Overview
Why take this course?
Statistics is an exciting subject with a rapidly increasing uptake in industry, commerce, government and academia. More and more professions, from the everyday to the exotic, depend on data and numerical reasoning. Data are not just numbers, but numbers that carry information about a specific setting and need to be interpreted within that setting. With the growth in the use of data comes a growing demand for the services of statisticians, although, as stated by the Royal Statistical Society, there is a lack of statisticians across all areas of applications in the UK and internationally. The need is especially pronounced in the finance, insurance and pharmaceutical industries. To help meet the demand for well trained statisticians, the School of Computing Sciences, in close collaboration with the School of Medicine and the School of Economics, offers a 1-year MSc in Statistics. Students will have the ability to choose specialist pathways out of medical, finance/insurance or knowledge discovery. The programme will prepare you for employment across a range of industries and for further education through a PhD in order to pursue a career in industrial or academic research.
Contact time
Students have on average 15 hours of contact time per week with teaching staff through lectures, laboratory sessions and seminars, though this may vary depending on module choices. Additionally, students should allocate at least 25 hours per week for study, coursework assignments and projects.
Teaching and Assessment
The MSc Statistics is normally a 12-month full-time intensive course but may be studied part-time over 24 months.
The course develops statistical thinking and provides training in the advanced aspects of statistical theory as well as familiarising students with applications of statistics and exposure to practical problems. Particular attention is given to statistical computing, with a core module in the first semester, and exposure to R and SAS statistical programming languages throughout the course. SAS is an industry standard both in pharmaceutical and in the financial services industry. R is a free flexible statistical environment used extensively in academia and in Research and Development.
The course is broadly divided into three parts. The first part is a set of core modules that are taught in the first semester. These include Computational Statistics, Inference, Statistical Modelling and Multivariate Analysis. The second part comprises specialised pathways in either financial statistics, medical statistics or knowledge discovery and takes place in the second semester. This allows students to build on or broaden their existing knowledge. The third part of the course is a three-month research project that is written up in the form of a dissertation. This may be based on a placement in a pharmaceutical or financial/insurance company. Part-time students follow a similar structure, except that the modules are spread over four semesters plus two summers.
Career opportunities
The School has an excellent record in both post-BSc and post-MSc placements and most of our recent graduates have found jobs.
SAS actively supports this new MSc in Statistics degree and is delighted that the University of East Anglia is expanding its portfolio of statistics courses. The University of East Anglia's reputation for research excellence means that SAS customers in the financial services and pharmaceutical industries will know where to look for their recruitment needs.
Geoffrey Taylor - SAS Academic Programme Manager
Please click here to view the course profile
Course Organiser Prof. Elena Kulinskaya
Course Organiser
Prof Elena Kulinskaya
Why Choose Us?
- 90% of research activity classified as internationally leading, excellent or recognised in the 2008 Research Assessment Exercise.
- Teaching of the highest quality; rated “Commendable” in the most recent Teaching Quality Assessment.
- In the last National Student survey, rated 26th out of over 200 computing departments in England, Scotland and Wales for overall satisfaction.
- In March 2009 the British Computer Society (BCS) renewed accreditation for taught programmes for five years.
- The School has its own consultancy company, SYSCO, through which it maintains close links with industry.
- Some of the projects in the taught programmes may be done in collaboration with industry and could involve paid placements.
Come and Visit Us
Our
Open Days will give you the opportunity to experience the University of East Anglia's unique campus atmosphere.
Entry Requirements
-
Good first degree (minimum 2.1 or equivalent) in Mathematics, Statistics, Operational Research or a related subject at bachelor level.
Students for whom English is a foreign language
International applicants are required to provide evidence of proficiency in the English language (if English is not their first language).
Preferred qualifications are:
- IELTS Minimum score of 6.5 with a minimum of 6.0 in each component
- TOEFL Minimum score of 92 (internet based test)
- Pearsons Test of English (PTE) Minimum score of 62 with no less than 55 in each component
Applicants who have previously studied in the English language may not be required to provide evidence of English language ability.
Year 1
Compulsory Study (140 credits)
Students will select 140 credits from the following module(s).
| Code |
Credits |
Period |
This module focuses on computers on statistical inference using computer intensive methods. We shall aim at understanding computer techniques which require innovative algorithms to apply frequentist and Bayesian inferences.
more...
|
CMPSMA17 |
20 |
Semester 1 |
In this module, Masters students are required to carry out project work with substantial research and practical elements on a specified topic for their MSc dissertation. The topic can be chosen and allocated from the lists of proposals from faculty members, and/or determined by agreement between the students and their supervisor. The work may be undertaken as part of a large collaborative or group project. A dissertation must be written as the outcome of the module.
more...
|
CMPSMP6X |
60 |
Semester 2 |
This module covers main areas of contemporary multivariate statistical analysis: multivariate linear models (regression, MANOVA, repeated measures) , methods of dimensionality reduction (PCA, canonical correlations, multidimensional scaling) and introduction to modelling of multivariate dependent data with copulas. Both the theory and practice of statistical analysis is considered and students will be expected to analyse real data. Multivariate methods are a vital part of the statistician's toolbox.
