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  • English-taught
  • Winter & summer intake

Data Engineering and Analytics (M.Sc.)

at TUM in Munich

At a glance

Language
English-taught
Duration
4 sem 120 ECTS
Intake
Winter & summer
Tuition
€6,000 per semester; see fee notes
Semester fee
€97

● Closed for this intake All applicants · Winter semester The window usually repeats next cycle; the full deadline table below shows every route and date.

Big-data-focused master combining data analysis (convex optimization, computational statistics, machine learning) with data engineering (distributed systems and databases, query optimization, high-performance computing), preparing for industry data-engineering and analyst roles as well as PhD research. This programme is based at TUM's Garching campus.

Admission requirements

Prior degree
B.Sc. in Informatics, or Mathematics with an Informatics minor (or similar)
Minimum grade
not confirmed
English
Sufficient English per TUM's accepted certificate list (GMAT above 600 or an English-medium prior degree also accepted)
German
not required
Prerequisite credits
  • Foundations of informatics; programming of algorithms and databases
Also required
Eignungsverfahren (aptitude assessment) required as part of the application process. Applicants are ranked; meeting minimum requirements does not guarantee admission. Two-stage procedure: written test (WS 2026/27 date 20 August 2026), a scientific essay of about 1,000 words and a two-page statement of reasons. GRE General mandatory for applicants with degrees from Bangladesh, China, India, Iran or Pakistan. uni-assist VPD (Vorprüfungsdokumentation) required for applicants with a non-German bachelor's degree; APS certificate required for applicants from China, India and Vietnam.

Deadlines & timeline

Deadlines differ by where your degree is from. Dates change every cycle, so always confirm on the official page before planning. Where no fixed calendar date exists or none is confirmed yet, the Details column explains each case.
Who Intake Deadline Details
All applicants Winter semester closed for this intake; next cycle usually repeats the same window Application window 1 February - 31 May annually via TUMonline. TUM strongly encourages non-EU applicants to apply by 15 March so there is time for visa processing; missing documents such as the uni-assist VPD can follow until the final deadline.
All applicants Summer semester Application window 1 October - 30 November annually via TUMonline.

Fees & funding

Tuition
€6,000 per semester
Semester contribution
€97
Semester ticket
not included

⚠ TUM charges tuition to non-EU/EEA students newly enrolling from WS 2024/25: €6,000/semester for this programme, on top of the €97/semester Studierendenwerk fee. EU/EEA citizens and holders of a German bachelor's degree or German Abitur pay no tuition; merit- and need-based fee waivers exist. No semester ticket is bundled - students subscribe separately to the discounted student Deutschlandticket. The Linde/MDSI Master's Scholarship (€1,000/month) specifically targets students of this programme. Verify at https://www.tum.de/en/studies/fees

Scholarships are listed per university: see TUM scholarships.

How to apply

uni-assist
required (the deadline table shows who this applies to)
Application portal
campus.tum.de
Official page
www.cit.tum.de

EU applicants and holders of German degrees usually apply directly via the university's Campusportal instead of uni-assist; the deadline table above says which route applies to you.

What students say

Via StudyCheck · 2024-2026: an aggregated rating, not our own assessment, paraphrased in our own words and never quoted.

4.5 /5

Only four StudyCheck reviews so far (4.5/5) - a small, provisional sample, but a consistently positive one. Reviewers praise the balance of theoretical computer science with hands-on data analysis, the big-data and machine-learning projects, and lecturers with industry experience. The recurring caveat is that two large foundational courses leave less room to specialize early on. The listing appears on StudyCheck under the slug 'data-web-engineering'.

Liked

  • Good balance of theoretical CS and hands-on data analysis
  • Big-data and machine-learning projects stand out
  • Lecturers with industry experience

Criticised

  • Two large foundational courses reduce early specialization flexibility
  • Very small review sample (n=4) - provisional

Where graduates go

No alumni outcome data found yet.

External resources

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