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Applied Statistics MSc/PGDip

Learn about the course below
September 2023
1 year full-time
2 years part-time
£10,500 (UK) *
£15,700 (EU/INT) *
Course leader
Nicholas Sharples
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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. You'll also learn how to use standard statistical software like R, 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, including being able to utilise our subscriptions to Bloomberg and Datastream.
  • As a student of this course you'll receive a free electronic textbook for every module.
  • The programme provides a practical guide to the overall statistical process including how to set up research aims and objectives, reviewing literature, how to collect data, how to analyse data using a variety of techniques, and writing up your results.
  • Options in the topics such as Machine Learning Methods and Time Series and Forecasting.

*Please note this course is subject to review.

Find out more

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What will you study on the MSc Applied Statistics?

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 data collection methods, the statistical process, exploratory data analysis, statistical modelling, probability, statistical inference and methods of analysis, and will work on applied problems. 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.

  • Modules

    • Data collection and analysis (15 credits) - Compulsory

      This module aims to give students a solid grounding in how to collect data and how to apply some of the main analysis techniques to data. The methods and techniques covered have a wide-range of applications from business to medicine.

    • Statistical thinking and processes (15 credits) - Compulsory

      This module aims to give students a practical grounding in the overall statistical process, including specifying a research problem, reviewing literature, understanding the main data collection methods, ethical considerations, preparing data, conducting an exploratory data analysis and communicating findings in a written report.

    • Statistical Modelling (15 credits) - Compulsory

      This module develops students understanding of the general linear model. The explicit links between ANOVA and regression will be made clear as students are introduced to more complex forms of both, including repeated measures analysis. Model selection and standard validation methods will be explored. These techniques will be extended to include generalised linear models with categorical outcomes Multilevel modelling will extend both of these to include data with a nested structure

    • Probability theory and mathematical analysis (15 credits) - Optional

      In this module you will refresh your mathematical skills for studying probability and statistics at level 7. We will then build a thorough grounding in probability theory developing both intuition and rigorous analytic tools to formulate and solve a variety of practical problems in probability.

    • Multivariate methods (15 credits) - Compulsory

      This module aims to give students a thorough grounding in the main multivariate techniques. It will develop students appreciation for critically analysing multivariate data and provide a keener understanding of the structures that underlie data observations.

    • Statistical Inference (15 credits) - Compulsory

      This module aims to introduce students to advanced techniques in statistical inference, looking at both the theory, but also practical examples. The module covers mainly the classical approach to inference, but there is also an introduction to the Bayesian approach. Different estimation techniques such as MLE and MoM will be presented. Statistical inference relating to point estimation, hypothesis testing and interval estimation is considered along with both parametric and non-parametric procedures.

    • Project (60 credits) - Compulsory

      The project allows students to consolidate their learning in a substantial piece of independent work utilising the skills and knowledge developed in the taught content. Students have the opportunity in this module to study a problem that interests them and that requires further study, allowing students to demonstrate expertise in problem definition, research design, analysis and critical presentation of the results.

    • Machine learning methods (15 credits) - Optional

      Machine learning methods are becoming increasingly popular for handling very large data sets, and modelling for relatively small or hidden effects. They are especially valuable when the predictive ability of a model may be more important than its explanatory power. This module introduces students to some of the most popular machine learning methods, so extending students understanding of modelling techniques beyond the standard statistics practice covered in MSO4353 Multivariate methods and MSO4355 Statistical modelling. The appropriate data preparation and subsampling techniques will be presented, along with both the advantages and limitations of these modelling methods.

    • Time Series and Forecasting (15 credits) - Optional

      Time series are extremely common. This module aims to build models for time series data with a view to forecasting future values of the series. The purpose of time series is generally twofold: to understand or model the mechanism that gives rise to an observed series and to predict or forecast future values of the series based on the history of that series and, possibly other related series or factors.

    • Stochastic Processes (15 credits) - Optional

      In this module you will build a solid grounding in modern topics and techniques in stochastic processes, developing both intuition and rigorous analytic tools to formulate and solve a variety of practical problems.

You can find more information about this course in the programme specification. Module and programme information is indicative and may be subject to change.

We are regularly reviewing and updating our programmes to ensure you have the best learning experience. We are taking what we've learnt during the pandemic and enhancing our teaching methods with new and innovative ways of learning. Please regularly check this section of the course page for updates.

