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 do the Professional Skills module. See our local page from some background information and the official University pages for – well – official details (see remarks below on core regulations however, and please take into account that core regulation updates usually are published only in August, i.e. the ones you can see are often those from the running academic year). These 15 credits are compulsory, i.e. you have to take them and there is no choice.
- All of our students do Core I (basic methods training). These 30 credits are compulsory, too.
- 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. The Core II modules are collected in List A in the core regulations.
- 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. You don’t have to formally commit to one specialisation when you sign up for the course (though we might ask for preferences). You commit to a specialisation once you are in Durham throughout induction week/prior to the first lecture day of term 1. Specialisations are lectures from List B in the core regulations.
- All of our students do a project (dissertation). Again, we have some internal info and the official module description. See also the Science and Data @ Durham rubric and its subitems for some stories of previous projects and collaborations.
|Term 1||Term 2||Term 3|
||Presentation and Ethics
|Core Modules||Core I modules
Core II modules
Please consult List A from the core regulations.
(30 or 45 credits)
Please consult list B from the core regulations.
Important links with 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.
- Further details about the individual specialisations can be found in the menu to the left.
- The menu to the left also contains further information about prerequisites.
Please note that the description below covers examples. The exact courses on offer can change each year, and there might even be changes to the course regulations. However, these examples give you a good idea what the course looks like. Please have a look at the student testimonials, too. Some of them refer to particular parts of the course.
First example (the physics guy): 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 preprocessing. 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.