Middlesex University London - Applied Statistics MSc/PGDip

Middlesex University London

Applied Statistics MSc/PGDip

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.

Entry Requirements

  • 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
  • We also consider candidates with other relevant qualifications and individuals with a minimum of three years work experience. Those without formal qualifications need to demonstrate relevant work experience and the ability to study at postgraduate level.
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    The most commonly accepted evidence of English language ability is IELTS 6.5 (with minimum 6.0 in all components).

Career Prospects

The MSc Applied Statistics course prepares graduates for various employment opportunities in various fields, including commerce, economics, accounting, health sciences, natural and environmental sciences, computing, engineering, law, medical statistics, medical research, and pharmaceutical industry. These professionals work in various fields, such as government, market research, and medicine effectiveness. Graduates can find employment opportunities in medical statistics, medical research, commerce, and industry, particularly in the pharmaceutical industry. They also have career opportunities in areas related to statistics, such as economics and accountancy, as well as in health sciences, natural and environmental sciences, computing, engineering, and law.

Course Details

The MSc Applied Statistics program offers a comprehensive understanding of mathematical and statistical concepts and techniques, as well as applied problems. Students will develop advanced knowledge of data collection methods, the statistical process, exploratory data analysis, statistical modelling, probability, statistical inference, and methods of analysis. The course covers big data and the use of small samples and big data to make judgments about large populations. The course will be taught through lectures, talks, workshops, seminars, and discussions, promoting critical thinking and independent reading. Assessment will be conducted through coursework, tests, and projects.
The program has developed new approaches since the 2021/22 academic year, combining pre-pandemic teaching methods with online learning and digital resources. The timetable will be built around on-campus sessions using professional facilities, with online sessions for virtual activities. Technology will be used to enhance learning and provide access to online resources.
Students will be expected to commit 1200 hours to their course across all styles of learning, with some additional hours for placements. The course structure includes live in-person on-campus learning, live online learning, and tutor set learning activities. Students are expected to learn, prepare, revise, and reflect in their own time.

Course Modules

Compulsory Modules
Data collection and analysis (15 credits)
Statistical thinking and processes (15 credits)
Statistical Modelling (15 credits)
Multivariate methods (15 credits)
Statistical Inference (15 credits)
Project (60 credits)
Optional Modules
Probability theory and mathematical analysis (15 credits)
Machine learning methods (15 credits)
Time Series and Forecasting (15 credits)
Stochastic Processes (15 credits)

Attachments

*The information’s are correct at the time of publishing, however it may change if university makes any changes after we have published the information. While we try our best to provide correct information, It is advisable to call us or visit university website for up to date information.

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