner’s view of the data analyst career path, our guide on how to become a data analyst covers the foundations – this article picks up where that one ends, for readers already comfortable in Excel.

And the market shift is structural, not cyclical – there are clear reasons why now is the best time to pursue a career in data, regardless of where you’re starting from.

Step 1 – Recognise when Excel stops scaling

The first step in any data analyst learning path is recognising the signs that your current workflow has outgrown spreadsheets.

There are usually five clear indicators:

1. Your datasets are becoming too large

Excel works brilliantly – until it doesn’t. Large operational datasets quickly become slow, unstable and difficult to manage.

2. Collaboration becomes messy

Spreadsheets are notoriously difficult to govern across teams. Different versions, conflicting formulas and unclear ownership create friction fast.

3. You become the bottleneck

If every dashboard refresh depends on you manually updating formulas or cleaning data, the process cannot scale.

4. Automation is limited

Modern analytics workflows rely on repeatability. Excel often struggles with robust automation, especially when pulling from multiple systems.

5. Insight delivery becomes reactive

You spend more time maintaining reports than solving strategic problems. That’s often the point where professionals start looking beyond spreadsheets.

Excel users already bring valuable business context, stakeholder awareness and operational understanding. The next step is building the technical stack that helps you scale that impact.

Step 2 – Learn the stack that replaces Excel at scale

The transition from spreadsheet user to analyst is less about learning “coding” and more about learning the right workflow.

Strong data analyst skills today are typically built in layers – each tool solving a problem Excel cannot solve efficiently at scale.

Start with SQL – the single most in-demand data skill

If you’re moving from Excel to SQL, the good news is this: the leap is smaller than most people expect.

SQL is essentially the language of business databases. It allows analysts to filter, join, aggregate and analyse millions of rows directly from the source.

For Excel users, the mental model already exists.

As Harry Kelleher explained:

“A really important foundation for anyone working with AI is knowledge in the subject matter, and therefore knowing your basics in coding, and being able to understand the data analytics lifecycle.”

Think of it this way:

  • Filters become WHERE
  • VLOOKUPs become JOINs
  • Pivot tables become GROUP BY

A good first milestone? Replicate one Excel report in SQL within two weeks.

Add visualisation – Tableau or Power BI

Excel charts communicate information. Tableau and Power BI communicate decisions.

Modern organisations increasingly expect dashboards to be interactive, shareable and connected to live data sources. Analysts are now expected to build tools businesses actually use – not static screenshots pasted into PowerPoint decks.

Luis Cantu, now a Data and Insights Analyst at Leeds United Football Club, moved into data analytics from a career in independent commercial consulting. In his current role, he has seen firsthand how effective data visualisation can influence decision-making:

“Showing them dashboards that come from the analysis… and condensing it into a concrete, well-presented dashboard… helped me feel confident going in front of directors.”

The programme includes dedicated visualisation training using Tableau, helping learners move from “reporting numbers” to telling business stories.

Layer in Python – for automation and real analytics

Python is often the point where Excel users stop repeating manual work and start automating it.

Instead of spending hours cleaning spreadsheets every week, Python allows analysts to automate workflows, process large datasets and perform analysis at scale.

It also unlocks capabilities that Excel simply cannot support effectively:

  • Predictive modelling
  • Sentiment analysis
  • API integration
  • Unstructured data analysis
  • Scalable automation

Python is often the point where Excel users start automating repetitive work. 

As Luis Cantu explained:

“Using advanced statistical tools like R and Python to really dig into the data using just a few lines of code is really powerful.”

Don’t skip GenAI fluency

The future-thinking analyst works alongside AI, using it to solve problems faster and more strategically. 

During the webinar, Harry Kelleher described it this way:

“We’re going to be writing less code, and we’re going to be more orchestrating the use of AI, and doing a lot more strategic thinking.”

That means analysts increasingly need to know:

  • How to prompt effectively
  • How to validate outputs
  • How to identify hallucinations or bias
  • How to apply AI within business context
  • How to communicate findings responsibly

The LSE research on data literacy in the AI era reinforces this change: the competitive advantage is moving away from pure tool execution and towards problem framing, strategic thinking and human judgement.

This stack opens up a much wider career surface than Excel alone – see the full range of careers you can pursue with data analytics skills for a sense of what becomes possible.

Step 3 – Build a portfolio that proves you can solve business problems

Hiring managers increasingly want proof that you can apply data tools to real business problems. 

That means building a data analyst portfolio.

The strongest portfolios are not built around generic datasets like Titanic survival predictions. They’re built around business problems that feel commercially real.

Good portfolio projects usually demonstrate:

  • A clear business problem
  • A structured analytical process
  • Appropriate tool selection
  • Actionable insight
  • Communication clarity

This is where Excel users often have a hidden advantage. You already understand how businesses operate. You know what stakeholders care about. You know what “good enough” looks like in a commercial environment.

Structured programmes can accelerate this process significantly. The LSE Data Analytics Career Accelerator culminates in a live Employer Project where learners solve real business challenges with organisations, including EdPlace and GAEA AI. 

Karen Munro, a Career Coach on the programme, described the transition this way:

“Coaching helps learners realise that they’re not starting from zero… helping them weave together previous experience with their new data skills.”

Emma Roberts’ mid-career shift from engineering to data analytics – and now to running her own analytics consultancy – shows what becomes possible when applied learning replaces theoretical study.

Step 4 – Position yourself for the role (not the restart)

One of the biggest mistakes career switchers make is positioning themselves as beginners. You are probably not a “junior analyst with no experience”.

You are more likely:

  • A marketer with analytics capability
  • An operations professional with automation skills
  • A finance lead with data visualisation capability
  • A commercial manager who can translate insight into revenue impact

That combination is extremely valuable.

As Luis Cantu explained during the webinar:

“We’re going to be measured not on how well we solve problems, but how much value we’re adding.”

Hiring managers increasingly want domain-fluent analysts – people who understand both the technical workflow and the commercial environment.

This changes how you position yourself:

  • Lead your CV with business outcomes, not tasks
  • Quantify impact wherever possible
  • Bring portfolio projects into interviews
  • Use existing professional networks
  • Reframe your experience as analytical, not administrative

This is also where coaching comes into play. Karen Munro described one of the biggest mindset shifts this way:

“Shifting from ‘I’m a student’ to ‘I’m a data professional who solves problems.’”

That reframing is often what unlocks the transition. And the outcomes can be significant. Career Accelerator graduates reported an average salary uplift of 31% after completion (FourthRev Completers’ Survey).

For a sense of what’s actually achievable in this transition, the real-world results of our LSE learners show what Career Accelerator alumni have gone on to do.

Make your move from Excel to analytics

The move from spreadsheets to analytics builds on the experience you already have.

The professionals best prepared for the future combine business understanding with modern analytics skills. 

The roadmap is straightforward:

  1. Diagnose the Excel ceiling
  2. Learn the modern analytics stack
  3. Build portfolio evidence
  4. Position yourself around business impact

The LSE Data Analytics Career Accelerator is built for exactly this transition. 

Over six months, learners build practical fluency in SQL, Python, Tableau, GenAI tools and data storytelling, culminating in a live Employer Project. Learners earn a Certificate of Competence from LSE and receive up to 12 months of 1:1 career coaching.

To learn more about the LSE Data Analytics Career Accelerator, download the programme brochure.

FAQs

Can I become a data analyst with just Excel?

Excel is an excellent foundation, but most modern analyst roles now also expect SQL, visualisation tools and some familiarity with Python or AI-enabled workflows.

How long does it take to move from Excel to data analytics?

That depends on your starting point and study intensity, but many professionals can build job-ready analytics capability within six to 12 months through structured, portfolio-led learning.

Do I need a degree to move into data analytics?

Not always. Many employers prioritise practical skills, portfolios and commercial experience. Structured programmes may also offer aptitude-based entry routes for experienced professionals without formal quantitative degrees.

Careers You Can Pursue With Data Analytics Skills

Posted on by FourthRev Team

Becoming skilled in data doesn’t mean you have to become a data analyst. Data analytics is one of the most portable skill sets in the modern job market. It applies across engineering, finance, agriculture, fashion, retail and marketing, and it’s fast becoming a baseline expectation for almost any senior tech role.

What has changed since we last wrote this article is who the market is paying, how much, and why. AI has moved from a niche capability to a default one. Employers now want analysts who can work alongside machine learning tools rather than being replaced by them. Salaries for data specialists remain some of the strongest in UK tech.

Below, we look at the current state of the data industry in 2026, the career paths you can actually take with a foundation in data analytics, and the salary ranges you can realistically expect at each stage.

The state of the data industry in 2026

The demand for data professionals continues to outpace the supply. According to the UK government’s Quantifying the UK Data Skills Gap report, UK businesses are recruiting for up to 234,000 data roles, with 48% of businesses actively hiring for data and 46% struggling to find the right people. Separate research reported by HR Review estimates that the UK economy loses roughly £57 billion a year because of the data skills gap.

Robert Walters’ Future of Data analysis continues to show that data analysts, data engineers and data scientists dominate UK tech hiring. In their most recent update, machine learning postings were up 27% month on month, with much of that growth concentrated on data engineers building the infrastructure needed to scale AI in production.

The sectors hiring hardest for data professionals are technology, media and telecoms (around 43% of AI and data roles), followed by financial services (37%). Insurance, retail, healthcare, energy and the public sector are all actively hiring too.

