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

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

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: 

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.