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MSc in Statistics
A flexible
taught masters course in statistics combining 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.
Commencing: September 2011
How to Apply: You can apply online by visiting this weblink:
www.leeds.ac.uk/students/apply/htm
You can also download a form for completion
by hand from this link.
Want to know more?
Continue reading this page, or contact
Ms Iwona Malinowska
Taught Postgraduate Administrator
School of Mathematics
Telephone: + 44(0) 113 343 5111
email: maths-msc@maths.leeds.ac.uk
Dr Stuart Barber
Programme manager, MSc Statistics
email: stuart@maths.leeds.ac.uk
Telephone: +44 (0) 113 343 5146
Why study for an MSc in Statistics?
There is a shortage of well-qualified statisticians in the
UK and other countries. Numeracy, in general, is an
attribute keenly sought after by employers.
The emergence of data mining and analysis means that
demand for statisticians is growing across a wide range of
professions - actuarial, betting and gaming industries,
charitable organizations, commercial, environmental,
financial, forensic and police investigation, government,
market research, medical and pharmaceutical organisations.
The course is designed specifically to meet this
demand.
Many statistical careers require people educated to
masters degree level. This course is designed to build on
existing mathematical skills and deepen knowledge of
statistics in order for you to access a variety of
professions or pursue further research as a PhD student.
The MSc is accredited by the Royal Statistical
Society. Completion of the programme will qualify students
for the status of ?Graduate Statistician? - the first
stage towards Chartered Statistician status.
The School of Mathematics
The School comprises three Departments: Applied
Mathematics, Pure Mathematics, and Statistics. Together,
they form one of the largest research schools of
mathematicians in the UK, with around 65 academic staff,
20 postdoctoral fellows and 75 PhD students.
Each Department has an enviable research reputation - each
one rated 5 ("internationally excellent") in the
2001 UK Research Assessment Exercise. In the recent 2008
RAE, the Statistics Department had 65% of its research
graded as "world leading" or "internationally
excellent".
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About the course
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MSc in Statistics
The MSc in Statistics at the
University of
Leeds is a flexible degree programme
enabling
students from a wide range of
backgrounds
to both broaden and deepen their
understanding
of statistics.
The programme provides training
in a range
of statistical techniques (and
transferable
skills) suitable for either careers
in statistics
and research-related professions,
or for
further academic research in
statistics.
Options within the course vary
from mainstream
topics in statistical methodology
to more
specialized areas and reflects
specific research
interests of academic staff within
the Department
- examples include statistical
shape analysis,
directional data, statistical
genetics and
stochastic financial modelling.
The course is a full-time MSc
lasting 12
months, starting in September.
The Diploma
is just over 8 months duration.
The Course consists of two semesters
of taught
modules, with the third semester
(MSc only)
devoted to a major dissertation.
Within each
semester there is one compulsory
module.
However, students tailor the
course to meet
their individual needs through
selection
of a further three modules from
a variety
of options. This selection process
means
a student can specialise in biological
or
financial applications of statistics
or retain
a broad base of statistical expertise.
Why study Statistics at the University of
Leeds?
Simply because you will be taught by leading
figures in their field of specialisation!
We have recently made several exciting new
appointments including Professor Wally Gilks
to head up our new Centre for Statistical
Bioinformatics.
Our School?s reputation is recognised worldwide
through research collaborations with other
universities, with government bodies, hospitals,
sectors of industry and through a variety
of interdisciplinary projects with other
departments within the University.
Our Department is home to LASR - Leeds Annual
Statistics Research workshop - an event originated
(and still led) by Professor Kanti Mardia
over 25 years ago. It has grown from a small
group of specialists sharing common interests
in development of their research into an
international platform attracting experts
at the cutting-edge of statistical research
including spatial statistics; shape analysis,
bioinformatics and image analysis.
Other areas of research include:
Modern Data Analysis including robustness
(a key concept in modern statistical modelling);
pattern recognition and machine learning;
statistical computation and wavelets.
Statistical Bioinformatics including phylogeny;
sequential analysis; structure analysis and
gene expression analysis.
