Data Science Abroad: Costs, Countries, and How to Apply

Where you can study it

Every country below has verified programmes listed on Global Admissions. Figures are the cheapest-to-median sourced tuition per country, not a currency conversion — tap through to the sample programmes below for individual, linkable listings.

Figures are the fee as recorded on Global Admissions from each programme's own listing — basis (per year, per semester, or per credit) can vary by programme; verify the exact figure directly on the university's own page before budgeting.

Sample programmes

A cheapest-first sample of individual, sourced programmes — each one links to its own page on Global Admissions.

Fees as recorded from each programme's own listing; verify on the university's page before budgeting.

The famous names

These come up constantly in data-science-abroad discussions — here's what each one is actually known for, so you can weigh the reputation against your own background and budget.

You probably don't need a computer science degree

The University of Bath's MSc Data Science says applicants should have a good first degree in a numerate subject, explicitly naming computer science, mathematics, physics, economics, engineering, and relevant social sciences as acceptable. What Bath actually checks is proficiency in calculus and linear algebra, familiarity with probability and statistics, and a good foundation in programming — not the name of your undergraduate degree.

Not every school is this flexible, though. TUHH's Data Science master's expects applicants to already hold a bachelor's in data science or an equivalent programme with a sufficiently strong computer science and mathematics background — a stricter bar than Bath's. Read each programme's actual prerequisites rather than assuming one school's flexibility applies everywhere.

The technical bar, in specifics

Where schools do publish detail, it's specific enough to self-check against. KTH's master's in Biostatistics and Data Science requires at least 180 ECTS or equivalent, including at least 60 credits across mathematics, statistics, and programming — specifically multivariable calculus, numerical methods, probability theory and statistics, and programming in a general-purpose language such as C++, Python, or Java.

TUHH goes further and strongly recommends its own preparatory MINTFIT test for Data Science applicants, covering databases and big data, mathematical preliminaries for machine learning, elements of machine learning, probability and statistics, and programming — a useful way to self-assess readiness before you apply, even if you're not applying to TUHH specifically.

Checking whether a programme is genuinely technical

"Data science" gets used loosely: some programmes are closer to business analytics with a data label attached. Two checks help separate the genuinely technical programmes from the rebrand: whether the school runs professional accreditation, and whether the curriculum includes real project and industry work rather than theory alone. BCS's AfDSP Degree Accreditation recognises data-science programmes that meet joint professional standards across mathematics and statistics, computing, and ethics — a useful objective signal.

The University of Bath's MSc, for instance, includes collaboration in multidisciplinary teams on data-driven projects and consultation with industry experts as part of the course itself, not an optional add-on. Ask any programme you're considering for specifics on both fronts before assuming the title tells you enough.

Before you shortlist a programme

A few checks that catch the most common surprises in data-science admissions abroad — worth running through before you assume your background doesn't qualify.

1. 1

   Check whether your first degree needs to be computer science specifically

   Some schools, like Bath, accept a numerate background more broadly, including economics or relevant social sciences, as long as you can evidence calculus, linear algebra, probability, and programming. Others, like TUHH, expect a stronger existing CS and maths background. Check the specific programme.

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2. 2

   Confirm you can evidence the actual prerequisites, not just a numerate-sounding degree

   What schools check is usually calculus, linear algebra, probability/statistics, and programming ability — sometimes a specific credit count, like KTH's 60 credits across maths, stats, and programming within its 180-ECTS minimum. Map your own transcript against the published list.

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3. 3

   Use a self-assessment resource if you're unsure of your readiness

   TUHH strongly recommends its own MINTFIT preparatory test, covering databases and big data, ML mathematical preliminaries, ML elements, probability/statistics, and programming — a useful way to check your own gaps before you apply anywhere.

   source
4. 4

   Look for professional accreditation as a quality signal

   BCS's AfDSP Degree Accreditation recognises data-science programmes meeting joint standards across maths and statistics, computing, and ethics — worth checking for if you want an objective marker beyond a programme's marketing.

   source
5. 5

   Budget for a non-refundable deposit once you accept an offer

   The University of Bath, for example, requires a non-refundable £1,000 deposit to secure your place after an offer. Confirm whether your target school charges one and how it applies against your first-term fees.

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Funded routes worth checking

The strongest sourced route here is Sweden-specific, but general government routes are worth checking too — none guarantee full funding, so confirm eligibility before you count on any of them.

Each route name links to its official source. Verify the current call and eligibility before applying — cycles and seat counts change annually.

Frequently asked questions