Postgraduate Taught Degrees

MSc Statistics

Duration:: 1 years
Attendance:: Full Time
Award:: Degree of Master of Science
School of Study:: Computing Sciences
Brochure:: Faculty of Science Postgraduate Brochure (PDF)

Overview
Why Choose Us
Course Profile
Requirements
Fees and Funding
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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

Course Organiser:Prof Elena Kulinskaya

95% of research activity was classified as internationally leading, excellent or recognised in the 2008 Research Assessment Exercise.
Our Masters programmes are accredited by the BCS - The Chartered Institute for IT to full Chartered IT Professional (CITP Further Learning Element) as well as leading to Chartered Engineer (CEng) status.
The School maintains close links with industry and many of our student assignments relate to real world problems.
The School of Computing Sciences leads the university in utilizing its internationally recognized research commercially, through SYS Consulting, its Consulting company and through Knowledge Transfer Partnerships.
Our students also use their technical and entrepreneurial skills to play influential roles in the creation of companies such as Travel Republic, Evoke systems, Fyfo and Shoretech systems.

Come and Visit Us

Our Open Days will give you the opportunity to experience the University of East Anglia's unique campus atmosphere.

Year 1

Compulsory Study (140 credits)

Students must study the following modules for 140 credits:

Name	Code	Credits
COMPUTATIONAL STATISTICS	CMPSMA17	20
This module covers modern statistical methodology and focuses on statistical inference using computer intensive methods. We shall aim at understanding computer techniques which require innovative algorithms to implement inference. More...
DISSERTATION	CMPSMP6X	60
Reserved for postgraduates in the School of Computing Sciences, this module provides the student with a piece of individual work, with substantial research and practical elements. The subject of the dissertation will be determined by agreement between the student and his or her supervisor. The work may be undertaken as part of a large collaborative or group project. More...
MULTIVARIATE ANALYSIS	CMPSMA18	20
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...
STATISTICAL INFERENCE	CMPSMA19	20
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...
STATISTICAL MODELLING TBA	CMPSMA13	20
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...

Option A Study (40 credits)

Students will select 40 credits from the following modules:

Name	Code	Credits
APPLIED ECONOMETRICS	ECO-M002	20
This is a graduate-level unit in applied econometrics, aimed at students with some prior knowledge of econometrics. The unit is divided into four modules: panel data, simultaneous equation models, time series analysis and microeconomics. There is an emphasis on the practical side, with the specialist econometric package STATA being used extensively. Assessment is by take-home assignments and computer test.
ARTIFICIAL INTELLIGENCE AND ALGORITHMICS	CMPSMA24	20
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...
CLINICAL TRIALS: DESIGN AND ANALYSIS	MED-M39E	20
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
DATA MINING	CMPSMC24	20
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 data gathering, preliminary data anaylsis or 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 each stage of the process, as well as practical experience using leading KDD software packages throughout all of the stages of KDD process. More...
EPIDEMIOLOGY: DESIGN AND ANALYSIS	MED-M40E	20
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
FINANCIAL MATHEMATICS	ECO-M022	20
This is a technical finance unit 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 unit 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; "Value at Risk" analysis and Monte-Carlo methods.

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.

Entry Requirements

Degree Subject:: Mathematics, Statistics, Operational Research or a related subject
Degree Classification:: Good first degree (minimum 2.1 or equivalent).

Students for whom English is a Foreign language

We welcome applications from students whose first language is not English. To ensure such students benefit from postgraduate study, we require evidence of proficiency in English. Our usual entry requirements are as follows:

IELTS: 6.5 (minimum 6.0 in all components)
TOEFL: Internet-based score of 88 (minimum 18 in listening, 21 speaking, 19 writing and 20 reading)
PTE (Pearson): 62 (minimum 55 in all components)
Test dates should be within two years of the course start date.

Other tests such as TOEIC and the Cambridge Certificate of Advanced English are also accepted by the university. Please check with the Admissions Office for further details including the scores or grades required.

INTO UEA and INTO UEA London run pre-sessional courses which can be taken prior to the start of your course. For further information and to see if you qualify please contact intopre-sessional@uea.ac.uk (INTO UEA Norwich) or pseuealondon@into.uk.com (INTO UEA London).

Fees and Funding

Tuition Fees 2013/14

UK/EU £5,000
International £12,500

Funding

International applicants applying to this course can be considered for one Faculty of Science half fees scholarship or one £2000 scholarship. The deadline is 1st April 2013.

Faculty of Science Scholarships
Students wishing to apply should submit an essay answering the following question in 1000 words: 'Statistics as the science of learning from Data (Jon Kettering, ASA President, 1997). Discuss'. Essays should be emailed to the Admissions Office. Please ensure you include your full name, the course you have applied to, and your applicant number in your email.

For more information please contact the Computing Sciences Postgraduate Admissions Office (cmp.pgt.admiss@uea.ac.uk).

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 application form.

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.