Skip to main content


The one-year course is structured into four types of modules (Professional Skills, Core modules, Specialisation and Project) spanning three terms:

  • All of our students select one specialisation area. Please see the pull down menu on the top for information about particular specialisations. Within each specialisation, there is a set of compulsory lectures. Students pick their specialisation when they apply.
  • All of our students do the Professional Skills module. See our local page from some background information and the official University pages
  • All of our students do Core I (basic methods training). These 30 credits are compulsory.
  • All of our students do a selection of Core II modules (advanced methods). Here, you have some freedom. You don’t have to choose the modules right away, but you can pick the modules of your choice towards the end of term 1/prior to term 2.

Schematic timetable

  Term 1 Term 2 Term 3
Professional Skills

(15 credits)

Software carpentry

  • Agile Software Project Management
  • Systematic Testing and Reproducibility
  • Version Control and Continuous Integration
Presentation and Ethics

  • Technical & Scientific Report Writing
  • Ethics of Data Science
  • Communicating Science
Entrepreneurial Thinking

  • Innovation management
  • Change management
  • Computational intelligence
Core Modules Core I modules
(30 credits)

Core II modules
(30 or 45 credits; depends on the weight of the specialisation – sum of all modules has to be 180)

Please consult List A from the core regulations.


  • Duration: 8-10 weeks
  • Written thesis
  • Options
    • with external partner, typically in a team of 2-3 students;
    • within subject specialisation; or
    • with methodological work.

(60 credits)

Subject specialisation

(30 or 45 credits)

Please consult list B from the core regulations.


Further details

  • For an official course description, please see the University’s page on G5K609.
  • All the core information, i.e. which courses have to be chosen and what is available, can be found in the course’s core regulations. The core regulations for one academic year are typically released in the summer before the term starts, i.e. become available pretty late. Previous year’s regulations however give a good indication which modules are typically offered (not all of them run each individual year).
  • You can search for details on particular modules through the Postgraduate Modules directory.
  • The menu above provides additional info about the specialisations and particular modules.

Example routes through the programme

First example (the physics expert): The student has chosen Astrophysics and is interested in large-scale computations. So in the first term, the student attends 30 credits of astrophysics. The student also has to sit the first few  Professional Skills workshops in term 1 as well as the Core I modules. These two are compulsory (see core regulations). After term 1, the student does the remaining 15 credits of Astro and the Professional Skills, but really found that she is very interested in the interplay of large-scale cosmology simulations and the calibration of the insights to real observation data. So she takes a module worth 15 credits on simulation techniques (both continuous and discrete systems) plus a 15 credit module on data acquisition and pre-processing. In term 3, she does a dissertation in collaboration with a Physics professor.

Second example (the data analysis enthusiast): The student takes Astrophysics, Professional Skills, and Core I, but is really interested in the statistics and mathematics behind machine learning and AI as they are used in Physics. He thus takes two statistics modules in term 2 which are worth 30 credits. After term 2, the student books into a dissertation project in collaboration with a local start-up.

Third example (a quantitative finances fan): The student takes Financial Technology modules which is a lot of mathematical principles behind financial models, Professional Skills, and Core I. In term 2, the student studies the modelling of discrete and continuous phenomena (development of stocks can be modelled by partial differential equations, e.g.) and complements this with more statistics lectures from the methodological stream. After term 3, the student books into a dissertation project in collaboration with a local academic with a strong business background who offers a project under the umbrella of MISCADA.