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Statistical science skills are powerful tools that play a valuable role in all pure and applied sciences as well as in finance, law and marketing. Read more
Statistical science skills are powerful tools that play a valuable role in all pure and applied sciences as well as in finance, law and marketing. New and exciting opportunities in industry, medicine, government, commerce or research await the graduate who has gained the quantitative skills training provided by this MSc.

Degree information

The programme uses a broad-based approach to statistics, providing up-to-date training in the major applications and an excellent balance between theory and application. It covers modern ideas in statistics including applied Bayesian methods, generalised linear modelling and object-oriented statistical computing, together with a grounding in traditional statistical theory and methods.

Students undertake modules to the value of 180 credits.

The programme consists of a foundation module, four core modules (60 credits) four optional modules (60 credits) and a research dissertation (60 credits).

Core modules
-Foundation Course (not credit bearing)
-Statistical Models and Data Analysis
-Statistical Design of Investigations
-Statistical Computing
-Applied Bayesian Methods

Optional modules
-Decision and Risk
-Stochastic Systems
-Forecasting
-Statistical Inference
-Medical Statistics I
-Medical Statistics II
-Stochastic Methods in Finance I
-Stochastic Methods in Finance II
-Factorial Experimentation
-Selected Topics in Statistics
-Bayesian Methods in Health Economics
-Quantitative Modelling of Operational Risk and Insurance Analytics

Dissertation/report
All MSc students undertake an independent research project, culminating in a dissertation of approximately 10,000–12,000 words.

Teaching and learning
The programme is delivered through a combination of lectures, tutorials and classes, some of which are dedicated to practical work. External organisations deliver technical lectures and seminars where possible. Assessment is through written examination and coursework. The research project is assessed through the dissertation and a 15-minute presentation.

Workshops running during the teaching terms provide preparation for this project and cover the communication of statistics e.g. the presentation of statistical graphs and tables.

Careers

Graduates typically enter professional employment across a broad range of industry sectors or pursue further academic study.

Top career destinations for this degree:
-Management Associate, HSBC
-Statistical Analyst, Nielsen
-PhD Statistics, University College London (UCL)
-Mortgage Specialist, Citibank
-Research Assistant Statistician, Cambridge Institute of Public Health

Employability
The Statistics MSc provides skills that are currently highly sought after. Graduates receive advanced training in methods and computational tools for data analysis that companies and research organisations value. For instance, the new directives and laws for risk assessments in the banking and insurance industries, as well as the healthcare sector, require statistical experts trained at graduate level. The large amount of data processing in various industries (known as "data deluge") also necessitates cutting-edge knowledge in statistics. As a result, our recent graduates have been offered positions as research analysts or consultants, and job opportunities in these areas are increasing.

Why study this degree at UCL?

One of the strengths of UCL Statistical Science is the breadth of expertise on offer; the research interests of staff span the full range from foundations to applications, and make important original contributions to the development of statistical science.

London provides an excellent environment in which to study statistical science, being the home of the Royal Statistical Society as well as a base for a large community of statisticians, both academic and non-academic.

The Statistics MSc has been accredited by the Royal Statistical Society. Graduates will automatically be granted the society's Graduate Statistician status on application.

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Financial engineering involves the creation of financial products that are aimed specifically at the needs of investors, rather than the conventional approach of defining assets on the basis of borrowers' requirements. Read more
Financial engineering involves the creation of financial products that are aimed specifically at the needs of investors, rather than the conventional approach of defining assets on the basis of borrowers' requirements. Central to Financial Engineering are relative value (sometimes called arbitrage) trading strategies and the structuring of financial products, and the closely associated process of securitisation. Structuring involves the transformation of cash flows derived from an asset and improving the risk profile of the structured product. The contemporary derivative markets are driven by the process structuring, both in terms of transforming cash flows through “swaps” and credit enhancement through credit derivatives.

The programme aims to develop the skills and knowledge required by the modern investment and asset management industry where relative value trading strategies and structuring dominate. The emphasis is on developing a range of practical skills rather than develop an abstract "theory of everything". This reflects the need for practitioners to be able to employ different techniques in the ever changing world of contemporary finance.

The material is based substantially on the PRIMIA syllabus for risk management and the Actuarial Profession’s Specialist Technical (ST) syllabus to value and manage the risks associated with a portfolios of derivatives.

The taught component of the degree makes up 120 credits. There are seven mandatory courses leading to 75 credits and consisting of:

• Enterprise Risk Management (15 credits, Semesters 1) - a comprehensive treatment of Financial Risk Management focusing on quantitative aspects.

• Derivative Markets and Pricing (15 credits, Semester 1) - an introduction to derivative markets and how derivative products are priced.

• Modelling and Tools (15 credits, Semester 2) - the fundamental techniques of deterministic and probabilistic mathematical modelling.

• Financial Engineering (15 credits, Semester 2) - provides a thorough grounding in the mathematics underpinning Financial Engineering. Topics include non-standard derivatives, securitisation and structuring, modelling interest rates (including Libor Market Models and valuing swaptions) and contemporary issues in asset management (relative value and pairs trading strategies).

• Credit Risk Modelling (15 credits, Semester 2) - a detailed treatment of the mathematics underpinning Basel Accord on banking supervision and Solvency II for insurance.

Students will also choose three of the following five optional courses leading to a further 45 credits

• Statistical Methods (15 credits, Semester 1) - a foundation course in probability and statistics.

• Financial markets (15 credits, Semester 1) - an introduction to the financial markets.

• Time Series Analysis and Financial Econometrics (15 credits, Semester 2) - analysis and modelling of financial data.

