What to Study for a Career in AI

Choosing what to study can feel overwhelming, especially when you're interested in something as exciting and fast-moving as artificial intelligence. 

There are clear pathways into AI careers, whether you're choosing GCSEs, planning your A Levels, or thinking about university. This guide will show you exactly what to study for a career in AI. We've broken down the AI career landscape into simple, practical steps you can follow right now.

Key Takeaways

• Maths and Computer Science are the foundation – these subjects are essential at both school and university level for most AI careers.

• There are multiple routes in – traditional degrees, bootcamps, online courses, and self-taught paths can all lead to AI roles.

• AI careers are diverse – you don't have to be a programmer; roles range from machine learning engineers to AI ethicists.

• Start early and build projects – learning Python and creating a portfolio will give you a head start, no matter your age.

Why AI Is One of the Fastest-Growing Career Fields

Artificial intelligence is transforming nearly every industry you can think of. From helping doctors diagnose diseases to powering the apps on your phone, AI is everywhere.

The global AI market was valued at $279.22 billion in 2024 and is growing year-on-year according to (opens in a new tab)Grand View Research (opens in a new tab). This means one thing: companies desperately need people who understand AI.

• Healthcare organisations use AI to spot patterns in medical scans. 

• Banks rely on it to detect fraud. 

• Manufacturers use AI-powered robots to build cars. 

• Streaming services like Netflix use it to recommend what you should watch next

• Students can use AI tools to help enhance their learning experience.

What makes this career field particularly exciting is that it's not slowing down. As AI technology becomes more sophisticated, new roles are being created all the time.

What Skills Do You Need to Work in AI?

Working in AI requires a blend of technical abilities and softer skills that help you solve real-world problems.

Technical Skills 

These form the backbone of most AI careers. Programming is essential – you'll need to write code that tells computers what to do. Python is the most popular language in AI because it's relatively easy to learn and has powerful libraries for machine learning.

Understanding maths is crucial too. You'll use: 

• Statistics to analyse data

• Linear algebra to work with large datasets

• Calculus to understand how machine learning algorithms improve over time

Soft Skills 

Soft skills are just as important. 

Problem-solving helps you break down complex challenges into manageable steps. AI projects rarely work perfectly the first time, so you need patience and creativity.

Communication matters more than you might think. You'll often need to explain technical concepts to people who aren't experts, whether that's your boss, a client, or the public.

Ethical awareness is becoming increasingly vital. AI systems can accidentally reinforce biases or be used in harmful ways, so understanding the ethical implications of your work is essential.

Data literacy and analytical thinking tie everything together. You need to understand where data comes from, whether it's reliable, and what stories it's telling you. Being able to look at numbers and spot patterns, trends, or problems is a skill that will serve you throughout your career.

What Subjects Should You Study at School?

Choosing the right subjects at school sets you up for success in AI. Let's break this down by stage.

GCSE – Maths, Computer Science, Physics

At GCSE level, focus on building a strong foundation in three key areas.

Maths is non-negotiable. Everything in AI involves numbers, calculations, and logical thinking. Take GCSE Maths seriously and aim for the highest grade you can achieve.

Computer Science introduces you to how computers work, basic programming concepts, and problem-solving with code. This subject shows you whether you genuinely enjoy working with technology.

Physics teaches you logical reasoning and how to work with abstract concepts. Many AI problems involve understanding systems and how different parts interact, which physics prepares you for brilliantly.

If your school offers it, taking Statistics can be helpful too. But don't panic if this isn’t available – the core trio of Maths, Computer Science, and Physics will serve you well.

A Level – Maths, Further Maths, Computer Science, Physics, Psychology

A Levels are where you really start specialising for an AI career.

Maths A Level is essential for almost every AI-related university course. Universities specifically look for this qualification because AI involves so much mathematical reasoning.

Further Maths gives you an even stronger mathematical foundation. While not always required, it's highly valued by top universities and will make your first year of university significantly easier.

Computer Science A Level builds on your GCSE knowledge and gets you programming more seriously. You'll learn about algorithms, data structures, and computational thinking – all crucial for AI work.

Physics A Level continues to develop your problem-solving abilities and mathematical applications. It's particularly valuable if you're interested in robotics or computer vision.

Psychology might seem like an unusual choice, but it's increasingly relevant for AI. Understanding how humans think and behave is important when you're designing systems that interact with people. It's particularly useful if you're interested in human-computer interaction or AI ethics.

What Should You Study at University?

University is where you'll gain the deep knowledge and practical skills needed for an AI career. Several degree paths can lead you there.

Computer Science is the most common route into AI. You'll learn programming languages, algorithms, data structures, and software engineering. Most Computer Science degrees now include AI and machine learning modules as standard.

Artificial Intelligence degrees are becoming more common. These specialist courses focus specifically on AI from day one, covering machine learning, neural networks, natural language processing, and robotics.

Data Science prepares you to work with large datasets and extract meaningful insights. You'll study statistics, data visualisation, and machine learning techniques. This degree is perfect if you're interested in the analytical side of AI.

Mathematics or Statistics degrees provide the strongest theoretical foundation. While you'll need to learn programming separately, the advanced maths you'll study is invaluable for understanding how AI algorithms actually work.