How AI is reshaping data careers

AI has not replaced data analysts. It has raised the bar for what one is expected to do.

Machine learning mentions in data analyst job postings have roughly doubled year on year, moving from around 7% of postings to 14%. The World Economic Forum’s Future of Jobs Report 2025 projects that 39% of core skills across all roles will change by 2030, with data analytics sitting near the top of that shift.

What that means in practice: routine data pulls, dashboard building and basic reporting are increasingly automated. The analysts who are best paid and hardest to hire in 2026 are the ones who can pair technical fluency (SQL, Python, cloud data tools) with commercial judgement, storytelling and the ability to work with AI as a force multiplier rather than a threat.

A term you’ll start seeing more of is the “augmented analyst,” meaning someone who uses AI to deliver insights faster and more accurately than a traditional analyst working alone. That is the archetype most employers are now hiring for.

The core skills that unlock every data career

Whether you want to be a business data analyst, a marketing analyst or a data engineer, the foundation is largely the same. Here’s what you’ll build on the LSE Data Analytics Career Accelerator:

  • Coding skills in the two most in-demand data programming languages: Python and R, applied to real-world business projects, not textbook problems.
  • The ability to speak the language of a business and turn data into stories that drive decisions.
  • Data visualisation with Tableau, including how to design reports that stakeholders actually read.
  • Ethical and regulatory frameworks for handling data, and how to identify algorithmic bias before it reaches production.
  • A working understanding of how AI and machine learning tools fit into an analyst’s workflow, so you can use them well.

Data analyst career paths in 2026

For data analysts, career growth is rarely linear. There are three general routes most people take once they have a solid foundation:

  1. From junior analyst upwards. Junior analyst to senior analyst, into leadership roles like head of data or chief data officer.
  2. Deep into advanced analytics. Data science, data engineering, data architecture, or machine learning.
  3. Data plus industry specialism. Analytics expertise applied to a specific field: finance, marketing, product, research, healthcare.

The roles below cover all three routes. Where salary ranges are given, they are UK figures cross-referenced from Glassdoor, Indeed, IT Jobs Watch and Robert Walters, valid as of mid-2026 and rounded to the nearest £1,000.

Business data analyst

What they do: A business data analyst investigates and analyses a business’s performance using data and statistical methods. They produce the insight reports that shape decisions on product, marketing, pricing and customer experience. This is different from a business analyst (who focuses on IT processes and org structures).

Most common skills and tools: SQL, Excel, Power BI or Tableau, Python, business KPIs, storytelling with data.

Qualifications and experience: A background in IT, business admin or business studies is helpful; strong applied skills matter more than a specific degree.

What they earn (UK, 2026):

Entry: £30,000–£38,000

Average: £47,000

Senior: £65,000–£78,000

Financial analyst

What they do: Analyse financial data, build models, produce forecasts and present findings to management. Increasingly, financial analysts are expected to blend traditional finance skills with data literacy: SQL, Python, and comfort with large datasets.

Most common skills and tools: Excel (advanced), SQL, Python, financial modelling, forecasting, valuation, Power BI.

Qualifications and experience: A degree in finance, economics or maths is typical, sometimes with a CFA or ACA in parallel.

What they earn (UK, 2026):

Entry: £32,000–£40,000

Average: £55,000

Senior: £75,000–£95,000

Logistics analyst

What they do: Collect, interpret and analyse data across supply chain, sourcing and distribution. Logistics analysts help predict and control operational performance, and are increasingly involved in AI-driven forecasting and route optimisation.

Most common skills and tools: SQL, Python or R, forecasting, supply chain KPIs, ERP systems (SAP, Oracle), Tableau.

Qualifications and experience: A background in logistics, supply chain or finance is useful but not essential; data skills and industry knowledge count for more.

What they earn (UK, 2026):

Entry: £28,000–£35,000

Average: £45,000

Senior: £60,000–£75,000

Market research analyst

What they do: Source and analyse market data, run experimental research and produce trend and attribution reports. At the senior end of the role, market research analysts use ML for demand forecasting and to identify emerging opportunities.

Most common skills and tools: SQL, Python or R, statistical analysis, survey design, attribution modelling, Tableau.

Qualifications and experience: A background in business, marketing or social sciences is common. Applied research experience matters.

What they earn (UK, 2026):

Entry: £27,000–£33,000

Average: £42,000

Senior: £58,000–£72,000

Marketing analyst

What they do: Use data to identify target audiences, track paid media performance, run pricing experiments and shape marketing strategy. Modern marketing analysts sit close to product and data teams, and are expected to work fluently with attribution modelling and increasingly with AI-driven creative and audience tools.

Most common skills and tools: SQL, GA4, Looker Studio, Google Ads / Meta Ads, marketing mix modelling, A/B testing.

Qualifications and experience: A marketing, business or analytics background. A quantitative bent goes a long way in this role.

What they earn (UK, 2026):

Entry: £28,000–£35,000

Average: £45,000

 Senior: £65,000–£85,000

Grow into a leadership role: If you want to combine data with digital strategy at the senior end, the LSE Digital Marketing Strategy & Analytics Career Accelerator is designed to move you into that kind of role.

Quantitative analyst

What they do: Known as “quants,” quantitative analysts build mathematical models to forecast changes in the value of financial instruments (equities, bonds, derivatives) and to manage risk. This is one of the highest-paid data-adjacent roles in the UK, particularly in London-based hedge funds and investment banks.

Most common skills and tools: Python, C++, R, stochastic modelling, statistical analysis, Bloomberg, KDB.

Qualifications and experience: Advanced maths, physics, statistics or financial engineering, often at PhD level. High tolerance for pressure.

What they earn (UK, 2026):

 Glassdoor London quant analyst data places senior quants in London well above £120,000, with top earners at hedge funds routinely clearing £150,000 to £200,000+ with bonuses.

Machine learning engineer

What they do: Design, build and deploy the models that let software learn from data and improve on its own. They own the pipeline from research to production, and are among the most in-demand hires in UK tech in 2026.

Most common skills and tools: Python, TensorFlow, PyTorch, MLOps, cloud (AWS/Azure/GCP), LLMs, statistics.

Qualifications and experience: A computer science, maths or data science degree, often with a strong portfolio of production ML work.

What they earn (UK, 2026):

Average: £76,000

Senior: £105,000–£166,000+

Build toward this: The LSE AI Leadership Career Accelerator and the Cambridge PACE Data Science Career Accelerator both give you the technical foundations and strategic AI context employers are actively hiring for.

Data scientist

What they do: Design data modelling processes, build algorithms, and find new ways to capture and analyse data. Moving from analyst to scientist typically means levelling up your programming skills, learning ML seriously, and getting comfortable with statistical modelling. Employers increasingly favour applied, portfolio-based training over purely academic routes.

Most common skills and tools: Python, R, SQL, machine learning, statistical modelling, Tableau or Power BI, cloud data platforms.

Qualifications and experience: A degree or credit-bearing qualification in data science, statistics or computer science, plus a working portfolio.

What they earn (UK, 2026):

•       Entry: £34,000–£41,000

•       Average: £54,000 (£50,000–£61,000 in London)

•       Senior: £77,000–£100,000+; lead roles routinely exceed £120,000

The right route in: The Cambridge PACE Data Science Career Accelerator is a seven-month applied programme featuring 20+ industry projects, designed to turn data skills into production-ready business impact.

Data architect

What they do: Design and manage a business’s overall data architecture: data flows, models, warehousing, and storage. They work closely with engineers, scientists and business analysts to make sure data can support the strategies leadership wants to run.

Most common skills and tools: SQL, cloud data warehouses (Snowflake, BigQuery, Redshift), ETL, Kafka, dbt, data governance frameworks.

Qualifications and experience: A background in business intelligence, application architecture or network management, usually with several years of hands-on data engineering behind it.

What they earn (UK, 2026):

•       Average: £85,000–£100,000

•       Senior: £120,000–£150,000+

Data engineer

What they do: Build and manage the data pipelines that everyone else in the business relies on. Ensure quality, security, scalability and compliance. This has become one of the most sought-after specialisms in 2026 as companies scale up their AI infrastructure.

Most common skills and tools: Python, SQL, Spark, Airflow, cloud (AWS/Azure/GCP), Kafka, data warehousing, big data frameworks.

Qualifications and experience: A background in maths, engineering, computer science or applied data. Hands-on experience with big data pipelines matters more than any single degree.

What they earn (UK, 2026):

Glassdoor UK data engineer salary reports an average of £62,571 nationally; IT Jobs Watch puts the median at £70,000, with London upper quartiles pushing £79,000+.

Senior: £95,000–£130,000+ in tech and financial services.

Analytics engineer (the new one to watch)

What they do: A hybrid role that has grown fast since 2024. Analytics engineers sit between the data engineers who build pipelines and the analysts who use the data. They model data, build reliable datasets, and make analytics self-serve for the wider business. If you enjoy both the code and the storytelling sides of data, this is a career worth watching.

Most common skills and tools: SQL, dbt, Snowflake or BigQuery, Python, Git, data modelling, BI tools.

What they earn (UK, 2026):

Average: £60,000–£80,000

Senior: £90,000–£110,000+

Frequently asked questions

Is data analytics a good career in 2026?

Yes. Data analytics remains one of the most in-demand skill sets in the UK job market, with hundreds of thousands of unfilled roles and strong salary progression. The role has changed since 2024, AI has automated the routine reporting side, so the best-paid analysts are those who can pair technical skills with commercial judgement and AI literacy.