• Modern Portfolio Theory (15 credits, Semester 2) - classical portfolio theory based on maximising expected utility

• Bayesian Inference & Computational Methods (15 credits, Semester 2) - a course on modern Bayesian statistical inference and involving implementing the Bayesian approach in practical situations

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In this digital and data-rich era the demand for statistics graduates from industry, the public sector and academia is high, yet the pool of such graduates is small. Read more

Programme description

In this digital and data-rich era the demand for statistics graduates from industry, the public sector and academia is high, yet the pool of such graduates is small. The recent growth of data science has increased the awareness of the importance of statistics, with the analysis of data and interpretation of the results firmly embedded within this newly recognised field.

This programme is designed to train the next generation of statisticians with a focus on the newly recognised field of data science. The syllabus combines rigorous statistical theory with wider hands-on practical experience of applying statistical models to data. In particular the programme includes:

classical and Bayesian ideologies
linear and generalised linear models
computational statistics applied to a range of models and applications
regression
data analysis

Graduates will be in high demand. It is anticipated that the majority of students will be employed as statisticians within private and public institutions providing statistical advice/consultancy.

Programme structure

To be awarded the MSc degree you need to obtain a total of 180 credits. All students take courses during semester 1 and 2 to the value of 120 credits of which compulsory course units comprise 60 credits. Successful performance in these courses (assessed via coursework or examinations or both) permits you to start work on your dissertation (60 credits) for the award of the MSc degree. The dissertation will generally take the form of two consultancy-style case projects or an externally supervised project.

Compulsory courses (60 credits):

Statistical Theory (10 credits, semester 1)
Statistical Regression Models (10 credits, semester 1)
Bayesian Theory (10 credits, semester 1)
Statistical Programming (10 credits, semester 1)
Bayesian Data Analysis (10 credits, semester 2)
Likelihood and Generalised Linear Models (10 credits, semester 2)

Optional courses (60 credits) include:

Data Analysis (20 credits, semester 1)
Introductory Applied Machine Learning (10 credits, semester 1)
Text Technologies for Data Science (10 credits, semester 1)
Fundamentals of Optimization (10 credits, semester 1)
The Analysis of Survival Data (10 credits, semester 2)
Stochastic Modelling (10 credits, semester 2)
Multilevel Modelling (20 credits, semester 2)
Large Scale Optimization for Data Science (10 credits, semester 2)
Modern Optimization Methods for Big Data Problems (10 credits, semester 2)
Time Series Analysis and Forecasting (5 credits, semester 2)
Combinatorial Optimization (5 credits, semester 2)
Probabilistic Modelling and Reasoning (10 credits, semester 2)

Learning outcomes

At the end of this programme you will have:

knowledge and understanding of statistical theory and its applications within data science
the ability to formulate suitable statistical models for new problems, fit these models to real data and correctly interpret the results
the ability to assess the validity of statistical models and their associated limitations
practical experience of implementing a range of computational techniques using statistical software R and BUGS/JAGS

Career opportunities

Trained statisticians are in high demand both in public and private institutions. This programme will provide graduates with the necessary statistical skills, able to handle and analyse different forms of data, interpret the results and effectively communicate the conclusions obtained.

Graduates will have a deep knowledge of the underlying statistical principles coupled with practical experience of implementing the statistical techniques using standard software across a range of application areas, ensuring they are ideally placed for a range of different job opportunities.

The degree is also excellent preparation for further study in statistics or data science.

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This MSc has a strong theoretical and methodological component to supplement a focus on applications of statistics to real life scientific problems. Read more
This MSc has a strong theoretical and methodological component to supplement a focus on applications of statistics to real life scientific problems. You can opt to follow pathways in medical, pharmaceutical or environmental statistics, depending on your field of interest. Graduates tend to enter careers as practising statisticians, university research assistants or go on to study for a PhD.

For each pathway, you will follow a set of compulsory modules covering core theory and methods, applied statistical modelling and practical skills in topics such as statistical computing, scientific writing, presentation and consultancy. You will also study optional modules tailored to your research interests and career aspirations. Your studies are completed with a supervised, in-depth, dissertation aimed at solving a substantive research question.

Modules
Compulsory modules:
• Statistics in Practice
• Likelihood Inference
• Generalised Linear Models
• Bayesian Inference
• Computational Intensive Methods

Optional modules (choose five from):
• Extreme Value Theory
• Clinical Trials
• Principles of Epidemiology
• Statistical Genetics and Genomics
• Longitudinal Data Analysis
• Pharmacological Modelling
• Survival and Event History Analysis
• Adaptive/Bayesian Methods in Clinical Research
• Environmental Epidemiology

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We have a strong international reputation for making original contributions to Bayesian methodology, bioinformatics and biostatistics. Read more
We have a strong international reputation for making original contributions to Bayesian methodology, bioinformatics and biostatistics. We invite postgraduate research proposals in any of these three areas.

As a research postgraduate in the School of Mathematics and Statistics you will be supported by a team of experts in your chosen field. You will also have the opportunity to develop and enhance your skill set through appropriate research training.

To help you identify a topic and potential supervisor, we encourage you to find out more about our staff specialisms and read about the PhD projects undertaken by some of our recent postgraduate students. A list of example statistical projects currently offered is also available.

As a PhD student you will be supported by team supervision. You will also go through a research training analysis to identify any skills that you need to develop.

Attendance is flexible and agreed between you and your supervisors depending on the requirements of your research project. You are expected to undertake 40 hours of work per week, with an annual holiday entitlement of 35 days (including statutory and bank holidays).

Research areas

We have broad research interests covering applied and medical statistics, and a lively seminar programme.

