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How to become a quant in the UK and Europe, in quantitative finance

Reference article

A quant uses probability, statistics and code to price things and manage risk in markets. The route in the UK and Europe has the same three parts as anywhere else: the maths, the code, and practice on real data. What changes by country is the shape of the degrees. The three UK programmes on this page are listed at 10 months to a year, and the three European ones in the table at 12 months to four semesters.

A quant is a person who uses maths, statistics and code in quantitative finance, to price things and manage risk in markets. The word "quant" covers a few different jobs. A researcher looks for patterns in data, a trader runs a book of positions, a developer builds the systems, and a risk quant measures what could go wrong. The four quant jobs explains each one.

This page is for readers in the UK and the rest of Europe. It lists what to learn, which master's programmes teach the maths, a free course to start with, and public data to practise on. Every outside page below was read on 8 November 2026. Official pages change, so open each link before you rely on a line.

Nothing here promises a job or a salary. For what some trading firms say about degrees, read getting into quant from any college.

The subjects, in the order we suggest

The subjects are the same in London, Zurich, Paris or Amsterdam.

  1. Probability. Probability is the maths of chance: how likely each outcome is. The expected value is the average outcome if you could repeat the same bet many times.
  2. Statistics. How to learn from data, and how sure you can be of what it shows. The standard deviation measures how far results usually land from their average.
  3. Linear algebra and calculus. Linear algebra is the maths of tables of numbers. Calculus is the maths of change.
  4. Stochastic processes. A stochastic process is a quantity that moves at random over time, such as a price. Imperial's course page, below, names stochastic analysis in a core module.
  5. Code. Python first. Python is a programming language: a way to write instructions a computer follows. Some roles add C++, a faster and stricter language.

After that, the subjects split by role. The quant roadmap by role sets out each branch.

A ten-minute self-test

We suggest you try these before you choose a course. They show the level the first two subjects start at. Work each one on paper, then read our answer.

1. Two dice. You roll two fair dice. What is the chance the total is 7?

There are 6 times 6, so 36, equally likely pairs. Six of them total 7: (1,6), (2,5), (3,4), (4,3), (5,2) and (6,1). The chance is 6 out of 36, which is 1 in 6.

2. Three coins. You toss a fair coin three times. What is the chance of at least one head?

It is easier to count the opposite. No heads at all means tails three times, with chance one half times one half times one half, which is 1 in 8. So at least one head has chance 7 in 8.

3. A fair price for a game. A game pays you the number a fair die shows, in pounds. What is the most you should pay to play, if you only care about the average?

Each face comes up with chance 1 in 6. The average payout is (1 + 2 + 3 + 4 + 5 + 6) divided by 6, which is 21 divided by 6, or 3.5 pounds. Pay less than that and you win on average. This is an expected value. In our view, questions of this shape are a common way for quant interviews to start.

4. From a day to a year. A price moves by about 1% a day, measured as a standard deviation. Roughly how much does it move in a year of 252 trading days?

If each day is independent of the last, the variances add up. Variance is the standard deviation squared. So the yearly standard deviation is 1% times the square root of 252. The square root of 252 is about 15.9, so the answer is about 15.9% a year. The answer leans on the independence assumption: if prices trend from day to day, the true number is larger.

If all four felt easy, start on statistics and code. If one felt hard, start with probability.

UK programmes that teach the maths

None of these is a requirement. They are examples of taught master's programmes whose official pages describe probability, finance and code. Read each page for entry rules and dates.

In our view, a mathematics, physics, statistics, computer science or economics degree with strong maths is also a sound base. What matters past the degree is whether you can use the maths, which the next sections are about.

European programmes that teach the maths

Again, these are examples, not a list you must choose from.

Many people search for quant jobs by city, such as London or Amsterdam. We do not list or rank firms here. Each firm's own careers page is the source for what it asks for.

How the programme lengths compare

The lengths below come from the official pages linked above.

