Overview Often known as the science of uncertainty, statistics is of vital importance in modern society where almost all sectors rely on the collection, analysis and interpretation of data. There is a great shortage of well qualified statisticians, data analysts and statistical consultants across the sector and this course has been specifically designed to meet that demand. Why study MSc Applied Statistics at Middlesex University? Applied statistics involves putting theory into practice - not only summarising and describing data, but extrapolating from it to draw conclusions about the population being studied. This is an applied, practically-orientated course that gives you advanced knowledge of statistical methods and the theory that underpins these methods. With a strong emphasis on relating theory to practice, you will develop your analytical, logical, numerical and problem-solving, skills that are in such high demand with employers. Middlesex is one of only four institutions in the UK which has been approved as a mirror for the statistical programming language R – meaning you will have access to software used in the industry. You'll also learn how to use standard statistical software like SPSS and Minitab. You'll have the freedom to choose the type of independent research project you do which can take the form of a theoretical dissertation, a survey or a more practical project involving a data set. If you're working, you'll have the option of basing your project at your workplace – making your studies even more relevant and beneficial for both you and your employer. Course highlights
- You will be able to work with real datasets by utilising our subscriptions to Bloomberg and Datastream
- As a student of this course you'll receive a free electronic textbook for every module.
Statistical Modelling (30 credits) - Compulsory Probability and Stochastic Processes (30 credits) - Compulsory Inference Theory (15 credits) - Compulsory Descriptive Statistical Analysis (15 credits) - Compulsory Time Series and Forecasting (15 credits) - Compulsory Data Mining (15 credits) - Optional Survival Analysis (15 credits) - Optional
You’ll be assessed through exams, tests and your dissertation, as well as other individual and group coursework.
We normally require a second class honours degree 2:2 or above, in an appropriate subject with a significant amount of mathematics in its curriculum
English language requirements
|With at least 6.0 in each component
|With at least 21 in listening & writing, 22 in speaking and 23 in reading
|With at least 51 in all components
Fees and funding
*This is a provisional fee and subject to change.