Our work breaks down into the following research groups:
-Bayesian statistics
-Biostatistics
-Statistical bioinformatics and stochastic systems biology

Research funding

We undertake projects funded by the Research Councils, major trusts, government departments and the EU. Since 2008, members of the School have been named on over £16m of research awards. Many of these awards were for large interdisciplinary projects, in which the School played a vital role, including:
-A £5.5m Engineering and Physical Sciences Research Council grant to explore the potential of microorganisms to provide clean water
-A £1.5m European Commission project to study eukaryotic genomic origins, parasites, and the essential nature of mitochondria
-A £2.1m Medical Research Council funded project on ways to advance health and wellbeing in later life

In total, £3.5m of funding was attributed to the School.

Facilities

We are located in the Herschel building which has well-equipped seminar and meeting rooms. You will have access to online research facilities via your own desktop PC in a shared postgraduate work space. There is also a teaching cluster (of about 150 PCs) within the School. Computing support is provided by two specialist technical staff who are expert in providing assistance with fast numerical and distributed processing.

As well as the library resources provided by the main Robinson Library, you will have access to the School's mathematics and statistics library and reading room.

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This MSc teaches advanced analytical and computational skills for success in a data rich world. Read more
This MSc teaches advanced analytical and computational skills for success in a data rich world. Designed to be both mathematically rigorous and relevant, the programme covers fundamental aspects of machine learning and statistics, with potential options in information retrieval, bioinformatics, quantitative finance, artificial intelligence and machine vision.

Degree information

The programme aims to provide graduates with the foundational principles and the practical experience needed by employers in the area of machine learning and statistics. Graduates of this programme will have had the opportunity to develop their skills by tackling problems related to industrial needs or to leading-edge research.

Students undertake modules to the value of 180 credits.

The programme consists of four core modules (60 credits), four optional modules (60 credits) and a research project (60 credits). Please note that not all combinations of optional modules will be available due to timetabling restrictions.

Core modules
-Supervised Learning
-Statistical Modelling and Data Analysis
-Graphical Models or Probabilistic and Unsupervised Learning
Plus one of:
-Applied Bayesian Methods
-Statistical Design of Investigations
-Statistical Computing
-Statistical Inference

Optional modules - students select 60 credits from the following list:
-Advanced Topics in Machine Learning
-Affective Computing and Human-Robot Interaction
-Applied Bayesian Methods
-Approximate Inference and Learning in Probabilistic Models
-Computational Modelling for Biomedical Imaging
-Information Retrieval and Data Mining
-Machine Vision
-Selected Topics in Statistics
-Optimisation
-Statistical Design of Investigations
-Statistical Inference
-Statistical Natural Language Programming
-Stochastic Methods in Finance
-Stochastic Methods in Finance 2
-Advanced Topics in Statistics
-Mathematical Programming and Research Methods
-Intelligent Systems in Business

Dissertation/report
All MSc students undertake an independent research project, which culminates in a dissertation of 10,000-12,000 words.

Teaching and learning
The programme is delivered through a combination of lectures, discussions, practical sessions and project work. Student performance is assessed through unseen written examinations, coursework, practical application and the project assessment process.

Careers

There is a strong national and international demand for graduates with skills at the interface of traditional statistics and machine learning. Substantial sectors of UK industry, including leading, large companies already make extensive use of computational statistics and machine learning techniques in the course of their business activities. Globally there are a large number of very successful users of this technology, many located in the UK. Areas in which expertise in statistics and machine learning is in particular demand include; finance, banking, insurance, retail, e-commerce, pharmaceuticals, and computer security. Graduates have gone on to further study at, for example, the Universities of Cambridge, Helsinki, Chicago, as well as at UCL. The MSc is also ideal preparation for a PhD, in statistics, machine learning or a related area.

Top career destinations for this degree:
-Statistical and Algorithm Analyst, Telemetry
-Decision Scientist, Everline
-Computer Vision Researcher, Slyce
-Data Scientist, YouGov
-Research Engineer, DeepMind

Employability
Scientific experiments and companies now routinely generate vast databases and machine learning and statistical methodologies are core to their analysis. There is a considerable shortfall in the number of qualified graduates in this area internationally. CSML graduates have been in high demand for PhD positions across the sciences. In London there are many companies looking to understand their customers better who have hired our CSML graduates. Similarly graduates now work in companies in, amongst others, Germany, Iceland, France and the US in large-scale data analysis. The finance sector has also hired several graduates recently.

Why study this degree at UCL?

The Centre for Computational Statistics and Machine Learning (CSML) is a major European Centre for machine learning having coordinated the PASCAL European Network of Excellence.

Coupled with the internationally renowned Gatsby Computational Neuroscience and Machine Learning Unit, and UCL Statistical Science, this MSc programme draws on world-class research and teaching talents. The centre has excellent links with world-leading companies in internet technology, finance and related information areas.

The programme is designed to train students in both the practical and theoretical sides of machine learning. A significant grounding in computational statistics is also provided.

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This programme qualifies for the prestigious Data Lab Masters Scholarships. The award covers full tuition fee costs. For further information please see. Read more
This programme qualifies for the prestigious Data Lab Masters Scholarships. The award covers full tuition fee costs. For further information please see: Data Lab Masters Scholarships.

Why this programme

◾The Statistics Group at Glasgow is a large group, internationally renowned for its research excellence.
◾Our expertise spans topics including: biostatistics and statistical genetics; environmental statistics; statistical methodology; statistical modelling and the scholarship of learning and teaching in statistics.
◾Our Statistics MSc programmes benefit from close links lecturers have with industry and non-governmental organisations such as NHS and SEPA.
◾You will develop a thorough grasp of statistical methodology, before going on to apply statistical skills to solve real-life problems.
◾You will be equipped with the skills needed to begin a career as a professional statistician; previous study of statistics is not required.
◾You will be taught by world-leading experts in their fields and will participate in an extensive and varied seminar programme, are taught by internationally renowned lecturers and experience a wide variety of projects.
◾Our students graduate with a varied skill set, including core professional skills, and a portfolio of substantive applied and practical work.