How much does a data analyst earn in the UK in 2026?

The UK average data analyst salary is around £37,000 to £39,000 (Glassdoor and Indeed, June 2026). Entry-level roles start at roughly £24,000. In London the average rises to between £42,000 and £48,000, and senior data analysts can earn £61,000 or more. Specialist paths like data engineering and data science pay significantly more.

How do I become a data analyst in the UK?

Most people become a data analyst by combining a solid foundation in SQL, Python and data visualisation with a portfolio of real-world projects. A degree is helpful but not essential in 2026. Increasingly, employers hire on the strength of applied programmes and demonstrable work, especially ones backed by a recognised university.

What is the difference between a data analyst and a data scientist?

A data analyst investigates data to answer specific business questions, usually with SQL, Excel and BI tools. A data scientist builds statistical and machine learning models to predict future outcomes. Data scientists typically go deeper into programming and statistics, and are paid substantially more, but the routes into the field increasingly overlap.

Will AI replace data analysts?

No, but it’s changing what a data analyst does. Routine tasks like data pulls and standard dashboards are being automated. The analysts most in demand in 2026 are the ones using AI as a force multiplier: interpreting insights, communicating with stakeholders and shaping business decisions. Technical fluency plus commercial judgement is what employers now pay for.

Which career accelerator should I choose if I want a data career?

If you are starting out or moving into data, the LSE Data Analytics Career Accelerator is the right foundation. If you want to specialise in AI and machine learning, the LSE AI Leadership Career Accelerator or the Cambridge PACE Data Science Career Accelerator are stronger fits. This guide helps you decide.

Where to start

The data analytics field is one of the widest and longest-running career paths open to anyone entering tech in 2026. New specialisations keep emerging, from AI-native analyst roles to analytics engineering, and the demand for people who can work fluently with data shows no sign of slowing.

The LSE Data Analytics Career Accelerator is where most FourthRev learners begin. It covers Python, R, Tableau, statistical thinking and applied business projects, and you’ll work with a dedicated Career Coach to figure out which of the routes above suits you best.

Not sure whether now is the right time? Read our guide to why now is the best time to pursue a career in data, or see how Luis broke into sports analytics through the LSE Data Analytics Career Accelerator.

Why Now Is The Best Time To Pursue A Career in Data

Posted on by Summer White

The data industry has never been more accessible or more rewarding. Whether you are considering a career change into data analytics, exploring data science as a first career, or looking to upskill in a data-related role, the timing could not be better.

As data continues to become more important to businesses, organisations, and society as a whole, the need for data scientists, analysts, engineers, and other data-related roles has increased significantly.

The areas where data and analytics can be applied have expanded rapidly, and new tools and methods are continuously being developed to extract more complex and relevant information.

We look at the current state of data-related jobs, the demand, salaries, specialisations, and why now is the best time to pursue a career in data.

What you’ll learn in this guide

  • The current demand for data professionals and the global talent shortage
  • Five compelling reasons to become a data scientist or analyst
  • Real-world applications of data skills across industries
  • How to tell different data roles apart (analyst vs. scientist vs. engineer)
  • Data professional salaries in the UK
  • Career progression paths and how to get started
  • Key technical and business skills employers look for

The current demand for data professionals

In 2019 the World Economic Forum (WEF) called data the “new oil” of the global economy and data professionals the “talent that provides the ability to extract, refine, and deploy this new source of value.” Since then, data science has been listed among the best jobs based on salary, career opportunities, and job satisfaction.

A data career also has significant long-term growth and opportunity; the latest Future of Jobs Report 2023 found that the roles of data analysts and data scientists are among the 10 jobs expected to grow the fastest between 2023 and 2027.

Yet, there is a dire data talent shortage across the world. The UK government’s Quantifying the UK Data Skills Gap report found almost half of the businesses were recruiting for roles that require hard data skills (programming, analysis, data visualisation, machine learning (ML), data communication, knowledge of emerging technologies and solutions), but almost just as many struggled to recruit for these roles. At the time of this research in 2021, there were between 178,000 and 234,000 ads for people with hard data skills. 

Global hiring platforms continue to report steady year-on-year growth in demand for analytics, artificial intelligence and machine learning roles. Organisations across finance, healthcare, retail, logistics and government are putting data at the centre of how they operate and compete.
The shift is broad and sustained. For professionals entering the field today, that points to long-term career resilience rather than a passing trend.

According to the U.S. Bureau of Labor Statistics, employment for data scientists is projected to grow 35% from 2022 to 2032 –  significantly faster than the average for all occupations. In the UK, data-related job postings have grown by over 200% since 2015, with demand accelerating further as organisations invest in artificial intelligence (AI) and machine learning infrastructure. This makes a career in data analytics one of the most future-proof choices available today.

This industry snapshot reveals multiple reasons why you should pursue a career in data science and analytics. Now, let’s focus on the career itself and what it can offer you – both professionally and personally. 

5 Reasons to become a data scientist or analyst

We’ve listed our top five reasons why data science or analysis is a good career for you in 2024: 

  1. Social impact: You can positively impact your company or the planet by solving real-world problems through data. From predicting disease outbreaks to optimising renewable energy distribution, data professionals are at the forefront of solving humanity’s biggest challenges. Organisations like the United Nations and the World Health Organisation actively recruit data talent to support sustainable development goals.
  2. High salary: With a shortage of talent, businesses are willing to pay for the right people with the right skills. In the UK, entry-level data analysts can expect to earn between £38,000 and £53,000, while experienced data scientists and engineers regularly command salaries exceeding £80,000. In competitive markets like London, senior data roles can reach six figures, making it one of the highest-paying career paths in tech.
  3. Future-proofing: Data is an ever-evolving field, allowing you to stay at the forefront of industries, trends, and innovation. As AI and automation reshape the workplace, data literacy is becoming a baseline requirement across virtually every role. If you build your career in data science now, you will be well placed to lead the next wave of digital transformation.
  4. Business diversity: You aren’t siloed to a particular business or sector, almost every business is moving towards data-driven operations. Data professionals are in demand across industries from healthcare and fintech to entertainment and government, which means you can move between sectors over time without having to retrain from scratch.
  5. Global opportunity: Data skills are in demand worldwide, so you could work remotely on international projects or pursue opportunities in different countries.

Unique ways to put your data skills to use

To some, data may seem like a lot of numbers, systems, and processes. But these techniques are being applied to some of the most relevant and urgent challenges we face as a society. These are just some of the solutions you could work on:

  • If you’re passionate about fighting climate change, you could build climate models and weather prediction technology, helping to save lives affected by floods, drought, and other extreme weather phenomena. You could also work on cutting-edge public transportation projects that help reduce CO2 emissions.
  • If food security interests you, you could analyse crop yields and agricultural practices to help farmers increase their food output. 
  • If health is your domain, you could work on fighting cancer by using AI to develop new medicines and medical technologies or to identify patterns from imaging scans not easily detected by humans.
  • If good governance and development is your passion, you can use data to combat corruption, increase citizen engagement, or drive budget transparency and decision-making. In the developing world, it can be used to develop solutions to local problems around safety, women’s health, education, and economic empowerment.
  • If you’re interested in combating misinformation and preventing cyber hacks, you can use the formidable set of tools and techniques available to predict risks and prevent the spread of fake news.  
  • If you’re business-minded, you can help companies drive efficiencies, glean deep operational insights, and generate more revenue.

Untangling the different roles in data

The broad scope and overlap between roles have led to confusion around the tasks and responsibilities of data professionals. Recruiters often use the term ‘data analyst’ loosely, with some jobs advertising for ‘data unicorns’ – those rare people simultaneously skilled in statistics, analysis, data engineering, systems development, people management, interfacing with business and tech stakeholders, and developing and deploying algorithms… 

Looking for unicorns is unrealistic and has led to the Harvard Data Science Review (HDSR) and the Department for Digital, Culture, Media & Sport (DCMS) calling for the standardisation of the roles. Harvard suggested that roles be classified into three key role families: data analyst, data scientist, and data engineer. 

From here, more specialised or domain-specific roles can be established, e.g., ML and AI engineer, big data analyst or engineer, analytics and AI translators, data-oriented product managers, and insights interpreters.

For those who are just starting in their careers or transitioning into data from another field, a data analyst role gives you the core foundation and experience you need, before you decide to specialise. 

Data Analyst vs. Data Scientist vs. Data Engineer: The key differences

 

Data Analyst

Data Scientist

Data Engineer

Primary focus

Interpreting data to inform decisions

Building predictive models and algorithms

Designing data infrastructure and pipelines

Key tools

SQL, Excel, Tableau, Power BI

Python, R, TensorFlow, Jupyter

Spark, Hadoop, AWS, Airflow

Typical rntry path

Career Accelerator or bootcamp

Advanced degree or specialised programme

Software engineering background

UK salary range

£38k – £53k

£58k – £74k

£52k – £82k

Many of our learners on the LSE Data Analytics Career Accelerator started the programme with little-to-no data experience. Our Career Coaches play a key role in preparing our learners for their new careers, like plotting development plans and preparing for interviews.

How much can you earn as a data professional? 

Data science is one of the highest-paying jobs in tech right now. Data analysts tend to earn slightly less, and data engineers and specialists a little more. Those working as a consultant or who are paid project-to-project can command considerable fees. 

