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MSc Applied Statistics

Course Overview

Often known as the science of uncertainty, statistics - the study of the collection, analysis, interpretation and presentation of data – is a subject that has an impact in almost all sectors of society. Applied statistics involves putting the theory into practice - not only summarising and describing data, but extrapolating from it to draw conclusions about the population being studied. Social policy, medical practice and engineering all rely substantially on statistics and their correct use and interpretation; its impact can be life-saving.

This practically-orientated course gives you the skills employers are looking for – not just in terms of statistical knowledge, but transferable skills which will be useful whatever field you decide to work in. There is a shortage of well-qualified statisticians across the scientific, industrial and public sectors and a great demand for data analysts and statistical consultants, which this course has been specifically designed to meet.

Key information

    Course name

    MSc Applied Statistics

    Total academic credits

    180 credits

    Qualification awarded


    Awarding body

    Middlesex University

    Professional recognition


    Academic level

    Postgraduate (QCF_NQF Level 7)

    Study mode

    Full time


    1 year

    Tuition fees




    Work placement


    Course location


Academic entry requirements

• A good honours degree, or equivalent qualification, in an appropriate subject.

• International students: equivalent of the above qualifications from a recognised overseas qualification.

• Candidates with other degrees are welcome to apply provided they can demonstrate appropriate levels of experience.

• Candidates without formal qualifications need to demonstrate relevant work experience and the ability to study at postgraduate level.

English entry requirements

Students for whom English is not the first language must satisfy the University requirement for IELTS currently at 6.5 overall, with no less than 6.0 in any component. If you don't meet our minimum English language requirements, you may be able to take an intensive Pre-sessional English course.

Progression route

Further academic study at postgraduate level including MPhil, DBA, PhD or professional career.

Career opportunities

Graduates of the programme will be equipped as a statistician and a data analyst for careers in engineering, computing and communications sciences, natural and environmental sciences, health and social sciences, economics and finance.



  • Probability and stochastic processes (30 credits)
  • Inference theory (15 credits)
  • Descriptive statistical analysis (15 credits)
  • Statistical modelling (30 credits)
  • Time series and forecasting (15 credits)
  • Project (60 credits)



  • Survival analysis (15 credits)
  • Data mining (15 credits)


Assessment methods
You’ll be assessed through exams, tests and your dissertation, as well as other individual and group coursework.


Course highlights

  • This is an applied course, with a strong emphasis on relating theory to practice, which aims to develop the analytical, logical, numerical and problem-solving skills that are in such 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 Middlesex students have access to this software and can use the same systems that are used in industry
  • If you're working, you'll have the option of basing your independent project at your workplace – making your studies even more relevant and beneficial for both you and your employer. You'll have quite a free choice over what sort of project you do – it could be a more theoretical dissertation, a survey or a more practical project involving a data set, whatever suits your interests and skills
  • The university intends to have the course accredited by the Royal Statistical Society so you will be granted Graduate Statistician status when you complete it
  • The university’s subscriptions to Bloomberg and Datastream allow you to work with real datasets. You'll also learn how to use standard statistical software like SPSS and Minitab
  • As a student of this course you'll receive a free electronic textbook for every module


Aims of the programme

The programme aims to:

  • develop awareness and understanding, at an advanced level, of mathematical and statistical concepts and techniques in order to apply them to cross-sectional, time-series, longitudinal, multi-level, spatial and event-oriented data sets
  • develop an advanced knowledge of probability, distributions, inference and stochastic processes, statistical modelling and analysis in order to solve problems in engineering, computing and communications sciences, natural and environmental sciences, health and social sciences, economics and finance
  • develop the ability to work independently and as part of a team on highly technical problems requiring statistical techniques and to communicate the results to a wide range of audiences


Main learning outcomes

You’ll gain a thorough understanding of mathematical and statistical concepts and techniques and how to apply them to data sets. You’ll develop an advanced knowledge of probability, distributions, inference and stochastic processes, statistical modelling and methods of analysis, and will work on highly technical problems both independently and as part of a team. You’ll learn how to obtain different types of data from a variety of sources, including electronic databases; analyse it using programming and computer packages; and compare and choose between different methods of modelling and analysis. The course also covers big data, and the use of both small samples and big data to make judgments about large populations.


Knowledge and understanding

On completion of this programme the successful student will have knowledge and understanding of:

  • advanced techniques in statistics
  • various types of data
  • theories and methods for modelling and analysing complex data sets and their relative merits
  • core concepts and theories of probability and stochastic processes
  • core concepts and theories of advanced techniques inference
  • research methods and techniques


Cognitive (thinking) skills

On completion of this programme the successful student will be able to:

  • explain and evaluate methods for analysing and modelling complex data sets
  • explain and critically compare competing methods for modelling data
  • use advanced methods of stochastic analysis and applied probability to formulate and solve practical problems
  • identify suitable techniques and justify their appropriateness to solve technical applied problems
  • effectively organise, structure and produce a project at an advanced level


Practical skills

On completion of the programme the successful student will be able to:

  • obtain and critique data from a range of sources including electronic databases
  • analyse data using both programming and computer packages
  • use a variety of advanced statistical techniques to model data
  • formulate and solve practical problems
  • apply advanced statistical theory to practice in a variety of settings