Programme structure

Modes of delivery of the Masters across the Statistics programmes include lectures, laboratory classes, seminars and tutorials and allow students the opportunity to take part in lab, project and team work.

Courses include (twelve chosen)
◾Advanced Bayesian methods
◾Advanced data analysis
◾Bayesian statistics
◾Biostatistics
◾Computational inference
◾Data analysis
◾Data management and analytics using SAS
◾Design of experiments
◾Environmental statistics
◾Flexible regression
◾Financial statistics
◾Functional data analysis
◾Generalised linear models
◾Introduction to R programming
◾Linear mixed models
◾Machine learning
◾Multivariate methods
◾Principles of probability and statistics
◾Professional skills
◾Spatial statistics
◾Statistical genetics
◾Stochastic processes
◾Time series
◾Statistics project and dissertation.

Summer (May – August)
Statistics project and dissertation (60) - assessed by a dissertation

Career prospects

Our graduates have an excellent track record of gaining employment in many sectors including medical research, the pharmaceutical industry, finance and government statistical services, while others have continued to a PhD.

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Medical statistics is a fundamental scientific component of health research. Medical statisticians interact with biomedical researchers, epidemiologists and public health professionals and contribute to the effective translation of scientific research into patient benefits and clinical decision-making. Read more
Medical statistics is a fundamental scientific component of health research. Medical statisticians interact with biomedical researchers, epidemiologists and public health professionals and contribute to the effective translation of scientific research into patient benefits and clinical decision-making. As new biomedical problems emerge, there are exciting challenges in the application of existing tools and the development of new superior models.

Degree information

The UCL Medical Statistics degree provides students with a sound background in theoretical statistics as well as practical hands-on experience in designing, analysing and interpreting health studies, including trials and observational studies. The taught component equips students with analytical tools for health care economic evaluation, and the research project provides experience in using real clinical datasets.

Students undertake modules to the value of 180 credits.

The programme consists of a foundation course, six core modules (90 credits) two optional modules (30 credits) and the research dissertation (60 credits).

Core modules
-Foundation Course (not credit bearing)
-Statistical Inference
-Statistical Models and Data Analysis
-Medical Statistics I
-Medical Statistics II
-Statistical Computing
-Applied Bayesian Methods

Optional modules - at least one from:
-Statistics for Interpreting Genetic Data
-Bayesian Methods in Health Economics

and at least one from:
-Epidemiology
-Statistical Design of Investigations

Dissertation/report
All MSc students undertake an individual research project, culminating in a dissertation of approximately 10,000–12,000 words.

Teaching and learning
The programme is delivered through a combination of lectures, tutorials and classes, some of which are dedicated to practical work. External organisations deliver technical lectures and seminars where possible. Assessment is through written examination and coursework. The research project is assessed through the dissertation and a 15-minute presentation.

Workshops running during the teaching terms provide preparation for this project and cover the communication of statistics, for example, the presentation of statistical graphs and tables.

Careers

Medical statisticians enable the application of the best possible quantitative methods in health research and assist in the reliable translation of research findings to public and patients’ health care.

The National Institute of Health Research (NIHR) has identified Medical Statistics as one of the priority areas in their capacity building strategy and has awarded UCL two studentships annually for this MSc.

Top career destinations for this degree:
-Graduate Bio-Statistician, PRA International
-Statistical and Epidemiological Modeller, University of Oxford
-Biostatistician, Boehringer Ingelheim
-PhD Statistical Science, University College London (UCL)

Employability
There is an acute shortage of medical statisticians in the UK and employment opportunities are excellent. Recent graduates from this programme have been employed by clinical trials units, pharmaceutical industry, NHS trusts and Universities (e.g. London School of Hygiene and Tropical Medicine, UCL).

Why study this degree at UCL?

One of the strengths of UCL Statistical Science is the breadth of expertise on offer; the research interests of staff span the full range from foundations to applications, and make important original contributions to the development of statistical science.

UCL is linked with four NHS hospital trusts and hosts three biomedical research centres, four clinical trial units and an Institute of Clinical Trials and Methodology. Established links between the Department of Statistical Science, the NIHR UCLH/UCL Biomedical Research Centre and the Clinical Trial Units provide high-quality biomedical projects for Master's students and opportunities for excellent postgraduate teaching and medical research.

The programme has been accredited by the Royal Statistical Society. Graduates will automatically be granted the society's Graduate Statistician status on application.

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Specialised statistical methods are hugely important in dealing with particular problems of economic data. Read more
Specialised statistical methods are hugely important in dealing with particular problems of economic data. For instance, time series econometrics provides methods for analysing the dynamic processes that are often found in macroeconomics, while other techniques are required for analysing the stock market and other financial data.

Econometrics can be described as the application of statistics in an economic context so this course will interest you if your first degree included some training in both statistics and economics.

You study topics including:
-Methods of linear regression and hypothesis testing
-Bayesian statistical modelling and methods
-Actuarial modelling and time series models
-Applied statistics
-Game theory

Our Department of Mathematical Sciences has an international reputation in many areas including semi-group theory, optimisation, probability, applied statistics, bioinformatics and mathematical biology.

You are also taught within our Department of Economics, who are rated consistently highly for student satisfaction and are Top 5 in the UK for research, with over 90% of their research rated as ‘world-leading’ or ‘internationally excellent’ (REF 2014).