As you build experience and specialise, earning potential in data increases – particularly for professionals who combine technical skills with domain expertise like finance or healthcare.

Here are the average salaries across data roles in the UK: 

Data Role

UK Salary Range

Data Architect

£81,000 – £107,000

Data Engineer

£52,000 – £82,000

Data Scientist

£58,000 – £74,000

Data Analyst

£38,000 – £53,000

Machine Learning Engineer

£65,000 – £95,000

Head of Data / Chief Data Officer

£100,000 – £160,000+

Salaries vary significantly by location, with London-based data roles typically paying 15–25% more than equivalent positions elsewhere in the UK. Remote roles with international companies can also offer competitive compensation regardless of where you are based.

For more info on data roles, read our guide on how to become a data analyst and explore our new Career Accelerator in data science from the University of Cambridge Professional and Continuing Education (PACE).

Career track and progression

Generally, there are three main data paths to explore. You could choose the business analytics and intelligence route, where you’ll use data to make business decisions and help solve problems. 

Alternatively, you could focus on the technological theory of data science and specialise in algorithms and big data infrastructure. Or, you could go into data engineering and warehousing, designing and managing big data warehouses and optimising data collection, processing, and analysing. 

When it comes to career progression in data, you can start as an analyst and move up to become a senior or principal analyst, data scientist, or engineer, and eventually a chief data officer. Other options are in academic research and teaching or working as a data consultant.

Data expertise is not confined to analyst roles. It increasingly serves as a gateway to more strategic positions: analytics leadership, AI product management and digital transformation consulting. In these roles, technical fluency becomes a platform for influence. Data informs capital allocation, shapes product strategy and underpins executive decision-making.

For those who combine analytical depth with commercial judgement, the trajectory can extend well beyond execution into leadership.

Typical data career progression path

  • Entry Level (0–2 years): Junior Data Analyst → Data Analyst → Senior Data Analyst
  • Mid Level (3–5 years): Lead Analyst → Data Scientist → Data Engineer → ML Engineer
  • Senior Level (5–10 years): Principal Data Scientist → Head of Analytics → Director of Data
  • Executive Level (10+ years): VP of Data → Chief Data Officer → Chief Analytics Officer

How to start a career as a data professional

Breaking into data does not demand a traditional technical background. It does, however, require rigour: structured learning, applied practice and evidence of capability. A disciplined path tends to include five steps.

Learn the fundamentals

Build a working grasp of statistics, data literacy, spreadsheets and core programming concepts. Fluency in first principles underpins everything that follows.

Free resources such as Khan Academy (statistics), Google’s Data Analytics Certificate, and freeCodeCamp (Python basics) are excellent starting points. Dedicate at least 5–10 hours per week to build a consistent learning habit before moving to more advanced material.

Develop technical skills

Focus on tools such as SQL, Python, R, Power BI, and Tableau. SQL is the single most in-demand skill in data analyst job listings. Pair it with Python for data manipulation (using pandas and NumPy) and a visualisation tool like Tableau or Power BI to cover the core technical requirements of most entry-level data roles.

Build a credible portfolio

Produce end-to-end projects using real datasets. A strong portfolio signals readiness more convincingly than certificates alone. Host your projects on GitHub and create a simple portfolio website. Focus on 3–5 well-documented projects that demonstrate different skills: data cleaning, exploratory analysis, visualisation, and a predictive modelling project. Use publicly available datasets from Kaggle, the UK Data Service, or Google Dataset Search.

Gain practical experience 

Internships, freelance work, and employer-driven learning programmes help strengthen employability. Volunteering your data skills for non-profits through platforms like DataKind or Statistics Without Borders is another way to gain real-world experience. Many employers value this kind of applied, impact-driven project work alongside traditional internships.

Apply for entry-level roles

Junior analyst or reporting analyst roles are strong entry points into the field. When applying, tailor your CV to each role by mirroring the language used in the job description. Highlight specific tools (SQL, Python, Tableau) and quantifiable outcomes from your portfolio projects. Many hiring managers scan for keywords before reading in detail, so make your technical skills immediately visible.

Key Skills Employers Look for in Data Professionals

Technical skills

  • SQL and database querying
  • Python or R programming
  • Data visualisation tools
  • Excel modelling
  • Machine learning fundamentals
  • Cloud platforms (AWS, Google Cloud, Azure) –  increasingly required for mid-level and senior role
  • Version control with Git –  essential for collaborative data projects

Business skills

  • Problem-solving and critical thinking
  • Data storytelling
  • Understanding KPIs and business metrics
  • Stakeholder management

Soft skills

  • Communication
  • Collaboration
  • Strategic thinking
  • Intellectual curiosity –  the best data professionals constantly ask “why?” and explore data beyond the brief
  • Adaptability –  tools and technologies evolve rapidly. Showing a willingness to learn continuously is critical.

As a data professional, you’ll be navigating technical problems that need strategic thinking and collaborative problem-solving. Because it’s a highly practical job that has the potential to truly impact a business, employers will want proof that you’re capable of doing the work. 

Our data-focused Career Accelerators give you the university recognition and practical experience that employers are looking for. While each programme is unique, they all focus on applying the skills to practical projects. 

In the end, you walk away with a portfolio of work and a globally-recognised university certificate that assures employers you’re ready for a new career. 

Frequently Asked Questions About Careers in Data

Is a career in data analytics a good choice?
Yes. Data careers offer strong salary growth, long-term job security, and opportunities across nearly every industry. Data analytics has been ranked among the top 10 most in-demand careers globally for four consecutive years. With organisations investing billions in data infrastructure, the demand for skilled analysts is expected to remain strong well into the 2030s.

Do you need a degree to become a data analyst?
Many employers prioritise practical skills and portfolio experience over traditional degrees. Programmes like the LSE Data Analytics Career Accelerator are designed to give career changers the practical skills and recognised credentials to break into data without a traditional computer science degree. Employers increasingly value demonstrated ability over academic background.

How long does it take to transition into a data career?
Many professionals become job-ready within 6–12 months with structured learning. The timeline depends on your starting point and time commitment. Those with some analytical background (e.g. finance, marketing, research) can often transition faster, while complete beginners typically need 9–12 months of focused study and portfolio-building.

Which industries hire data professionals?
Finance, healthcare, retail, logistics, technology, and government sectors all employ data specialists.

What is the difference between data science and data analytics?

Data analytics focuses on examining existing data to identify trends and inform decisions, typically using tools like SQL, Excel, and Tableau. Data science goes further by building predictive models and algorithms using programming languages like Python and R. Both are valuable career paths, but data science generally requires more advanced statistical and machine learning knowledge.

Why data careers are growing faster than ever

The global economy is now decisively data-driven. Organisations are investing heavily in artificial intelligence, predictive analytics and automation to sharpen decisions and stay competitive. As a result, professionals who can interpret, manage and operationalise data are no longer niche specialists – they are central to performance across sectors.

Entering the field today places you at the heart of this shift. Demand for applied analytics capability continues to grow, offering durable career prospects, strong earning potential and the opportunity to tackle complex commercial and societal challenges with evidence rather than instinct.

Whether you are looking to become a data analyst in the UK, transition into data science from another career, or simply future-proof your skill set, there has never been a better time to start. The combination of high demand, competitive salaries, flexible working, and meaningful impact makes a career in data one of the smartest professional investments you can make in 2024 and beyond.

If you’re interested in transitioning or advancing your career in data analytics, explore the LSE Data Analytics Career Accelerator. 

To learn advanced data science skills and tools, such as machine learning concepts, deep learning, and NLP, download the brochure for the Data Science Career Accelerator.

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

Posted on by Marisa Crous

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.

How Luis Broke Into Sports Analytics Through the LSE Data Analytics Career Accelerator 

Posted on by Marisa Crous

Luis Cantu is now a Data and Insights Analyst at Leeds United Football Club – a role he moved into from independent commercial consulting, with no prior background in data analytics.

The six-month LSE Data Analytics Career Accelerator gave him the credibility and problem-solving mindset to help him make that shift. 

“I was looking for a strong credential, since I was moving into an area where it was not only completely new to me, but where I didn’t have experience doing data analytics.”

In a recent Ask Me Anything session, he shared the story behind his career transition, offering a candid look at the challenges and decisions that shaped his journey.

From commercial experience to a career crossroads

Luis began his career in commercial strategy and business development, working with small and medium-sized enterprises across the UK. With more than 10 years of experience, he had built a strong foundation in understanding how businesses grow and generate revenue.

But as an independent consultant, he reached a point of uncertainty. He wanted to move into a full-time role while also gaining an edge in a changing job market.

At the same time, he recognised a challenge: moving into a new field like data analytics meant starting without direct experience. That created a credibility gap he needed to close.

He explored several options, from data science to user experience, but data analytics stood out. It offered a way to combine his commercial background with a technical skill set that employers increasingly value.

Luis needed structured learning with real application built in – not just new tools, but the chance to prove he could use them.

The turning point: Learning to think like a problem solver

Enrolling in the LSE Data Analytics Career Accelerator marked a shift in how Luis approached his career.

While the programme introduced new tools like Tableau, Python and SQL, what stood out most was the focus on application and thinking.

He learned how tools work and how to apply them to solve real business problems. The programme encouraged reflection at every stage, helping him connect new skills to real-world contexts.

“The course is structured for you to reflect along the journey. What I’m learning here is just the first step towards learning how to use tools that I can leverage as part of my career.” 