How will the MSc Applied Statistics be taught?

Lectures and talks will introduce you to concepts and techniques, which you’ll explore further through workshops, seminars, and discussions in class. Examples will help you to relate theory to practice, and we’ll encourage you to think critically. You’ll supplement all this with your own independent reading and study, including the use of online resources.


You’ll be assessed through coursework, tests and your project.

Teaching and learning from 2022

We have developed new approaches to teaching and learning since the 2021/22 academic year, and have resumed the majority of our teaching on campus.

We are currently reviewing our approach to teaching and learning for 2023 entry and beyond. We've learned a lot about how to give you a quality education - we aim to combine the best of our pre-pandemic teaching and learning with access to online learning and digital resources which put you more in charge of when and how you study. We will keep you updated on this throughout the application process.

Your timetable will be built around on campus sessions using our professional facilities, with online sessions for some activities where we know being virtual will add value. We’ll use technology to enhance all of your learning and give you access to online resources to use in your own time.

The table below gives you an idea of what learning looks like across a typical week. Some weeks are different due to how we schedule classes and arrange on campus sessions.

This information is likely to change slightly for 2023 entry as our plans evolve. You'll receive full information on your teaching before you start your course.

Learning structure: typical hourly breakdown in 2022/23

Live in-person on campus learning

Contact hours per week, per level:

9 hours

Live online learning

Average hours per week, per level:

3 hours

Outside of these hours, you’ll be expected to do independent study where you read, listen and reflect on other learning activities. This can include preparation for future classes. In a year, you’ll typically be expected to commit 1200 hours to your course across all styles of learning. If you are taking a placement, you might have some additional hours.

Definitions of terms

  • Live in-person on campus learning – This will focus on active and experiential sessions that are both:
    • Led by your tutors including seminars, lab sessions and demonstrations We’ll schedule all of this for you
    • Student-led by you and other students.
  • Live online learning – This will include lectures, tutorials, workshops and supervision sessions led by your tutor and timetabled by us. It also includes student-led work that takes place online
  • Tutor set learning activities – This covers activities which will be set for you by your tutor, but which you will undertake in your own time. Examples of this include watching or working through online materials, participating in an online discussion or reading specific texts. You may be doing this by yourself or with your course mates depending on your course and assignments. Outside of these hours, you’ll also be expected to do further independent study where you’ll be expected to learn, prepare, revise and reflect in your own time.


You have a strong support network available to you to make sure you develop all the necessary academic skills you need to do well on your course.

Our support services will be delivered online and on campus and you have access to a range of different resources so you can get the help you need, whether you’re studying at home or have the opportunity to come to campus.

You have access to one to one and group sessions for personal learning and academic support from our library and IT teams, and our network of learning experts. Our teams will also be here to offer financial advice, and personal wellbeing, mental health and disability support.

  1. Standard entry requirements
  2. International (inc. EU)
  3. How to apply
  1. UK
  2. EU/International
  3. Additional costs
  4. Scholarships and bursaries

How can the MSc Applied Statistics support your career?

There is a need in both the public and private sectors for well-qualified statisticians and this course will leave you ideally placed for a wide variety of employment opportunities in:

  • commerce
  • economics
  • accountancy
  • health sciences
  • natural and environmental sciences
  • computing
  • engineering
  • law
  • medical statistics
  • medical research
  • pharmaceutical industry

You might also wish to explore your options in research or academia, or even complete further study at doctoral level.

Statisticians work in many fields, from government to market research, measuring anything from changes in the environment revealing the effects of global warming to the effectiveness of medicines. There are a large number of employment opportunities for our graduates in medical statistics, medical research, commerce and industry, particularly the pharmaceutical industry. There are also many career opportunities both in areas directly related to statistics, such as economics and accountancy, and wider field in areas like health sciences, natural and environmental sciences, computing, engineering and law.

We’ll carefully manage any future changes to courses, or the support and other services available to you, if these are necessary because of things like changes to government health and safety advice, or any changes to the law.

Any decisions will be taken in line with both external advice and the University’s Regulations which include information on this.

Our priority will always be to maintain academic standards and quality so that your learning outcomes are not affected by any adjustments that we may have to make.

At all times we’ll aim to keep you well informed of how we may need to respond to changing circumstances, and about support that we’ll provide to you.

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