This course can also be studied to a PGDip level - for more information, please view this web-page: http://www.essex.ac.uk/courses/details.aspx?mastercourse=PG00807&subgroup=2

Our expert staff

Our Department of Mathematical Sciences is a small but influential department, so our students and staff know each other personally. You never need an appointment to see your tutors and supervisors, just knock on our office doors – we are one of the few places to have an open-door policy, and no issue is too big or small.

Our staff have published several well-regarded text books and are world leaders in their individual specialisms, with their papers appearing in learned journals like Communications in Algebra, Studia Logica, International Journal of Algebra and Computation, SIAM Journal in Optimization, IEEE Evolutionary Computation, Computers and Operations Research, Ecology, Journal of Mathematical Biology, and Journal of Statistical Applications in Genetics and Molecular Biology.

The academic staff in our Department of Economics are also exceptionally well-regarded; our researchers are at the forefront of their field and have even received MBEs.

Many of our researchers in economics also provide consultancy services to businesses in London and other major financial centres, helping us to develop research for today's society as well as informing our teaching for the future.

Specialist facilities

-Unique to Essex is our renowned Maths Support Centre, which offers help to students, staff and local businesses on a range of mathematical problems. Throughout term-time, we can chat through mathematical problems either on a one-to-one or small group basis
-We have our own computer labs for the exclusive use of students in the Department of Mathematical Sciences – in addition to your core maths modules, you gain computing knowledge of software including Matlab and Maple
-Extensive software for quantitative analysis is available in all computer labs across the university
-We host regular events and seminars throughout the year
-Our students run a lively Mathematics Society where you can explore your interest in your subject with other students
-Alternatively, our Economics Society is an active and social group

Your future

Our graduates are sought after by employers in banking, investment and forecasting, local government and other fields.

We also offer supervision for PhD, MPhil and MSc by Dissertation. We have an international reputation in many areas such as semi-group theory, optimisation, probability, applied statistics, bioinformatics and mathematical biology, and our staff are strongly committed to research and to the promotion of graduate activities.

We additionally work with our Employability and Careers Centre to help you find out about further work experience, internships, placements, and voluntary opportunities.

Example structure

-Modelling Experimental Data (optional)
-Statistical Methods (optional)
-Stochastic Processes (optional)
-Applied Statistics (optional)
-Bayesian Computational Statistics (optional)
-Research Methods
-Dissertation
-Mathematics of Portfolios (optional)
-Financial Derivatives (optional)
-Partial Differential Equations (optional)
-Econometric Methods (optional)
-Economics of Financial Markets (optional)
-Game Theory and Applications (optional)
-Time Series Econometrics (optional)
-Panel Data Methods (optional)

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These programmes offers the opportunity to begin or consolidate your research career under the guidance of internationally renowned researchers and professionals in the School of Mathematics, Statistics and Actuarial Science (SMSAS). Read more
These programmes offers the opportunity to begin or consolidate your research career under the guidance of internationally renowned researchers and professionals in the School of Mathematics, Statistics and Actuarial Science (SMSAS).

Research interests are diverse and include: Bayesian statistics; bioinformatics; biometry; ecological statistics; epidemic modelling; medical statistics; nonparametric statistics and semi-parametric modelling; risk and queueing theory; shape statistics.

Visit the website https://www.kent.ac.uk/courses/postgraduate/169/statistics

About the School of Mathematics, Statistics and Actuarial Science (SMSAS):

The School has a strong reputation for world-class research and a well-established system of support and training, with a high level of contact between staff and research students. Postgraduate students develop analytical, communication and research skills. Developing computational skills and applying them to mathematical problems forms a significant part of the postgraduate training in the School. We encourage all postgraduate statistics students to take part in statistics seminars and to help in tutorial classes.

The Statistics Group is forward-thinking, with varied research, and received consistently high rankings in the last two Research Assessment Exercises.

Statistics at Kent provides:

- a programme that gives you the opportunity to develop practical, mathematical and computing skills in statistics, while working on challenging and important problems relevant to a broad range of potential employers

- teaching and supervision by staff who are research-active, with established reputations and who are accessible, supportive and genuinely interested in your work

- advanced and accessible computing and other facilities

- a congenial work atmosphere with pleasant surroundings, where you can socialise and discuss issues with a community of other students.

Course structure

The research interests of the group are in line with the mainstream of statistics, with emphasis on both theoretical and applied subjects.

There are strong connections with a number of prestigious research universities such as Texas A&M University, the University of Texas, the University of Otago, the University of Sydney and other research institutions at home and abroad.

The group regularly receives research grants. The EPSRC has awarded two major grants, which support the National Centre for Statistical Ecology (NCSE), a joint venture between several institutions. A BBSRC grant supports stochastic modelling in bioscience.

Research areas

- Biometry and ecological statistics

Specific interests are in biometry, cluster analysis, stochastic population processes, analysis of discrete data, analysis of quantal assay data, overdispersion, and we enjoy good links within the University, including the School of Biosciences and the Durrell Institute of Conservation and Ecology. A recent major joint research project involves modelling the behaviour of yeast prions and builds upon previous work in this area. We also work in collaboration with many external institutions.

- Bayesian statistics

Current work includes non-parametric Bayes, inference robustness, modelling with non-normal distributions, model uncertainty, variable selection and functional data analysis.

- Bioinformatics, statistical genetics and medical statistics

Research covers bioinformatics (eg DNA microarray data), involving collaboration with the School of Biosciences. Other interests include population genetics, clinical trials and survival analysis.

- Nonparametric statistics

Research focuses on empirical likelihood, high-dimensional data analysis, nonlinear dynamic analysis, semi-parametric modelling, survival analysis, risk insurance, functional data analysis, spatial data analysis, longitudinal data analysis, feature selection and wavelets.