He began to see himself as a problem solver, noting that employers value people who deliver outcomes beyond dashboards and code. 

“What people are going to be looking for is what problems you can solve…” 

That mindset was reinforced throughout the programme, especially during the Employer Project. Working on a live business challenge for six weeks, Luis had to apply everything he had learned under real-world constraints and expectations.

At one point, his team was asked to automate a key part of their solution with less than a week to deliver. Taking ownership of the task, he worked through the problem, found a solution and delivered results that impressed both the employer and his peers. 

“I made something valuable out of everything that I learned through the programme.” 

That experience proved to him that he could take on unfamiliar, complex challenges and deliver meaningful outcomes.

The outcome: A new role in sports analytics

After completing the programme, Luis successfully transitioned into a new industry. He secured a role as a Data and Insights Analyst at Leeds United Football Club, moving into the highly competitive world of sports analytics.

In his current role, he is responsible for helping the club make better commercial decisions through data.

His work includes building the data infrastructure needed to support decision making, identifying the right tools for different teams and analysing fan behaviour to drive revenue.

He described his role in simple terms: the objective is to generate value. 

For example, by analysing customer data, Luis can identify which fans are most likely to purchase different types of tickets, from general admission to premium hospitality packages. These insights help the club target the right audiences and improve commercial performance.

This is where his combined skill set makes a difference. Luis’ background in commercial strategy allows him to understand business goals, while his data skills enable him to uncover the insights needed to achieve them.

The LSE credential also played an important role in his transition.

“LSE is a strong brand, and brands still matter…It doesn’t guarantee you an interview, but it is a positive.” 

In a competitive job market, that signal helped him stand out.

Luis’s outcome reflects a broader trend among learners. According to the FourthRev Completers’ Survey, 87.5% of graduates achieved a desired career goal within six months of completing their Career Accelerator. 

More than skills: Support, confidence and community

Luis’s journey combined technical learning with structured support. 

Throughout the programme, he worked with a Success Manager, a Career Coach and Course Facilitators. He also collaborated with a global cohort of peers.

This combination created an environment where he could ask questions, test ideas, and stay motivated.

“I didn’t really feel alone at any point. You do feel that you have someone you can just send a message to.” 

Career coaching was particularly valuable. Luis’ coach challenged him to think critically about his goals, positioning, and strategy. 

When interviews did not go as planned, they worked together to analyse what happened and improve his approach.

This process helped him build not just skills, but resilience and confidence.

A new mindset for a changing world

One of the most important lessons Luis took from the programme is how to approach problems in a modern, data-driven environment.

He sees tools like AI as part of a broader toolkit that supports critical thinking. 

For him, the real value lies in asking the right questions, testing ideas, and interpreting results.

“If you go for this programme, think of it that way, that it’s going to make you a more attractive person in terms of how capable you are at dealing with challenges that come your way.” 

His advice: Invest in becoming a problem solver

For anyone considering a similar path, Luis’s advice is clear: focus on what tools enable you to do. 

“Think of it as an investment…not just in getting new skills, but building your brand towards becoming someone that can solve problems.” 

That perspective is what ultimately enabled his career transition.

Luis developed data analytics skills, along with the ability to think, act, and communicate like someone who can deliver data results. 

Ready to take the next step?

Luis’s story shows what’s possible when you combine experience with the right learning environment and support.

The LSE Data Analytics Career Accelerator is designed to help you build practical technical skills, create a portfolio of real-world work, and develop the confidence to move forward in your career. If you’re ready to position yourself as a problem solver in today’s data-driven world, download the programme brochure to learn more.

Emma’s Shift into Data Analytics: From Engineering to Solving Business Problems with Data

Posted on by Marisa Crous

Emma Roberts did not set out to start a data consultancy. Nor did she expect a mid-career shift would take her from chemical engineering into running her own analytics business. Yet that is exactly where she is today.

Two years after completing the LSE Data Analytics Online Career Accelerator, an LSE online programme developed in collaboration with FourthRev, Emma now leads Nimble Data, helping small businesses and manufacturers modernise their data platforms through analytics, automation and AI.

In a recent Ask Me Anything webinar, she shared what the transition really involved.

A strong career and a growing question

On paper, Emma’s career followed a clear trajectory.

With a Master’s in Engineering, she joined BP through a graduate scheme, became a Chartered Chemical Engineer and later moved to ExxonMobil. Over 15 years, she worked across both manufacturing and office-based roles, some deeply technical, others less so.

Over time, however, something changed.

She found herself missing hands-on problem-solving and working directly with data. As the industry evolved, a more fundamental question surfaced. Did she want to continue on the same path long term?

Working with a career coach helped clarify her thinking. She recognised two consistent motivations: 1) a preference for data-driven decision-making and 2) a desire to help organisations improve performance.

Data analytics offered a way to bring both together without abandoning the experience she had built.

Choosing structure over going it alone

Emma first explored internal opportunities, but when those did not materialise, she assessed her options.

A second degree felt too time-intensive and costly. Self-study, while flexible, lacked structure and accountability.

Instead, she chose a middle path. A structured, part-time programme with clear milestones and support. The LSE Data Analytics Online Career Accelerator runs over six months, part-time and online, building capability across Excel, SQL, Tableau, Python and R before culminating in a live Employer Project.

For Emma, the deciding factor was not just the curriculum, but the support model.

“I was acutely aware I was changing careers mid-way through my working life and wanted support navigating that transition.”

That combination, subject facilitation, a Success Manager and dedicated career coaching, is built into the programme. For those making a career pivot, it can be the difference between starting and following through.

Building confidence through practical work

Emma was candid about her initial concerns.

“University was a long time ago, and my only previous coding experience was a bit of MATLAB during my degree. SQL and Python felt unknown and intimidating.”

What she encountered, however, was a learning experience grounded in application rather than theory.

“It’s very focused on the approach to solving a data problem and equips you with a whole bunch of tools that you can then use to go and find out a way of doing a certain thing or solving a problem later.”

Projects were designed to reflect real business scenarios. The emphasis was not just on learning tools, but on thinking like an analyst.

This came together in the Employer Project.

“And then you have the Employer Project where you work in a team to deliver something, and that’s quite intensive, and it’s the big finale of the course, where you get to bring everything together, there’s not really any rules, you get a pretty generic statement of please go and solve this business problem for someone.”

The ambiguity mirrored real-world work, requiring learners to engage with stakeholders and define solutions.

“It’s very real-world based, you have interactions with the employer to work out what it is you want to do.”

For Emma, this was critical. It meant she was not just learning concepts. She was practising the kind of work she would later deliver professionally.

Reframing experience, not replacing it

A common concern for mid-career professionals is whether prior experience still holds value.

In Emma’s case, it did, just in a different way.

Her background in manufacturing, operations and stakeholder environments became a strength when combined with analytics capability. She could not only build models but also interpret them within real operational contexts.

The programme helped formalise that shift. Structuring analysis, communicating insights clearly and selecting appropriate tools for different business questions.

Even now, she continues to revisit the material.

“Nora, who was our lead facilitator, her weekly lecture material was just perfect, it’s still my Bible, I go back to it most weeks.”

Breaking into the field

Securing her first opportunity in data was not immediate.

“Breaking into data at a mid-career level without 10 years of Python, AWS, Azure, etc., and a computer science background turned out to be very tricky.”

Positioning became key. Emma focused on clearly communicating her hybrid skill set and maintaining visibility, particularly on LinkedIn.

“One tip for anyone considering a career switch: keep your LinkedIn up to date. I wouldn’t be where I am now if I hadn’t.”

Her breakthrough came through a conversation with a potential client looking specifically for someone with both engineering and data expertise.

One project led to another, and eventually to founding her own consultancy.

Applying analytics in practice

Today, through Nimble Data, Emma supports organisations in modernising how they use data.

“It’s been incredible, it’s been life-changing for me.”

Her approach reflects the structure developed during the programme. Define the problem, analyse the data, test assumptions and communicate outcomes clearly.

“I love it, I love what I do, I’ve now got a really flexible career.”

Support that sustains momentum

Emma credits the programme’s support structure with helping her maintain progress during more challenging periods.

“Success managers are your personal cheerleaders and they check in with you that everything’s okay, they give you little pep talks and make sure you don’t miss deadlines and reminders.”

Balancing part-time study with work can be demanding. Structured milestones, accountability and coaching help reduce the risk of losing momentum.

The programme includes up to 12 months of career coaching, with an extension available if a qualifying career milestone has not yet been reached. This is not a job placement guarantee, but a framework designed to support continued progress.

Looking back

Two years on, Emma’s reflection is measured and realistic.

“Is every day perfect? No, it’s still work. But it’s meaningful, mentally stimulating, and aligned with what I care about.”

Her advice for others considering a similar move:

  • Be honest about what you want to change and how you work.
  • Do not underestimate your transferable skills.
  • Expect moments of doubt. They are normal, especially when you’re doing new and demanding things.

For Emma, the transition into analytics was not about starting again. It was about applying what she already knew in a new and more aligned way.

Interested in exploring the curriculum, structure and support model? Download the programme brochure to learn more.

10 In-demand Tech Careers for 2026

Posted on by FourthRev Team

Tech Nation reports the UK tech sector reached a combined market valuation of $1.2 trillion in 2025, cementing its place as the number one tech ecosystem in Europe.

But the roles powering that growth are shifting fast: AI is now woven into everyday workflows, and employers increasingly expect role-relevant AI literacy alongside core technical skills. 