more...
|
CMPSMA18 |
20 |
Semester 2 |
This module covers the foundations of statistical inference, both frequentist and Bayesian, including the definitions of random variables and distributions, moments and moment generating functions, principles of statistical estimation and testing.
more...
|
CMPSMA19 |
20 |
Semester 1 |
This module covers three topics in statistical theory: Regression and Linear Models, Generalised Linear Models and Non-parametric and Robust Methods. All topics consider both the theory and practice of statistical model fitting and students will be expected to analyse real data. Non-parametric and robust methods are a vital part of the contemporary statisticians armoury and cheap computing makes such techniques very powerful. We look at the permutation based methods, the empirical distribution function, M- and L-estimation.
more...
|
CMPSMA13 |
20 |
Semester 1 |
Option A Study (40 credits)
Students will select 40 credits from the following module(s).
| Code |
Credits |
Period |
This module builds on the econometric theory of earlier courses: ECO-M001, ECO-M017 and ECO-M003. It attempts to place the theoretical ideas of those modules in the context of current applied analysis. The module is divided into five main parts: data issues and distribution theory time series econometrics, estimation of systems of equations, microeconomics, and panel data models. There is an emphasis on the practical application of common estimation techniques, with the specialist econometric software package STATA being used extensively. These skills are assessed in an applied project at the end of the course.
more...
|
ECO-M002 |
20 |
Semester 2 |
This module introduces the student to core techniques in Artificial Intelligence and some topics in algorithmics. Topics to be covered include state space, search techniques, algorithmic paradigms, NP-completeness, metaheuristics, logic and knowledge representation and expert systems.
more...
|
CMPSMA24 |
20 |
Semester 2 |
By the end of this module, students should be familiar with the basic concepts underpinning clinical trials, common trial designs and approaches to data analysis. Student should be able to carry out sample size calculations in a variety of situations and provide statistical input to clinical trial proposals. They should be able to formulate statistical analysis plans for the more common design types and conduct appropriate analyses in SAS.
Content:
1. Introduction to Clinical Trials
- history, types of trial and essential features of trial designs
2. Computer Lab: Introduction to SAS software
3. Parallel Group Trials
4. Cross-Over Trials
5. Statistical Power and Sample Size calculations
6. Multiple outcomes in clinical trials
more...
|
MED-M39E |
20 |
Semester 2 |
This module is designed for postgraduate students studying on MSc courses. The module explores the methodologies of Knowledge Discovery and Data Mining (KDD). It aims to cover each stage of the KDD process, including preliminary data exploration, data cleansing, pre-processing and the various data analysis tasks that fall under the heading of data mining. Through this module, students should gain knowledge of algorithms and methods for data analysis, as well as practical experience using leading KDD software packages.
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|
CMPSMC24 |
20 |
Semester 2 |
By the end of the module, students should have a good grasp of the basic design features of the more commonly used epidemiological study designs and the commonly used epidemiological measures. They should be able to contribute to the statistical design and analysis sections of protocols for epidemiological studies. They should be able to analyse data using SAS from a variety of commonly encountered epidemiological studies.
Content:
1. What is epidemiology and basic study design
2. Diagnostic studies
3. Cohort studies
4. Case-control studies
5. Case-control studies
6. Survival Data 1
7. Survival Data 2
8. Survival Data 3
9. Lab: SAS and logistic regression
10. LAB: SAS and Poisson regression plus survival analysis
more...
|
MED-M40E |
20 |
Semester 2 |
This is a technical finance module aimed at students wishing to pursue careers in the financial sector. The focus will be on valuation and risk analysis of financial products and positions. The module will be highly analytical, with weekly exercises and assessment balancing mathematical problems and practical exercises involving Excel. Topics covered will include: present value calculation; bond analysis; futures markets; interest rate futures and yield curve analysis; option pricing and hedging; exotic options; Swaps; Martingales.
more...
|
ECO-M022 |
20 |
Semester 2 |
Disclaimer
Whilst the University will make every effort to offer the modules listed, changes may sometimes be made arising from the annual monitoring, review and update of modules and regular (five-yearly) review of course programmes. Where this activity leads to significant (but not minor) changes to programmes and their constituent modules, there will normally be prior consultation of students and others. It is also possible that the University may not be able to offer a module for reasons outside of its control, such as the illness of a member of staff or sabbatical leave. Where this is the case, the University will endeavour to inform students.
How To Apply
Applications for Postgraduate Taught programmes at the University of East Anglia should be made directly to the University.
You can
apply online, or by downloading the
hard copy application form, or by using the application form in the University’s Postgraduate Prospectus.
Further Information
To request further information & to be kept up to date with news & events please use our
online enquiry form.
If you would like to discuss your individual circumstances prior to applying please do contact us:
Postgraduate Admissions Office
Tel: +44 (0)1603 591515
Email:
admissions@uea.ac.uk
International candidates are also encouraged to access the
International Students section of our website.