Careers

Students often go into careers as professional statisticians in industry, government, research and teaching but our programmes also prepare you for careers in other fields requiring a strong statistical background. You have the opportunity to attend careers talks from professional statisticians working in industry and to attend networking meetings with employers.

Recent graduates have started careers in diverse areas such as the pharmaceutical industry, financial services and sports betting.

Find out how to apply here - https://www.kent.ac.uk/courses/postgraduate/apply/

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MPhil supervision covers a number of topics supported by research active academic staff. We conduct research in all areas of food and society, including subjects which require collaboration between the social and natural sciences, and translate research into policy recommendations. Read more
MPhil supervision covers a number of topics supported by research active academic staff. We conduct research in all areas of food and society, including subjects which require collaboration between the social and natural sciences, and translate research into policy recommendations.

Our research primarily involves food systems, food consumption and food marketing:
-Consumer studies in food, food provisioning and behaviour change
-Perceived risk associated with food and food production
-Food supply chains and territorial development
-International political economy of food and agriculture
-Risk-benefit communication
-Acceptance of novel food and technologies within the value chain

Opportunities are available for postgraduate research in the following areas.

Understanding and measuring societal and individual responses to risks and benefits
-Food, nutrition and healthy dietary choices
-Sustainable consumption and the reduction of food waste
-Food safety and authenticity throughout the supply chain
-Emerging food technologies

Developing new methodologies for assessing socio-economic impacts of food risks and communication strategies and other public health interventions related to food choice
-Systematic review
-Evidence synthesis
-Systems thinking
-Bayesian networks
-Rapid evidence assessment

Employing qualitative and quantitative methodologies to understand attitudes and behaviours related to food
-Microbiological food hazards
-Personalised nutrition
-Food authenticity
-Societal and consumer responses to emerging food production technologies
-Behaviour change in relation to food
-Food waste

Stakeholder analysis and effectiveness of public engagement
-Research agenda setting
-Policy and governance, in the area of emerging food technologies
-Food and agricultural policy issues

Integrating social and natural science into the development of predictive models of food security to provide evidence for policy translation in the agrifood sector.
-Bayesian networks
-Systems thinking

Delivery

We offer a number of different routes to a research degree qualification, including full-time and part-time supervised research projects. We attract postgraduates via non-traditional routes, including mature students and part-time postgraduates undertaking study as part of their continuing professional development. Off-campus (split) research is also offered, which enables you to conduct trials in conditions appropriate to your research programme.

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The course trains students from a variety of academic backgrounds to work as statisticians in various sectors including higher education, research institutions, the pharmaceutical industry, central government and national health services. Read more
The course trains students from a variety of academic backgrounds to work as statisticians in various sectors including higher education, research institutions, the pharmaceutical industry, central government and national health services. It provides training in the theory and practice of statistics with special reference to clinical trials, epidemiology and clinical or laboratory research.

The PSI Andrew Hewett Prize is founded in memory of Andrew Hewett, an alumnus of the School and awarded by the PSI (Statisticians in the Pharmaceutical Industry) to the best student on the course.
Duration: one year full-time or part-time over two years. Modes of study explained.

- Full programme specification (pdf) (http://www.lshtm.ac.uk/edu/qualityassurance/ms_progspec.pdf)

Visit the website http://www.lshtm.ac.uk/study/masters/msms.html

For the MSc Medical Statistics it is preferred that students should normally have obtained a mathematically-based first degree which includes some statistics. Graduates from other fields who have quantitative skills and some familiarity with statistical ideas may also apply.

Any student who does not meet the minimum entry requirement above but who has relevant professional experience may still be eligible for admission. Qualifications and experience will be assessed from the application.

Intercalating this course

(http://www.lshtm.ac.uk/study/intercalate)

Undergraduate medical students can take a year out either to pursue related studies or work. The School welcomes applications from medical students wishing to intercalate after their third year of study from any recognised university in the world.

Why intercalate with us?:
Reputation: The School has an outstanding international reputation in public health & tropical medicine and is at the forefront of global health research. It is highly rated in a number of world rankings including:

- World’s leading research-focused graduate school (Times Higher Education World Rankings, 2013)
- Third in the world for social science and public health (US News Best Global Universities Ranking, 2014)
- Second in UK for research impact (Research Exercise Framework 2014)
- Top in Europe for impact (Leiden Ranking, 2015)

Highly recognised qualification: possessing a Master's from the School will give you a focused understanding of health and disease, broaden your career prospects and allow you to be immersed in research in a field of your choice.

Valuable skills: you will undertake an independent research project (summer project) in your chosen topic, equipping you with research skills that will distinguish you in a clinical environment. While your medical qualification will give you a breadth of knowledge; undertaking an intercalated degree will allow you to explore your main area of interest in greater depth.

Alumni network: the School has a strong international and diverse alumni community, with more than 20,000 alumni in over 180 countries.

MSc vs. BSc: undertaking an MSc is an excellent opportunity to develop in-depth specialist knowledge in your chosen topic and enhance your skills in scientific research. Postgraduate qualifications are increasingly sought after by clinicians and possessing a Masters qualification can assist you in your future career progression.

Objectives

By the end of this course students should be able to:

- select appropriate study designs to address questions of medical relevance

- select and apply appropriate statistical techniques for managing common types of medical data

- use various software packages for statistical analysis and data management

- interpret the results of statistical analyses and critically evaluate the use of statistics in the medical literature

- communicate effectively with statisticians and the wider medical community, in writing and orally through presentation of results of statistical analyses

- explore current and anticipated developments in medical statistics

Structure

Term 1:
All students take five compulsory modules:
- Foundations of Medical Statistics
- Introduction to Statistical Computing (Stata/SAS/R)
- Clinical Trials
- Basic Epidemiology
- Robust Statistical Methods

Terms 2 and 3:
Students take a total of five modules, one from each timetable slot (Slot 1, Slot 2 etc.). The list below shows recommended modules. There are other modules which can only be taken after consultation with the course director.