In this guide to the top 10 most in-demand tech careers for 2026, we’ve analysed data and pulled together the roles that consistently show up across hiring and skills-gap insights, plus the skills that make candidates stand out. Salary ranges are UK-wide and will vary by location, industry and seniority.

10 in-demand tech careers

  1. Artificial Intelligence (AI) and machine learning engineers
  2. Data analysts and scientists
  3. Cybersecurity analyst
  4. Cloud engineers
  5. DevOps engineers
  6. Full-stack developers
  7. Blockchain engineers
  8. UX/UI product designers
  9. Product managers
  10. Network and systems administrators

Become the most sought-after talent

This list highlights careers across AI, data, development, cloud computing, machine learning, cybersecurity, blockchain, UX design and product management, roles that are integral to the UK’s digital economy. We also considered the availability of roles, core job functions, job satisfaction and annual earning potential.*

*Tech salaries remain competitive, with wage growth outpacing other sectors in 2024. Addison Group’s 2026 Workforce Planning Guide predicts tech salaries to jump 8-10% this year, outpacing expected inflation of 2.6%. While cities like London often offer higher average salaries, remote work continues to broaden access to opportunities across the country. 

The skills that show up across most tech job descriptions 

  • AI literacy for your role: understanding where AI helps, how to evaluate outputs and how to work responsibly with AI-enabled tools
  • Cloud fundamentals: identity and access management basics, networking foundations, and cost awareness
  • Security basics: secure-by-design habits, risk awareness and good data handling
  • Product thinking: user needs, prioritisation and measurement
  • Delivery habits: version control, documentation and clear stakeholder communication

How to choose the right tech career path

  • If you enjoy building models and working with complex data, explore AI and machine learning engineering or data science.
  • If you like finding patterns and influencing decisions, data analytics can be a strong fit.
  • If you’re motivated by protecting organisations and reducing risk, cybersecurity can offer purpose and stability.
  • If you enjoy systems, reliability and scale, cloud engineering, DevOps or network and systems administration may suit you.
  • If you like shaping what gets built and why, product management can be a strong pathway.

If you like designing experiences and improving usability, UX/UI product design can be a great fit.

Top tech roles for 2026

1. Artificial Intelligence (AI) and machine learning engineers

Demand: AI and machine learning are driving demand for engineers who can develop and improve algorithms and models. These professionals contribute to automation, forecasting and decision support.

Typical responsibilities: Building and testing models, improving performance, working with data pipelines and collaborating with product and engineering teams to deploy solutions.

Skills to build (and tools employers mention): Python, machine learning fundamentals, model evaluation, experimentation discipline and the ability to communicate trade-offs.

Job satisfaction: This role often involves high-impact problem-solving and continuous learning.

Salary: Starting salaries are around £45,000 per annum, with experienced professionals earning up to £120,000. 

If you’re exploring this path, there are two strong routes depending on your goals:

If you want to lead AI strategy and adoption (and make confident decisions about where AI creates value), the LSE AI Leadership Career Accelerator is designed for senior leaders and transformation-focused professionals. 

If you want to build hands-on technical capability in data science and machine learning, the Cambridge PACE Data Science Career Accelerator can help you develop advanced skills and apply them in a six-week live business project designed and facilitated by industry leaders (for example, the Bank of England).

What makes this programme distinctive is its focus on commercially minded data scientists who can use advanced data science, AI and machine learning skills to solve business challenges. You’ll get to practise these skills in a six-week live business project that’s designed and facilitated by industry leaders, such as the Bank of England. 

2. Data analysts and data science engineers

Demand: It is no secret that AI is changing the way companies are hiring in 2025 and beyond, but AI data scientists are being hired at a rapid rate. Data professionals analyse large data sets to uncover patterns and insights that inform decisions. Data analysts and data scientists often differ in methods and depth of modelling, but both remain in extremely high demand as organisations invest in data-driven strategy.

Typical responsibilities: Cleaning and analysing data, building dashboards, answering business questions and communicating insights to stakeholders.

Skills to build (and tools employers mention): SQL, Python, Tableau, statistics, data storytelling and stakeholder communication.

Job satisfaction: These roles can be rewarding for people who enjoy structured problem solving and measurable impact.

The growing importance of data in decision-making processes underscores the indispensable role of data scientists and analysts in today’s business landscape.

Salary: Data analyst salaries in the UK typically range from approximately £30,000 to £60,000 per year. Data scientists can earn between £45,000 and £85,000 annually. 

If you’re looking to fast-track your path to a data analyst career, the LSE Data Analytics Career Accelerator can help you build practical capability in tools like SQL, Python and Tableau, alongside career support.

Read this blog post to see what career results look like for LSE Career Accelerator learners (and potentially you, too). 

If you’re looking for practical guidance on how to move into a data career (from those who’ve already done it) watch the webinar below:

3. Cybersecurity analyst

Demand: Cyber-attacks remain increasingly common, and many organisations need people who can help identify risk, respond to incidents and improve security practices.

Typical responsibilities: Monitoring for threats, supporting incident response, assessing vulnerabilities and helping teams improve security processes.

Skills to build (and tools employers mention): security fundamentals, risk thinking, incident response basics, clear documentation and calm decision-making.

Job satisfaction: Many analysts value the sense of purpose and the stability that comes with protecting organisations.

Salary: In the UK, cybersecurity analysts earn an average annual salary of £45,000, with entry-level positions starting around £40,000 per year and experienced professionals earning up to £62,500. 

4. Cloud engineers

Demand: Cloud adoption continues to expand, and organisations need engineers who can build, maintain and optimise cloud infrastructure. 

Typical responsibilities: Designing and maintaining cloud environments, improving reliability, supporting deployments and managing access and cost.

Skills to build (and tools employers mention): cloud fundamentals, IAM basics, networking foundations, reliability thinking and cost awareness.

Job satisfaction: Cloud engineering can suit people who enjoy systems thinking and continuous improvement.

Salary: In the UK, cloud engineers earn an average salary of £54,286 per year, with entry-level positions starting around £30,000 and experienced professionals earning up to £144,552 annually. 

The growing reliance on cloud technologies highlights the essential role of cloud engineers in modern business operations.

5. DevOps

Demand: DevOps can include administrators, engineers, consultants and system engineers as well as platform specifc software specialists. Those in DevOps help teams deliver software more reliably by improving workflows between development and operations. The demand for DevOps professionals is experiencing significant, sustained growth, driven by the fact that roughly 74% to 80% of organisations worldwide have adopted DevOps practices.

Typical responsibilities: Supporting CI/CD, improving deployment processes, monitoring reliability and collaborating across teams.

Skills to build (and tools employers mention): automation mindset, scripting basics, reliability practices and strong communication.

Job satisfaction: Many DevOps engineers enjoy the blend of technical work and cross-team collaboration.

Salary: The median salaries for DevOps system engineers in the UK typically begin at £55,000 per annum, with experienced lead professionals earning from £85,000 to £120,000. 

6. Full-stack developers

Demand: Full-stack developers remain valuable because they can contribute across front-end and back-end development.

Typical responsibilities: Building features end-to-end, integrating APIs, improving performance and working with designers and product teams.

Skills to build (and tools employers mention): front-end fundamentals, back-end basics, APIs, testing habits and strong debugging.

Job satisfaction: This role can suit people who enjoy variety and building tangible products.

Salary: Salaries range from an average base salary of £55,00 to £87,000 annually, depending on experience and location. 

7. Blockchain engineers

Demand: Blockchain work remains specialised. Where organisations adopt decentralised systems, they need engineers who can build secure, reliable implementations. 

Typical responsibilities: Designing and implementing decentralised components, focusing on security, performance and reliability.

Skills to build (and tools employers mention): secure engineering habits, careful testing, systems thinking and an ability to communicate risk.

Job satisfaction: Many engineers value the complexity of the problems and the opportunity to work on emerging systems.

Salary: Entry-level positions start at £65,000 per annum, with experienced professionals earning up to £90,000. 

8. UX/UI Product Designers

Demand: As organisations prioritise customer experience, UX and UI designers play a key role in creating usable, accessible products. According to the US Bureau of Labor Statistics, the UI/UX job market is growing with an expected growth rate of 4.7%, and the employment rate is expected to rise by 7% for digital designers in 2024-2034, which is deemed much faster than average.

Typical responsibilities: Researching user needs, designing flows and interfaces, prototyping and iterating with product and engineering.

Skills to build (and tools employers mention): user research, interaction design, prototyping, accessibility awareness and portfolio quality.

Job satisfaction: Many designers enjoy the creative, human-centred nature of the work and the ability to influence product outcomes.

Salary: Salaries for UX/UI designers start at £36,000 per annum, with experienced professionals earning up to £71,000. 

For those entering or pivoting into product design, portfolio development and real-world experience can be key. The King’s UX & UI Product Design Career Accelerator supports you in building both through applied learning and a six-week business project set by top UX/UI companies like Creative Navy.

Siobhán was already working as a UX designer when she realised she needed a broader understanding of UX processes to advance her career. Within two months of starting the King’s Career Accelerator, she was promoted to a lead role. Read her story.

9. Product Managers

Demand: Product managers help teams build the right thing at the right time by aligning user needs with business goals.

Typical responsibilities: Discovery, prioritisation, roadmap planning, stakeholder alignment and supporting delivery.

