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Data Science (MSc)

at TU Dortmund in Dortmund

A mathematically rigorous, English-taught two-year research Master run jointly by the Faculties of Statistics, Computer Science and Mathematics, building on the German-taught BSc Data Science. It covers statistical theory, statistical learning, big-data methods and AI topics (deep learning, NLP, reinforcement learning), with case studies and a thesis.

At a glance

Degree
MSc
Language
English
English requirement
CEFR B2 minimum. Accepted proof: German Abitur (English from grade 5/7), an internationally recognised certificate, or a medium-of-instruction certificate. No specific IELTS/TOEFL score stated on the official page.
German requirement
not required
Credits
120 ECTS
Duration
4 semesters (2 years)
Intake
Winter & summer
Tuition
€0 (public university)

Focus areas: Advanced statistical learning · Statistical learning for big data · AI (deep learning, NLP, reinforcement learning, data visualization) · Practical projects / case studies

Admission requirements

Prior degree
BSc Data Science (TU Dortmund) or a comparable German/foreign degree assessed as informatics-, statistics- or mathematics-oriented
Minimum grade
2.7 (German scale)
Prerequisite credits
  • ≥44 ECTS total across mathematics, computer science and statistics
  • ≥8 ECTS computer science (algorithms, data structures, OOP, software engineering)
  • ≥16 ECTS mathematics (analysis, linear algebra, differential equations, discrete maths, numerics)
  • ≥4 ECTS statistics
Also required
Mandatory online self-assessment and self-disclosure; statistical report demonstrating special aptitude (waived for TU Dortmund BSc Data Science graduates). Not admission-restricted (no numerus fixus).

Deadlines & timeline

Deadlines differ by where your degree is from. Dates change every cycle — always confirm on the official page before planning. A dash means no fixed calendar date exists or none is confirmed yet — the Details column explains each case.
Who Intake Deadline Details
Non-EU applicants Winter semester Regular window early January – 15 May; extended to 15 June for WS 2026/27. Via International Office / uni-assist.
Non-EU applicants Summer semester — Annual window early November – 15 January. Via International Office / uni-assist.
EU / German-degree applicants Winter semester — EU applicants and holders of German degrees: up to the Friday before lectures start, via Campusportal; early application recommended.
EU / German-degree applicants Summer semester — EU applicants and holders of German degrees: up to the Friday before lectures start, via Campusportal; early application recommended.

Fees & funding

Tuition
€0 per semester
Semester contribution
€321.48
Semester ticket
included (Deutschland-Semesterticket)

University-wide: €0 tuition; €321.48 semester contribution confirmed for WS 2025/26, including the nationwide Deutschland-Semesterticket. A figure of ~€339.70 is in preparation for WS 2026/27.

Scholarships are listed per university — see TU Dortmund scholarships.

How to apply

uni-assist
required (see deadline details above for who exactly)
Application portal
www.uni-assist.de
Official page
statistik.tu-dortmund.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

Aggregated from StudyCheck · 2026 (n=14) — paraphrased in our own words, never quoted.

StudyCheck reviews (14 at last check, averaging 4.2/5) describe the programme as intellectually rewarding for mathematically minded students, with well-organised digital course materials and an experience that international students value. The recurring warning is difficulty: several reviewers report that certain courses are extremely hard to pass, sometimes across multiple attempts, on top of a generally heavy workload.

Liked

  • Rigorous, well-regarded curriculum
  • Good balance of theory and practice
  • Strong digital/online course materials
  • Lecturers described as responsive

Criticised

  • Very high workload
  • Some courses are extremely difficult to pass
  • Course organisation occasionally messy

Where graduates go

Further study
PhD pathway (source)
Industries
Pharmaceutical research, banking, insurance, software development, private and public research institutes, market research and management consulting — graduates sought especially for independently handling projects with large amounts of data (source)
Typical roles
Roles in AI, robotics, big data and data-project management (official programme page, aspirational) (source)

No official placement statistics exist for this programme — these are directions described by the university, not measured rates.