*Recommended modules

- Slot 1:
Generalised Linear Models (compulsory)

- Slot 2:
Statistical Methods in Epidemiology (compulsory)

- Slot 3:
Analysis of Hierarchical & Other Dependent Data*
Epidemiology of Non-Communicable Diseases
Modelling & the Dynamics of Infectious Diseases
Social Epidemiology

- Slot 4:
Survival Analysis and Bayesian Statistics (compulsory)

- Slot 5:
Advanced Statistical Modelling*
Advanced Statistical Methods in Epidemiology*

Further details for the course modules - http://www.lshtm.ac.uk/study/currentstudents/studentinformation/msc_module_handbook/section2_coursedescriptions/tmst.html

Project Report

During the summer months (July - August), students complete a research project, for submission by early September. This usually consists of analysing a set of data and writing a report, but methodological research can also be undertaken.

Find out how to apply here - http://www.lshtm.ac.uk/study/masters/msms.html#sixth

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This course develops the careers of doctors whose interest is the practice of medicine in tropical and low- and middle-income countries. Read more
This course develops the careers of doctors whose interest is the practice of medicine in tropical and low- and middle-income countries. The course offers a wide choice of modules and provides training in clinical tropical medicine at the Hospital for Tropical Diseases.

The Diploma in Tropical Medicine & Hygiene (DTM&H):
All students going on the MSc will take the Diploma in Tropical Medicine & Hygiene. Students with a prior DTM&H, or holding 60 Masters level credits from the East African Diploma in Tropical Medicine & Hygiene may apply for exemption from Term 1 via accreditation of prior learning.

Careers

Graduates from this course have taken a wide variety of career paths including further research in epidemiology, parasite immunology; field research programmes or international organisations concerned with health care delivery in conflict settings or humanitarian crises; or returned to academic or medical positions in low- and middle-income countries.

Awards

The Frederick Murgatroyd Award is awarded each year for the best student of the year. Donated by Mrs Murgatroyd in memory of her husband, who held the Wellcome Chair of Clinical Tropical Medicine in 1950 and 1951.

- Full programme specification (pdf) (http://www.lshtm.ac.uk/edu/qualityassurance/tmih_progspec.pdf)

Visit the website http://www.lshtm.ac.uk/study/masters/mstmih.html

Objectives

By the end of this course students should be able to:

- understand and describe the causation, pathogenesis, clinical features, diagnosis, management, and control of the major parasitic, bacterial, and viral diseases of developing countries

- demonstrate knowledge and skills in diagnostic parasitology and other simple laboratory methods

- understand and apply basic epidemiological principles, including selecting appropriate study designs

- apply and interpret basic statistical tests for the analysis of quantitative data

- critically evaluate published literature in order to make appropriate clinical decisions

- communicate relevant medical knowledge to patients, health care professionals, colleagues and other groups

- understand the basic sciences underlying clinical and public health practice

Structure

Term 1:
All students follow the course for the DTM&H. Term 1 consists entirely of the DTM&H lectures, seminars, laboratory practical and clinical sessions, and is examined through the DTM&H examination and resulting in the award of the Diploma and 60 Master's level credits at the end of Term 1.

Terms 2 and 3:
Students take a total of five study modules, one from each timetable slot (Slot 1, Slot 2 etc.). Recognising that students have diverse backgrounds and experience, the course director considers requests to take any module within the School's portfolio, provided that this is appropriate for the student.

*Recommended modules

- Slot 1:
Clinical Infectious Diseases 1: Bacterial & Viral Diseases & Community Health in Developing Countries*
Clinical Virology*
Epidemiology & Control of Malaria*
Advanced Immunology 1
Childhood Eye Disease and Ocular Infection
Designing Disease Control Programmes in Developing Countries
Drugs, Alcohol and Tobacco
Economic Evaluation
Generalised Liner Models
Health Care Evaluation
Health Promotion Approaches and Methods
Maternal & Child Nutrition
Molecular Biology & Recombinant DNA Techniques
Research Design & Analysis
Sociological Approaches to Health
Study Design: Writing a Proposal

- Slot 2:
Clinical Infectious Diseases 2: Parasitic Diseases & Clinical Medicine*
Conflict and Health*
Design & Analysis of Epidemiological Studies*
Advanced Diagnostic Parasitology
Advanced Immunology 2
Clinical Bacteriology 1
Family Planning Programmes
Health Systems; History & Health
Molecular Virology; Non Communicable Eye Disease
Population, Poverty and Environment
Qualitative Methodologies
Statistical Methods in Epidemiology

- Slot 3:
Clinical Infectious Diseases 3: Bacterial & Viral Diseases & Community Health in Developing Countries*
Control of Sexually Transmitted Infections*
Advanced Training in Molecular Biology
Applied Communicable Disease Control
Clinical Immunology
Current Issues in Safe Motherhood & Perinatal Health
Epidemiology of Non-Communicable Diseases
Implementing Eye Care: Skills and Resources
Medical Anthropology and Public Health
Modelling & the Dynamics of Infectious Diseases
Nutrition in Emergencies
Organisational Management
Social Epidemiology
Spatial Epidemiology in Public Health
Tropical Environmental Health
Vector Sampling, Identification & Incrimination