Skills to build (and tools employers mention): structured thinking, communication, prioritisation, measurement and comfort with ambiguity.

Job satisfaction: Many product managers value the strategic involvement and cross-functional collaboration.

Salary: Starting salaries are around £45,000 per annum. There are many titles within product managament and its important to apply for the right type of product management job. The median Lead product manager salary in the UK is £77,000 per year, with experienced professionals in the Digital product management space earning up to £80,000. If you’re looking to build product capability with practical application, the King’s Product Management Career Accelerator offers a structured pathway with applied learning and industry exposure.

10. Network and Systems Administrators

Demand: Over 80% of UK organisations now rely on a mix of cloud and on-premise systems, sustaining strong demand for infrastructure and reliability specialists. Site reliability engineers are especially valued for ensuring uptime, stability and performance in increasingly digital workplaces.

Typical responsibilities: Maintaining systems, troubleshooting issues, supporting users and improving reliability.

Skills to build (and tools employers mention): infrastructure fundamentals, troubleshooting, documentation and a calm approach under pressure.

Job satisfaction: Moderate to high. The stability and critical importance of this role make it a secure and rewarding career choice.

Salary: On average, network administrators in the UK earn around £34,000, while network systems administrators can earn upwards of £52,000. Site reliability engineers have a median salary of about £74,000.

Accelerate your tech career

As the tech sector continues to grow, the demand for skilled professionals remains high. However, the digital skills gap persists.

The UK is already experiencing a growing data skills crisis, with 63% of companies investing in AI technology claiming they have a shortage in the skills required for implementation. This issue will grow as more companies are already set to further invest in AI technology within the next two years.

Our research shows that without access to the right quality of talent, businesses are unlikely to achieve their key strategic deliverables. We found that 54% struggle to find good candidates for entry-level digital economy roles, and 53% said they’d pay whatever it takes to acquire the digital skills they need.

To address this, we’ve partnered with leading universities and top tech companies to deliver Career Accelerators, online programmes designed to equip you with in-demand skills, hands-on experience and career support.

These programmes help bridge the gap between industry needs and talent, supporting learners into high-growth roles across data, AI, product management and UX/UI product design. Learn more about Career Accelerators and start your journey into the tech industry today.

Frequently asked questions

Which tech career is most in demand in the UK in 2025?

Demand varies by sector, but roles across AI, data, cloud and cybersecurity continue to feature strongly in hiring and skills-gap reporting.

What’s the best tech career for career changers?

Many career changers start in data analytics, UX/UI product design or entry-level cloud and security pathways, depending on their strengths and the kind of work they enjoy.

Do I need a degree to work in tech in the UK?

Not always. Many roles value demonstrable skills, projects and practical experience. Requirements vary by employer and role.

What’s the difference between a data analyst and a data scientist?

Data analysts typically focus on answering business questions through analysis and reporting, while data scientists often work more deeply with modelling and advanced statistical or machine learning methods.

Is AI replacing tech jobs?

AI is changing how work gets done. In many roles, it can improve productivity and shift which tasks matter most, which is why building role-relevant AI literacy is increasingly useful.

Which tech careers pay the most in the UK?

Compensation depends on seniority and location, but roles in AI/ML, cloud and specialised engineering areas can command higher salaries.

How an LSE Data Analytics Career Accelerator Alumnus Advanced His Career at Adidas

Posted on by Marisa Crous

When Jip Sanders joined Adidas, he was driven by curiosity and a desire to improve how things worked. In his role managing product launches across European marketplaces such as Zalando and ASOS, he made sure each release went live smoothly. Yet even with his growing exposure to data, he sensed a gap.

I felt like there was something missing, that I didn’t have the fundamental skills to really show that I can do this, and for that I needed to have these data analytic skills….”

He was seeking a structured approach to learning – one that combined academic credibility with practical, hands-on experience. That search led him to the LSE Data Analytics Career Accelerator, developed in collaboration with FourthRev.

Over six months, Jip developed the technical fluency and professional confidence to move into Digital Sales – Product Launch & Optimisation at Adidas. Today, he leads automation projects that simplify data workflows across European marketplaces and contribute to smarter, faster launches. Jip shared his Career Accelerator experience with us during a live Ask Me Anything event.

Building data confidence and collaboration

When he started the Career Accelerator, Jip quickly saw how well it fit around his work. It was designed for professionals like him, people eager to grow while managing full-time careers.

You start off the programme learning about Microsoft Excel. You really learn about what data is, what the different types are, how to read each type of data, and you start at the very first stepping stone and week by week you grow into an individual that understands it.”

For Jip, the Employer Project was the most rewarding part. It brought everything together: technical skill, collaboration and real-world impact.

What I’m most proud of is definitely wrapping up the Employer Project at the end. This is the fourth module, and in this you work together with a group of fellow students… we wrapped up the presentation with a super good feeling. We jumped on a group call, and we celebrated together…”


Working with peers from Amsterdam, the UK, and the UAE on a live brief – forecasting ice cube sales in Spain – showed him how powerful shared problem-solving can be.

To learn how to work as a group is something that was very unique to me, and I think that’s also one of the best qualities of this programme, is that you learn how to work as a team on a data-oriented project.”

The experience mirrored real industry life, where collaboration and communication matter as much as technical expertise. It also gave him a clearer sense of how to use analytics to tell stories and drive decisions.

Career progression and credibility

After completing the programme, Jip’s progress was soon recognised internally. His stronger grasp of Python and SQL, paired with his ability to translate data into business solutions, opened doors to new responsibilities at Adidas.

It’s definitely given me more autonomy to lead on advanced automation projects.”

He soon stepped into Digital Sales – Product Launch & Optimisation, leading automation initiatives that improve how product data flows through Adidas’ European systems.

The credibility of the Certificate of Competence from LSE also made an impression.

When I tell my colleagues that I did a programme at the LSE, they’re like ‘Wow, no way, it’s such a prestigious university.’”

Jip’s story reflects a wider Career Accelerator trend: 87.5% of learners achieved a desired career goal within six months of completing a Career Accelerator (FourthRev 2023/24 Completers’ Survey).

Mindset, support, and advice

Beyond career growth, Jip’s perspective on data changed completely while on the programme. He no longer sees it as a technical process, but as a creative tool for solving problems and shaping ideas.

What this programme really teaches you is that data is not just ones and zeros. There’s a lot you can do with it, and you can really shape it in a certain way, and it can be quite magical... Without this programme, I wouldn’t have been taught that, and I would have put limits on what I believed was actually capable.”

This new mindset has shaped both his work at Adidas and his personal projects. He has built Python scripts to optimise workflows. He continues to explore ways AI can enhance automation and data visualisation tasks — skills also reinforced by the programme’s dedicated AI & Data Analytics Learning Track.

Jip credits the programme’s support system as one of its greatest strengths, saying: “The support system can only be described as excellent. I think you guys are always on standby, ready to answer questions.”

Advice for future learners

Jip acknowledges that starting a new programme alongside work can feel daunting, but he urges others to see it as an investment in their own potential.

I would recommend this to anyone who wishes to shake things up in the world. If you want to learn how to use data and use it to your advantage, or how to change up the ways your organisation works, then this is something that will totally help you.”

Jip’s Accelerator journey is a reminder that data confidence can transform not only a career, but how you think, collaborate, and lead.

Like Jip, you can gain industry-ready skills and an LSE credential that sets you apart. The LSE Data Analytics Career Accelerator, developed in collaboration with FourthRev, equips you to lead with data in any industry – from retail to finance, healthcare to tech.

Ready to take the next step in your data career? Download the programme brochure to see the curriculum, tools and next cohort details.

How to Unlock New Career Opportunities in 2025 With Practical Data Analytics Skills | LSE Data Analytics Career Accelerator

Posted on by FourthRev Team

In a data-driven economy, career growth depends on more than job titles. Employers are prioritising professionals who can navigate data fluently, interpret patterns confidently, and communicate their insights clearly across teams, tools, and time zones.

This shift formed the focus of a recent expert panel hosted by the London School of Economics and Political Science (LSE) and FourthRev: How to Unlock New Career Opportunities in 2025 With Practical Data Analytics Skills.

This blog captures the most relevant, practical insights from the panel — featuring LSE faculty, a LSE Data Analytics Career Accelerator alumna, and a data leader — and explores how applied analytics training can act as a lever for career transformation.

You can watch the full webinar recording below: 

The career outlook in 2025: Demand, urgency and upside

Data roles have grown nine times faster than the average job market. But what’s more telling is how central they’ve become to business strategy, no matter the sector. Organisations aren’t hiring for isolated technical roles. They’re looking for professionals who can think critically, communicate clearly, and contribute to company-wide transformation through data.

Much of this demand is being accelerated by the integration of AI, which has pushed data skills from desirable to essential. As businesses adopt new tools and automate routine tasks, they need professionals who can not only work with AI but also interpret the data it relies on and produces. 

Kate McDermott, Associate Director at Omnis Partners, explained how this shift is influencing hiring across sectors during the event:

“AI is driving a huge amount of the hiring that’s going on at the moment…Candidates need to be ready to start to think about how AI will impact their roles, particularly in the analytics space. It will be an enablement — for greater efficiency, for greater impact and greater performance overall.”

— Kate McDermott, Associate Director at Omnis Partners

Across the board, data literacy is no longer a siloed skill — it’s a core business capability. As organisations restructure their teams and workflows to adapt to new AI tools and larger datasets, they need candidates who can ask the right questions, extract insight from ambiguity, and move quickly from analysis to decision.