ProgrammeCountryListed length
Imperial, MSc Mathematics and FinanceUK1 year
Oxford, MSc in Mathematical and Computational FinanceUK10 months
LSE, MSc Financial MathematicsUK10 months
UZH ETH, MSc in Quantitative FinanceSwitzerland3 semesters
TUM, Mathematical Finance and Actuarial ScienceGermany4 semesters
University of Amsterdam, Financial EconometricsNetherlands12 months

The table is not a ranking. It is in the order the programmes appear above.

A free university course to start with

MIT OpenCourseWare publishes 18.S096 Topics in Mathematics with Applications in Finance from Fall 2013, taught by Dr. Peter Kempthorne, Dr. Choongbum Lee, Dr. Vasily Strela and Dr. Jake Xia. The page carries a Creative Commons licence and a download option. MIT is in the US, but the maths is the same everywhere.

For a longer free sequence, the free quant learning path lists four steps: two university courses and two free books.

Public data to practise on

Past data is what a backtest uses. A backtest runs a fixed rule on past data to see how it would have done. Read what a backtest is, in plain words first. Three public sources:

Check each source's terms of use before you publish anything built on it. Past results do not tell you the future, and a test that looks good can still be luck.

In our view, one small project on public data, with the method written down, says more than a line that reads "interested in quant".

Working in the UK from abroad

Rules on who may work where are set by each government. For the UK, the official starting point is GOV.UK's Work in the UK page. One route it lists is the Skilled Worker visa, which GOV.UK says "allows you to come to or stay in the UK to do an eligible job with an approved employer". This page gives no immigration advice. Read the official pages for your own case.

Practising for quant interviews

To practise, Thuztra Learn keeps a bank of interview questions and quant puzzles. For books, see the best books for quant interviews.

What Thuztra Learn covers for a UK or European reader

Thuztra Learn is our product. It teaches the maths of quantitative finance from zero, in short lessons. It starts with what quants do and school maths, then probability, data and Python, and then one path per role: researcher, trader, developer and risk. A separate coding path covers Python, C++ and SQL. It is not a degree, it does not prepare you for a university application or a visa, and it does not place anyone in a job. Membership is a paid subscription: see what Thuztra Learn is and what it costs.

A first step

Do the self-test above. Then open one programme page from the lists above, the one closest to where you live, and find its module list. Write down the first module you could not explain to a friend today.

You know it worked if you can name that module and one topic inside it. Two things go wrong most often. The module names are full of words you do not know: start with what quantitative finance actually is and the beginner lessons on Thuztra Learn (called Level 0) instead. Or a page has moved: go to the university's home page and search for the programme name.

Fine print: Imperial College London, the University of Oxford, LSE, the University of Zurich, ETH Zurich, Sorbonne Université, École Polytechnique, ESSEC, TUM, the University of Amsterdam, MIT, the Bank of England, Eurostat, the Federal Reserve Bank of St. Louis and GOV.UK are named here only as makers of programmes, courses, public data and official guidance. They have no link to Thuztra. Education only, not advice.

In short

No single degree is required. The programmes below are examples of where the maths is taught in depth, not a list you must pick from. Learn probability first, then statistics, then code, and show work on public data. Read each official page and each job description yourself, because they change.

Terms used here

Related

The quant roadmap by role, in quantitative finance

What to learn, in order, for a quant trader, researcher, developer or analyst. One shared start, four branches, and a plain list of which steps Thuztra Learn teaches and which it does not.

The four quant jobs

Researcher, trader, developer and risk, described by what each actually does all day. One rule followed through all four desks, and the places where the four of them disagree.

The best books for quantitative finance interviews

Nine books for quant interviews, grouped by the stage they help with, plus what the Green Book and Heard on the Street each are, from the author and catalogue records. No book problems copied.

What a backtest is, in plain words

Ten numbers, one written rule, and the whole test done by hand. Then the harder half: why a past that was kind to a rule proves very little.

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