- Slot 4:
Clinical Infectious Diseases 4: Parasitic Diseases & Clinical Medicine*
Epidemiology & Control of Communicable Diseases*
Ethics, Public Health & Human Rights*
Global Disability and Health*
Immunology of Parasitic Infection: Principles*
Analytical Models for Decision Making
Clinical Bacteriology 2
Design & Evaluation of Mental Health Programmes
Environmental Epidemiology
Evaluation of Public Health Interventions
Genetic Epidemiology
Globalisation & Health
Molecular Biology Research Progress & Applications
Nutrition Related Chronic Diseases
Population Dynamics & Projections
Reviewing the Literature
Sexual Health
Survival Analysis and Bayesian Statistics
Vector Biology & Vector Parasite Interactions

- Slot 5:
AIDS*
Antimicrobial Chemotherapy*
Mycology*
Advanced Statistical Methods in Epidemiology
Analysing Survey & Population Data
Applying Public Health Principles in Developing Countries
Environmental Health Policy
Integrated Vector Management
Integrating Module: Health Promotion
Molecular Cell Biology & Infection
Nutrition Programme Planning
Pathogen Genomics
Principles and Practice of Public Health

Further details for the course modules - http://www.lshtm.ac.uk/study/currentstudents/studentinformation/msc_module_handbook/section2_coursedescriptions/ttmi.html

Project Report:
During the summer months (July - August), students complete a research project in a subject of their choice, for submission by early September. Projects may involve writing up and analysing work carried out before coming to the School, a literature review, or a research study proposal. Some students gather data overseas or in the UK for analysis within the project. Such projects require early planning.

Students undertaking projects overseas will require additional funding of up to £1,500 to cover costs involved. The majority of students who undertake projects abroad receive financial support for flights from the School's trust funds set up for this purpose.

Find out how to apply here - http://www.lshtm.ac.uk/study/masters/mstmih.html#sixth

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The MSc in Statistics aims to train professional statisticians for posts in industry, government, research and teaching. It also provides a suitable preparation for careers in other fields requiring a strong statistical background. Read more
The MSc in Statistics aims to train professional statisticians for posts in industry, government, research and teaching. It also provides a suitable preparation for careers in other fields requiring a strong statistical background.

*This course will be taught at the Canterbury campus*

Key benefits

- Statistics is thriving at Kent, and the research of the Group was rated in the top ten in the UK in the most recent Research Assessment Exercise. We are also one of the main hubs of the National Centre for Statistical Ecology.

- Accredited by the Royal Statistical Society (RSS)

Visit the website: https://www.kent.ac.uk/courses/postgraduate/166/statistics

Course Outline

The programme, which has recently been updated, trains professional statisticians for posts in industry, government, research and teaching. It provides a suitable preparation for careers in other fields requiring a strong statistical background. Core modules give a thorough grounding in modern statistical methods and there is the opportunity to choose additional topics to study.

Format and assessment

You undertake a substantial project in statistics, supervised by an experienced researcher. Some projects are focused on the analysis of particular complex data sets while others are more concerned with generic methodology.

You gain experience of analysing real data problems through practical classes and exercises. The programme includes training in the computer language R.

Modules:

- Stochastic Processes and Time Series (15 credits)
- Stochastic Models in Ecology and Medicine (15 credits)
- Analysis of Large Data Sets (15 credits)
- Practical Statistics and Computing (15 credits)
- Computational Statistics (15 credits)
- Project (60 credits)
- Probability and Classical Inference (15 credits)
- Advanced Regression Modelling (15 credits)
- Bayesian Statistics (15 credits)
- Principles of Data Collection (15 credits)

Assessment is through coursework and formal examinations.

Careers

Students often go into careers as professional statisticians in industry, government, research and teaching but our programmes also prepare you for careers in other fields requiring a strong statistical background. You have the opportunity to attend careers talks from professional statisticians working in industry and to attend networking meetings with employers.

Recent graduates have started careers in diverse areas such as the pharmaceutical industry, financial services and sports betting.

How to apply: https://www.kent.ac.uk/courses/postgraduate/apply/

Why study at The University of Kent?

- Shortlisted for University of the Year 2015
- Kent has been ranked fifth out of 120 UK universities in a mock Teaching Excellence Framework (TEF) exercise modelled by Times Higher Education (THE).
- In the Research Excellence Framework (REF) 2014, Kent was ranked 17th* for research output and research intensity, in the Times Higher Education, outperforming 11 of the 24 Russell Group universities
- Over 96% of our postgraduate students who graduated in 2014 found a job or further study opportunity within six months.
Find out more: https://www.kent.ac.uk/courses/postgraduate/why/

Postgraduate scholarships and funding

We have a scholarship fund of over £9 million to support our taught and research students with their tuition fees and living costs. Find out more: https://www.kent.ac.uk/scholarships/postgraduate/

English language learning

If you need to improve your English before and during your postgraduate studies, Kent offers a range of modules and programmes in English for Academic Purposes (EAP). Find out more here: https://www.kent.ac.uk/courses/postgraduate/international/english.html

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This course provides a broad grounding in advanced statistical methods, with a focus on applications in research, the NHS and the pharmaceutical industry; the course structure also allows mathematicians with some statistical experience to move into this field. Read more

Summary

This course provides a broad grounding in advanced statistical methods, with a focus on applications in research, the NHS and the pharmaceutical industry; the course structure also allows mathematicians with some statistical experience to move into this field.

Modules

Analysis of repeated measures; bayesian methods; biological assay; clinical trials; communications and research skills; computer-intensive statistical methods; design and analysis of experiments; epidemiological studies; generalised linear models; multi-level modelling; multivariate distribution theory and inference; statistical computing; statistical genetics; survival analysis; univariate theory and inference; dissertation.

Visit our website for further information...



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