Dr James Abdey, Associate Professorial Lecturer in Statistics at LSE and Programme Coordinator of the LSE Data Analytics Career Accelerator, captured this shift succinctly:

“Data is like crude oil. In and of itself, it’s of limited use. We need to refine it into something valuable, and that’s what data analytics is. It’s about extracting value, converting data into insight, and enabling data-driven decisions.”

— Dr James Abdey, Associate Professorial Lecturer in Statistics at LSE

Why professionals get stuck — and how to move forward

While data roles are on the rise, many professionals — even those with deep domain expertise — find themselves unsure how to build credibility with data. Some feel overwhelmed by where to start. Others struggle to demonstrate experience in a field they haven’t formally worked in, despite working with data daily.

This was true for Anna Kramer, EMEA Finance Manager at Manychat, who shared during the panel discussions how her responsibilities were increasingly entangled with large datasets. Yet, her skill set wasn’t keeping pace:

“I worked with many reports containing a lot of data. And at some point, I had to ensure the data across different reports was still coherent. I realised that my basic knowledge of Excel wasn’t enough anymore. I saw that there were new tools available that could help ensure consistency and accuracy, and I wanted to be able to use them properly.”

— Anna Kramer, EMEA Finance Manager at Manychat & Career Accelerator Alumna

It wasn’t just the tools. Anna also found herself working more frequently with developers and data professionals, but lacked the vocabulary and fluency to engage fully:

“I was involved in projects implementing IT solutions for finance and compliance, and more and more, I was working closely with developers or business analysts. They were speaking in their IT or data language, and I wanted to understand them better, so our projects could be even more effective.”

Her experience speaks to a common blocker: the distance between knowing your domain and being able to interface confidently with data systems, tools, and teams. While that distance isn’t insurmountable, it requires targeted, practical learning, with a clear focus on what employers and projects actually demand.

To close that disconnect between insight and impact, Anna chose to build a data skill set that matched the scale and complexity of her role, using the LSE Data Analytics Career Accelerator.

What employers actually want: Skills that signal readiness

For professionals stepping into data-driven roles, the gap isn’t always about raw technical ability. Often, it’s about real-world application and the ability to translate that knowledge into business impact.

Kate McDermott sees this challenge play out frequently in the hiring process, particularly with junior analysts:

“What I see missing is any evidence of work that demonstrates their understanding of business problems, and how the analytics work they do connects to that. There’s a technical base in place, but it’s usually quite theoretical. It’s not clear how that skill will translate into the day-to-day of a business setting.”

— Kate McDermott, Associate Director at Omnis Partners

She explains that the technical CVs flooding hiring pipelines often lack exactly what businesses are prioritising most: contextual thinking, critical communication, and proof of skills that translate beyond the classroom.

Later in the panel, Kate explained what employers are really looking for — not just a checklist of tools, but a track record of applied thinking and outcomes:

“My advice to candidates is: if you want to stand out, you need to be able to connect the technical work that you’re doing to value creation. It doesn’t have to be commercial value. It could be time savings, efficiency, enabling workforce decisions. But if you can’t showcase that in your CV or in an interview, you’ll become part of a very large talent pool that’s technically able — but not hire-ready.”

— Kate McDermott, Associate Director at Omnis Partners

In today’s market, proven ability matters as much as knowledge. That’s why the LSE Career Accelerator combines academic credibility with hands-on project experience — blending world-class instruction with applied, real-world data work that builds confidence and a portfolio that proves you’re “hire-ready”.

How to build data skills that stick — and cut through the noise

Once you discover the demand for data fluency, the next question becomes practical: How do you learn what matters? And how do you build skills that are truly understood, not just practised in isolation?

Dr James Abdey was clear that it starts with focusing on principles, not trends.

“You need to start with a solid conceptual foundation. That’s what enables you to upskill later. The field will evolve — it’s evolving every day. New tools will emerge. But the basics of how to approach data problems, how to structure analysis, how to think critically with data — those are timeless.”

— Dr James Abdey, Associate Professorial Lecturer in Statistics at LSE

He also pointed to the difference between learning content and developing capability, and why the format matters when you’re trying to build long-term confidence:

“There are lots of MOOCs and free resources, and they’re not without value. But they can leave people with an illusion of understanding. You watch a video, you click through a notebook, but when you’re then asked to work with a messy dataset or explain your thinking, that’s when it breaks down. You want learning that’s applied, that involves feedback, and that helps you build fluency in the language of data.”

For professionals at an inflexion point — whether trying to switch careers or level up in their current field — James recommends starting with questions about outcomes, not platforms.

“Ask yourself: What kind of problems do I want to be solving? What kind of decisions do I want to influence? Once you answer that, then the right tools, techniques, and learning experiences tend to emerge pretty clearly. But if you start by chasing platforms or job titles, it’s easy to get lost.”

— Dr James Abdey, Associate Professorial Lecturer in Statistics at LSE

What you’ll actually do in a data role — and how to prepare

Understanding what data analysts actually do on a daily basis can feel vague or even overwhelming at first. But the practical day-one tasks aren’t theoretical — they’re grounded in real problems that need timely answers.

Anna offered a clear example from her own work:

“Before, when I worked with reports, I would find the answer to the question and move on. Now, I ask — what else can I get from this data? And then again: what else? I iterate. I get much more insight out of the same report. It helps me see how my team works — and that’s helped accelerate my path inside the company.”

— Anna Kramer, EMEA Finance Manager at Manychat & Career Accelerator Alumna

Anna’s experience shows how deeper engagement with data can unlock new momentum in your role. Dr James Abdey reinforced this, offering a sharp distinction between knowing tools and knowing how to apply them for business impact:

“In my view, the difference between someone who knows tools like SQL and Python — and someone who can actually use them to deliver business value — comes down to structured problem solving. It’s about understanding the business context, asking the right questions, and applying your tools intentionally.”

— Anna Kramer, EMEA Finance Manager at Manychat & Career Accelerator Alumna

Whether it’s identifying outliers in a product dataset, visualising marketing KPIs, or prompting a GenAI model to explore patterns, the toolkit is real and repeatable.

Tools you’ll use in many early-stage roles include:

  • SQL — for querying and joining datasets
  • Python — for automation and analysis
  • Power BI / Tableau — for dashboards and stakeholder reporting
  • GenAI tools — for ideation and exploration
  • Business storytelling — to shape your insights into action

The LSE Career Accelerator: Structured learning for real business impact

From SQL and Python to GenAI and business storytelling, mastering tools is just the start. What sets professionals apart is the ability to apply them in context — to solve real problems, influence decisions, and communicate insights clearly. 

The LSE Data Analytics Career Accelerator is a career-outcomes-driven programme that delivers exactly that: combining world-class instruction with practical experience to build full-stack data fluency. 

Designed in collaboration with industry experts, the six-month, part-time format blends foundational theory with hands-on application, helping learners go beyond tool proficiency to deliver business impact.

For Career Accelerator Alumna Anna Kramer, the impact of data skills was immediately clear when stepping into a new leadership role:

“I was able to get deeper insights faster, see connections between different functions, and help my team move from reactive to proactive. With a proper data toolkit, you’re no longer just describing what happened, you’re actually influencing what happens next.”

— Anna Kramer, EMEA Finance Manager at Manychat & Career Accelerator Alumna

From learning to doing: Practical skills and real-world experience

One of the defining features of the LSE Career Accelerator is its emphasis on real-world application. Through a final Employer Project — designed by an actual industry company — learners get the chance to apply their skills to a real business problem.

Anna spoke about this project as the turning point in her learning journey:

“The turning point was the final module — the Employer Project. I realised I could really speak with data analysts. Now I’m in cross-functional projects with our data analytics department and I feel 100% confident. They can’t tell me something’s impossible — because I know what’s possible.”

— Anna Kramer, EMEA Finance Manager at Manychat & Career Accelerator Alumna

To ease the leap from foundational concepts to real-world complexity, Dr Abdey explained how the programme carefully layers each stage of learning, so by the time learners reach the Employer Project, they’re equipped to work on it with confidence.

“We scaffolded the learning carefully. You start with exploratory analysis — how to visualise and tell stories with data. Then you move into more advanced tools like Python and predictive analytics. Finally, you apply everything through the Employer Project. It’s active learning, not passive. You don’t just ‘get a certificate’ — you get experience.”

By the end, learners are equipped to work across teams, influence decisions, and deliver outcomes through data.

A career catalyst, not just a course

If you’re considering a move into data, or want to integrate data skills into your existing role, the path doesn’t have to involve starting over. The LSE Data Analytics Career Accelerator offers a practical, recognised, and flexible route to career change and advancement, guided by employer input and expert career coaching.

Over six months, you’ll build practical fluency in SQL, Python, Power BI, GenAI tools, and data storytelling — all within a curriculum shaped by industry needs. You’ll work on a real business challenge from companies like the Bank of England, VP Analytics, StudyGroup, PureGym, and GaeaAI, producing a portfolio project that goes beyond theory to show how you solve problems, communicate insight, and create value with data. 

As Kate McDermott made clear, what stands out in candidates is their ability to translate technical skills into business value and the evidence they can make this connection. This link turns skill into opportunity, and learning into real career results.

Curious to see how the LSE Career Accelerator could support your next move? Download the brochure to explore the curriculum, structure, and outcomes.