Some testimonials of students (various languages incl Chinese, German and English)
- I’m really interested in machine learning and neural networks. So is this the right course for me?
MISCADA is about scientific computing and data analysis, i.e. we do cover ML but we focus not exclusively on it (we are also interested in simulations), we apply them to particular scientific problems, and we really dive into the foundations behind ML. If you want to see how we interpret ML and neural networks, have a look at this article. Though written by colleagues from another University, it sets the tone how we work in our course. If you search for a less theoretic approach to ML, then MISCADA is not the right course for you. A good test whether you are a fit to MISCADA is (a) to check that you are interested in science (and not just applying techniques) (b) to validate whether you consider yourself to be strong in C and Python programming and (c) to ask youself which specialisation you’d pick.
- I want to do the finance stream. Do I have the right qualification?
We do recommend our finance stream only for students with a math undergrad degree. You can do it with another degree, but you need a really good background in mathematics (in particular statistics) to be successful.
- I am not interested in physics or another specialisation, but in data analysis in … Can I do the course without the specialisation?
No, you can’t. If you are not interested in the specialisations and if you don’t have background knowledge in the specialisation area, then this is the wrong course for you! We target students interested in Physics or finances. The latter cohort need a very strong maths background.
- Where do graduates work?
Most of our graduates work in R&D divisions, compute centres or stay in academia. After all, we want to educate the next generation of researchers. Around 10% of our graduates continue with a PhD.
- How can I collaborate with industry? Do you collaborate with industry at all if you are a (rather pure) science course?
Our prime goal first of all is to get the fundamentals right. We do however collaborate with industry when it comes to the dissertation. Terms 1 and 2 are reserved for fundamental skills and techniques as well as for the specialisation. In term 3, many students opt for a project together with an industry partner, while others prefer a more academic route to become prepared for a future PhD. See our example routes through the course (scroll all the way own) or the info about selected projects.
- How big is the student cohort?
It is always hard to predict the bums on seats, but we are heading for a class of around 10-20 students per specialisation.
- Is there a reading list for astro- or particle-physics for non-physics students? Is there further prep work?
The curriculum tab on the top unfolds and you find some information about the different specialisations. They also comprise reading lists. There are also remarks on general prerequisites.
- Is technique/software XYZ covered in submodule Core …? Do we use … (insert MySQL, TensorFlow, Python or whatever you want) in submodule … (insert whatever you want)?
You might find information on the University’s webpages whether this particular topic has been covered in the previous year and/or used a particular technique. Usually however, these questions lead into the wrong direction. MISCADA covers fundamental concepts behind scientific computing and data analysis, i.e. behind the scenes, and from there illustrates how they are applied in state-of-the-art algorithms/software and the specialisation area. As MISCADA heads for a University degree, it is primarily about understanding concepts and methods rather than particular pieces of software or particular techniques. They might be outdated by the time you graduate. Your skills and knowledge will not. Therefore, we also change the toolset used in the course from year to year. Wherever we use specialised tools, we will introduce them throughout the course. What you need is:
- What is the most important skill I need to have (and to revise)?
Programming in C and Python. If you are not “fluent” in these programming languages (both of them!) you will struggle (see the Prerequisites rubric under Curriculum in the menu). Fluent for us means you have to be able write proper, working code for complex challenges. But you don’t need knowledge in software engineering or the development of large software packages. Some basic knowledge of object-oriented programming helps; again, no need for fancy object-oriented programming patterns or advanced meta programming or … This is not a software engineering degree. So they don’t harm, but you don’t need them. Ah: You also need maths. A lot of it! Finally, particular specialisation areas recommend that you bring in some particular knowledge already. See specialisation rubrics.
- Do you teach remotely?
Since early summer 2020, all of our lecturers are recorded and made available for offline revision/learning. We plan to return to face-to-face teaching in 2021 (the first term in AY 20/21 runs completely virtual), but we will always offer the opportunity to participate virtually in all events if you should have to self-isolate, e.g. For our labs, we realise them mainly through online servers anyway (see next item) and moving online is thus not that much of an issue for us. No matter whether teaching is in-person or virtual, there however will always be a strong synchronous element in the delivery, i.e. events, workshops and seminars that you have to attend.
- What hardware requirements are there?
If you study in Durham, you have access to our labs and our library which are equipped with all computer resources that you’ll need. Most of our teaching however is based upon online servers and particular supercomputers, i.e. you don’t have to own specialist equipment – you just need a proper browser and an internet connection. We’ll take care for the right resources under the hood. If you want to work from home without an online server – which is something we encourage you to try out at least – you’ll have to take care of your machine yourself. See some remarks at the Prerequisites page.
- When and how do I select my modules? How do I sign up for a specialisation?
In the induction week, some academics from the different specialisations will give a presentation about the exact nature of a specialisation and the content. Starting from AY 21/22, we’ll ask you for a specialisation preference when you apply for the course, but this is indicative: You will have the opportunity to ask the presenters about the specialisations throughout induction, and then you will commit to one of the routes; not before that. On the core side, there’s no choice in term 1. You have however modules to choose from in term 2. Towards the end of term 1, the programme members thus again will pitch the individual core modules from term 2 and you will be able to ask further questions – at that point, you’ve attended the majority of term 1 lectures and you will thus have a good understanding of what the course contents mean. You then have till the first day of term 2 to decide which core 2 modules you want to take in term 2. For an overview of all modules on offer, have a look at the course core regulations which you can get from here, e.g.
- What Unix version/Linux distribution should I use, what C IDE should I use, what Python packages do you recommend, what is the best book to learn …?
Such detailed questions are very much a matter of taste, and we do in general never give support for your own IT device. Please safe the questions until you are enrolled. There will be a course-specific forum where you can ask your fellow students about their preferences. We do monitor this forum (to ensure that no totally wrong things are written there), but we rely very much on the cohort spirit to answer such questions.
- to be continued …