Study Germany

Data Science (MSc)

at TU Dortmund in Dortmund

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

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.

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, 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
Non-EU applicants Winter semester closed for this intake; next cycle usually repeats the same window Regular window early January – 15 May; extended to 15 June for WS 2026/27. Via International Office / uni-assist.
Non-EU applicants Summer semester no fixed date Annual window early November – 15 January. Via International Office / uni-assist.
EU / German-degree applicants Winter semester no fixed date 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 no fixed date 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 last verified for WS 2025/26, including the nationwide Deutschland-Semesterticket. The WS 2026/27 amount is not yet published by TU Dortmund and is to be confirmed (the Studierendenwerk component is €97; the Deutschland-Semesterticket price rise suggests a moderate increase).

Scholarships are listed per university: see TU Dortmund scholarships.

How to apply

uni-assist
required (the deadline table shows who this applies to)
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

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

4.2 / 5 aggregated rating, not our own assessment

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 are 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.