Studying for an MPhil provides you with the opportunity to engage in focused research at masters level under close individual supervision. You will gain advanced expertise on your chosen range of topics in Philosophy, as well as carry out your own research project. The skills and subject knowledge your will gain will prepare you ideally for independent academic research.
Our MPhil is designed to cover the first two years of a ‘2+2’ programme (i.e. two year Masters plus two years PhD, or equivalent part-time study) towards completion of a doctorate. However, you are also welcome to undertake the 2-year course as a stand alone Masters-level degree, after which you may wish to apply for PhD study.
You will be required to study, and will receive supervisions in, three main areas of philosophy, chosen from two lists (see right hand column).
One list covers historical areas including key figures such as Plato, Descartes, Kant, Husserl and Wittgenstein, and the other covers topic areas including Moral and Political Philosophy, Aesthetics, Epistemology and Philosophy of Mind. We want to give you both a solid grounding in the historical tradition and the key skills to engage with debates in contemporary philosophy. In each of your three chosen areas, you will write two 5,000 word assessed essays, one due at the end of your first year, the other due at the end of the first term of the second year.
In the remainder of your second year, you will undertake and submit a research thesis of approximately 30,000 words. Completion of year two will earn you the degree MPhil in Philosophy. After this, you then have the opportunity to develop your research into a thesis of doctoral length and standard in years three and four of the programme (the PhD component). The programme as a whole can be tailored to your particular interests, reflecting our department’s research strengths. On completion of this programme, you should have sufficient depth in the relevant areas of Philosophy to be qualified to teach them at university level.
The primary means of study is by fortnightly supervisions, with at least one term of the first year devoted to each of the three areas of study you have chosen. You will be assigned a specialist supervisor for each area, who will agree with you topics for formative essays which you will produce for each supervision. In addition, you will be encouraged to attend appropriate graduate classes to support your supervisory preparation. Graduate classes may be taken from amongst those offered on our Masters programmes, or be research led classes put on for the benefit specifically of MPhil students and PhD students.
The MSc in Computational Mathematical Finance (CMF) is a dynamic new programme with the aim to deliver high quality training in the theory of Mathematical Finance with strong emphasis on computational methods.
Currently graduates in this field are expected to have a working knowledge of advanced computational finance (including construction of algorithms and programming skills) as well as a sound knowledge of the theory of Probability and Stochastic Analysis. These are the core theories needed in the modern valuation of complex financial instruments.
This MSc programme delivers:
You must obtain a total of 180 credits to be awarded the MSc. Over semesters 1 and 2, you will take compulsory courses worth a total of 85 credits and optional courses worth a further 35 credits. Successful performance in these courses (assessed through coursework or examinations or both) allows you to start work on a three-month dissertation project, worth 60 credits, for the award of the MSc degree.
There are two streams: the Financial stream and the Computational stream.
Compulsory courses (both streams):
Optional courses - Computational stream:
Optional courses - Financial stream:
At the end of this programme you will have:
Graduates can expect to go on to work in major financial institutions or to continue their studies by joining PhD programmes.
In this digital and data-rich era the demand for statistics graduates from industry, the public sector and academia is high, yet the pool of such graduates is small. The recent growth of data science has increased the awareness of the importance of statistics, with the analysis of data and interpretation of the results firmly embedded within this newly recognised field.
This programme is designed to train the next generation of statisticians with a focus on the newly recognised field of data science. The syllabus combines rigorous statistical theory with wider hands-on practical experience of applying statistical models to data. In particular the programme includes:
Graduates will be in high demand. It is anticipated that the majority of students will be employed as statisticians within private and public institutions providing statistical advice/consultancy.
To be awarded the MSc degree you need to obtain a total of 180 credits. All students take courses during semester 1 and 2 to the value of 120 credits which will be a combination of compulsory and optional courses. Successful performance in these courses (assessed via coursework or examinations or both) permits you to start work on your dissertation (60 credits) for the award of the MSc degree. The dissertation will generally take the form of two consultancy-style case projects or an externally supervised project.
The set of courses available is subject to review in order to maintain a modern and relevant MSc programme.
Previous compulsory courses for 2016-17:
Previous optional courses for 2016-17 include:
At the end of this programme you will have:
Trained statisticians are in high demand both in public and private institutions. This programme will provide graduates with the necessary statistical skills, able to handle and analyse different forms of data, interpret the results and effectively communicate the conclusions obtained.
Graduates will have a deep knowledge of the underlying statistical principles coupled with practical experience of implementing the statistical techniques using standard software across a range of application areas, ensuring they are ideally placed for a range of different job opportunities.
The degree is also excellent preparation for further study in statistics or data science.