Cognitive Science or Neuroscience offers an interdisciplinary approach. These degrees combine psychology, computer science, and biology to understand intelligence itself. They're particularly relevant if you're interested in how AI can mimic human thinking.

Alternative Routes Into AI

You don't need a traditional university degree to break into AI. Many successful AI professionals have taken non-traditional paths.

Coding bootcamps offer intensive, practical training in a matter of months rather than years. They're designed to get you job-ready quickly.

Online certifications provide flexible learning you can do at your own pace. Andrew Ng's Machine Learning course on Coursera (opens in a new tab) is legendary in the AI community and has taught millions of students worldwide.

Apprenticeships let you earn while you learn. Companies are beginning to offer AI and data apprenticeships where you work in a real job whilst studying for a qualification.

A 2022 Stack Overflow survey (opens in a new tab) found that learning to code online had grown from 60% to 70% over just one year, showing that self-taught routes are also viable. The key with alternative routes is demonstrating what you can do. Employers care about your skills and projects more than your credentials, especially in fast-moving fields like AI.

AI Career Paths and What to Study for Each

AI isn't just one job – it's an entire ecosystem of roles. Let's explore the main career paths and what you should study for each.

Machine Learning Engineer – Computer Science, Maths, Statistics

Machine Learning Engineers build the systems that learn from data. They create algorithms that can spot patterns, make predictions, and improve over time.

A Computer Science degree combined with specialised machine learning courses will set you up perfectly. Focus on courses covering neural networks, deep learning, and reinforcement learning.

Data Scientist – Data Science, Statistics, Python

Data Scientists investigate data to find insights that help businesses make decisions. They clean messy datasets, run statistical analyses, and build models to solve specific problems.

Statistics is your most important subject here. You need to understand probability, hypothesis testing, and regression analysis inside and out.

Programming in Python (particularly libraries like pandas, NumPy, and scikit-learn) is crucial. A Data Science degree or a Statistics degree with programming skills will work well.

AI Researcher – Maths, Deep Learning, Academic Research

AI Researchers push the boundaries of what's possible. They work in universities or research labs, developing new algorithms and techniques.

This path typically requires a PhD. You'll need exceptional mathematical ability and a deep understanding of current AI research.

Start with a strong undergraduate degree in Computer Science, Maths, or AI, then pursue postgraduate research. Publishing papers and contributing to the academic community is part of the job.

AI Ethicist – Philosophy, Law, Sociology, Data Ethics

AI Ethicists ensure AI systems are fair, safe, and beneficial to society. They consider questions like: "Should this algorithm be used?" "Is this AI system biased?" "What are the societal implications?"

You might study a humanities degree and then take technical courses to understand AI, or study Computer Science and supplement it with ethics courses.

Practical Steps to Start Building an AI Career

Ready to get started? Here are concrete actions you can take today, regardless of your age or current situation.

• Learn Python or R. Python is the most popular language for AI and has a gentle learning curve. 

  • Start with free resources like Python.org (opens in a new tab)'s tutorials (opens in a new tab). 

  • Aim to write a simple programme within your first week.

• Take a free machine learning course. Google's Machine Learning Crash Course is another solid option. 

  • Don't just watch – actually do the exercises.

• Join open-source projects. Websites like GitHub (opens in a new tab) host thousands of AI projects where beginners can contribute. 

  • Start by fixing small bugs or improving documentation. 

• Attend AI meetups or webinars. Look for local tech meetups or online AI communities.

  • These connections can lead to mentorship, job opportunities, and friendships with people who share your interests.

• Build a portfolio on GitHub. Create your own projects, even simple ones. 

  • Maybe build a programme that recommends films, or one that classifies images of cats and dogs. 

Start small. Don't expect to build the next ChatGPT in your first month. Focus on learning one concept at a time and building simple projects that gradually increase in complexity.

Frequently Asked Questions

What programming languages should I learn for AI?

Python is your best starting point. It's the most widely used language in AI and has extensive libraries that make machine learning much easier.

Can I get into AI without a maths background?

It's challenging but not impossible. Many AI roles require at least a basic understanding of statistics, algebra, and probability.

Some AI roles, like AI Ethics or AI Product Management, rely less heavily on advanced maths. You'll still need to understand how AI works, but you won't be deriving algorithms yourself.

What is the best degree for AI?

There's no single "best" degree because it depends on your interests and the specific AI career you want.

Computer Science is the most versatile choice. It prepares you for the widest range of AI roles and provides transferable skills if you change direction.

If you're certain about AI from the start, a specialised Artificial Intelligence degree will give you deeper, focused knowledge.

For research careers, a Mathematics degree followed by an AI-focused PhD is an excellent path.

Final Thoughts

AI is a broad and welcoming field. Whether you're drawn to the technical side of building algorithms, the analytical work of data science, or the ethical questions of responsible AI, there's a place for you.

The most important thing is to start now. Pick up Python today. Watch a tutorial. Write your first ten lines of code. Each small step builds momentum.

Your AI career starts with the choices you make today. Make them count.

References

Grand View Research - Artificial Intelligence Market Summary (opens in a new tab)

2022 Stack Overflow Developer Survey (opens in a new tab)

Andrew Ng's Machine Learning course on Coursera (opens in a new tab)

Python.org (opens in a new tab) tutorial (opens in a new tab)

Github (opens in a new tab)