Probability and Stochastic Processes comprising
parameter estimation for Markov processes;
stochastic financial models; stochastic differential
equations; propagation and extinction in
catalytic media; intermittency in random
media; limit theorems for sums and extreme
values and probabilistic methods in number
theory and combinatorics.
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The Course
A formal description of the course structure and options can be found in
the MSc Statistics entry in the University's Programme catalogue. Here is a brief summary.
Semester One
Statistical Computing
(compulsory)
An introduction to
methods of statistical
computing. Essential
for the applied statistician,
with an emphasis
on sampling-based methods,
such as Markov chain
Monte Carlo.
Optional Modules
- Multivariate and Cluster Analysis
- Statistics and DNA
- Linear Regression and Robustness and Smoothing
- Stochastic Financial Modelling
- Discrete Time Finance
- Core Epidemiology
- Advanced Epidemiology
Semester Two
Independent Learning Skills (compulsory)
An introduction to research methods including
literature search, writing styles,
mathematical typesetting and programming
skills.
Optional Modules
- Statistical Theory
- Times Series and Spectral Analysis
- Generalised Linear and Additive Models
- Continuous Time Finance
- Risk Management
- Hidden Markov Models
- Multilevel and Latent variable Modelling
- Advanced Modelling
Semester Three
Dissertation in Statistics
A four-month research project undertaken in
the Summer on a topic (chosen in conjunction
with project supervisors) culminating in a
dissertation on that project.
Example Project Topics include:
- Shape Analysis
- Spatial Statistics
- Robustness
- Data Mining
- Wavelets
- Bioinformatics
The taught course is primarily assessed by
end-of-semester examinations with a small
component of continuous assessment. The
Semester Three project is assessed by a
written dissertation and a short oral
presentation.
Optional Module Descriptions
Multivariate and Cluster Analysis
Some common methods, like t-tests, can be
extended to the multivariate setting and many
new approaches are now available. Principal
components analysis and factor analysis look
for ?interesting? combinations of the
variables; discriminant analysis tries to
separate different groups of data; cluster
analysis tries to find natural groupings in
the data.
Statistics and DNA
Modern
biological experiments produce large data sets
involving information related to DNA. This
module gives the basic biological background
before looking at a range of data types and
methods to analyse them.
Linear Regression and Robustness and Smoothing
A
fundamental statistical tool is the simple
linear regression model, which predicts the
value of a normally-distributed response
variable from a predictor variable. This
module explores many ways to extend this
simple model to cope with non-linear
relationships and data corrupted by non-normal
errors.
Stochastic Financial Modelling
Financial investments such as stocks and
shares are risky: their value can go down as
well as up. To compensate for the risk in a
fair market, a discount is needed. This module
will develop the necessary probablistic tools
to enable investors to value such assets.
Discrete Time Finance
This module
develops a general methodology for the pricing
of financial assets in risky financial markets
based on discrete time models.
Shape Analysis and Directional Data
The "shape" of an object is the
information that doesn't change when the
object is moved, rotated, enlarged or reduced.
Specialised statistical methods are needed to
study shape variation.
Statistical Theory
We often use
statistical tests and estimators without fully
exploring the theoretical basis for their
use. Here, we look more deeply into the
mathematics behind statistical inference and
compare the two main approaches to statistics:
Frequentist and Bayesian inference.
Time Series and Spectral Analysis
In time series, measurements are made at a
succession of times, and it is the dependence
between measurements taken at different times
which is important. We concentrate on
techniques for model identification, parameter
estimation and forecasting future values of
the time series.
Continuous Time Finance
Continuous
time models play a central role in pricing of
financial assets under more challenging
circumstances than can be handled with
discrete time models.
Risk Management
This module gives
comprehensive coverage of mathematical and
practical approaches to financial risk
management. Avoiding the disastrous
consequences of badly managed risk requires
detailed mathematical knowledge of how to
quantify financial risk and stress-test a
hedge.
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Other Information
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Opportunities for postgraduate research in the Department
of Statistics
Department of Statistics web pages
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University of Leeds |
Last modified: Mon Aug 16 10:57:49 BST 2010
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