Statistics

Leeds University Business School

Postgraduate

In Leeds

Price on request

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Description

  • Type

    Postgraduate

  • Location

    Leeds

  • Start date

    Different dates available

The MSc in Statistics is a flexible degree programme enabling students from a wide range of backgrounds to both broaden and deepen their understanding of statistics.
The programme combines in-depth training in mainstream advanced statistical modelling with a broad range of specialisations - from financial mathematics to statistical bioinformatics; from shape analysis to risk management. You’ll also develop your understanding of research methods in statistics from writing styles to programming skills, preparing you for a wide range of careers in different sectors. You’ll apply these skills in an independent research project.
If you do not meet the full academic entry requirements then you may wish to consider the Graduate Diploma in Mathematics. This course is aimed at students who would like to study for a mathematics related MSc course but do not currently meet the entry requirements. Upon completion of the Graduate Diploma, students who meet the required performance level will be eligible for entry onto a number of related MSc courses, in the following academic year.
Accreditation
This programme is accredited by the Royal Statistical Society under the condition that the final degree transcript shows at least 60 statistics credits in year 3.

Facilities

Location

Start date

Leeds (North Yorkshire)
Maurice Keyworth Building, The University Of Leeds, LS2 9JT

Start date

Different dates availableEnrolment now open

About this course

Entry requirements
A bachelor degree with a 2:1 (hons) in a relevant subject.
We accept a range of international equivalent qualifications.
English language requirements
IELTS 6.5 overall, with no less than 6.0 in all components. For other English qualifications, read English language equivalent qualifications.
Improve your English
If English is not your first language, you may be able to take a pre-sessional course before you begin your studies. This can help if you:
don't meet the English language requirements for your course or ss, contact the admissions team for help.
Documents and...

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This centre's achievements

2018

All courses are up to date

The average rating is higher than 3.7

More than 50 reviews in the last 12 months

This centre has featured on Emagister for 14 years

Subjects

  • Risk
  • Risk Management
  • Statistics
  • Mathematics
  • Financial
  • Project
  • Writing
  • IT risk
  • Financial Training
  • Programming
  • GCSE Mathematics

Course programme

The first two semesters of your course will consist of taught modules and in the third semester you will devote your time to a major dissertation.

Within each semester there is one compulsory module and a range of optional modules, allowing you to specialise in the area of statistics of most interest to you. Specialist areas within the course include biological or financial applications of statistics or broad based statistical expertise. The core modules will develop your skills to lay the groundwork of the programme. You’ll learn a range of statistical computing techniques and build research skills such as academic writing, programming and literature searches. Options within the course vary from mainstream topics in statistical methodology to more specialised areas and reflect specific research interests of our academic staff - examples include statistical shape analysis, directional data, statistical genetics and stochastic financial modelling.

Course structure

These are typical modules/components studied and may change from time to time. Read more in our Terms and conditions.

Modules Year 1

Compulsory modules

  • Independent Learning and Skills Project 15 credits
  • Statistical Computing 15 credits
  • Dissertation in Statistics 60 credits
Optional modules
  • Introduction to Clinical Trials 15 credits
  • Introduction to Health Data Science 15 credits
  • Further techniques in Health Data Analytics 15 credits
  • Modelling Strategies for Causal Inference with Observational Data 15 credits
  • Latent Variable Methods 15 credits
  • Mathematical Biology 15 credits
  • Evolutionary Modelling 15 credits
  • Linear Regression and Robustness 15 credits
  • Statistical Theory 15 credits
  • Stochastic Financial Modelling 15 credits
  • Multivariate Analysis 10 credits
  • Time Series 10 credits
  • Bayesian Statistics 10 credits
  • Generalised Linear Models 10 credits
  • Introduction to Statistics and DNA 10 credits
  • Discrete Time Finance 15 credits
  • Continuous Time Finance 15 credits
  • Risk Management 15 credits
  • Advanced Mathematical Biology 20 credits
  • Advanced Evolutionary Modelling 20 credits
  • Linear Regression, Robustness and Smoothing 20 credits
  • Multivariate and Cluster Analysis 15 credits

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