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Masters Degrees (Bayesian)

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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

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
  • computational statistics
  • regression
  • data analysis of a range of models and applications

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 which will be a combination of compulsory and optional courses. 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 standard dissertation will take the form of two consultancy-style case projects in different application areas.

The set of courses available is subject to review in order to maintain a modern and relevant MSc programme.

Previous compulsory courses for 2017-18:

  • Bayesian Data Analysis
  • Bayesian Theory
  • Generalised Regression Models
  • Incomplete Data Analysis
  • Statistical Programming
  • Statistical Research Skills

Previous optional courses for 2017-18 include:

  • The Analysis of Survival Data
  • Biomedical Data Science
  • Credit Scoring
  • Fundamentals of Operational Research
  • Fundamentals of Optimization
  • Genetic Epidemiology
  • Large Scale Optimization for Data Science
  • Machine Learning and Pattern Recognition
  • Machine Learning Practical
  • Nonparametric Regression Models
  • Object-Oriented Programming with Applications
  • Probabilistic Modelling and Reasoning
  • Python Programming
  • Scientific Computing
  • Statistical Consultancy
  • Statistical Methodology
  • Stochastic Modelling
  • Time Series

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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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. The quantitative skills training provided by this MSc can lead to new and exciting opportunities in industry, medicine, government, commerce or research.

About this degree

The programme takes 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.

Further information on modules and degree structure is available on the department website: Statistics MSc

Careers

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

Recent career destinations for this degree

  • Data Analyst, Bupa
  • Quantitative Risk Analyst, Santander
  • PhD in Statistics, UCL
  • Management Associate, HSBC
  • Statistical Analyst, Nielsen

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.



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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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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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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 Masters in Advanced Statistics will provide you with knowledge and experience of the principles, theory and practical skills of statistics. Read more

This Masters in Advanced Statistics will provide you with knowledge and experience of the principles, theory and practical skills of statistics.

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.

About this degree

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 healthcare 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.

Further information on modules and degree structure is available on the department website: Statistics (Medical Statistics) MSc

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.

Recent career destinations for this degree

  • Biostatistician, Boehringer Ingelheim
  • Statistical and Epidemiological Modeller, University of Oxford
  • PhD in Statistical Science, UCL
  • Graduate Bio-Statistician, PRA International

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, the pharmaceutical industry, NHS trusts and universities (e.g. London School of Hygiene & 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 UCL 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.



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This programme will help you develop professionally in the theory and practice of statistics and operational research (OR), providing the foundations for a successful career. Read more

This programme will help you develop professionally in the theory and practice of statistics and operational research (OR), providing the foundations for a successful career.

This programme will prepare you for work in areas such as the medical and health industry, government, the financial sector and any other area where modern statistical tools and OR techniques are used. You will also develop the wider skills required for solving problems, working in teams and time management.

You will be able to identify appropriate statistical or operational techniques, which can be applied to practical problems, and will acquire extensive skills in modelling using the packages R for Statistics and Arena for simulation.

Programme structure

This MSc consists of lecture-based courses and practical, lab-based courses. You will be assessed by exams, written reports, programming assignments and a dissertation project. The set of courses available is subject to review in order to maintain a modern and relevant MSc programme.

Previous compulsory courses for 2017-18:

  • Bayesian Theory
  • Fundamentals of Operational Research
  • Fundamentals of Optimization
  • Generalised Regression Models
  • Methodology, Modelling and Consulting Skills
  • Simulation
  • Statistical Programming
  • Statistical Research Skills

Previous optional courses for 2017-18 include:

  • The Analysis of Survival Data
  • Bayesian Data Analysis
  • Biomedical Data Science
  • Credit Scoring
  • Genetic Epidemiology
  • Incomplete Data Analysis
  • Integer and Combinatorial Optimization
  • Large Scale Optimization for Data Science
  • Machine Learning Practical
  • Nonparametric Regression Models
  • Operational Research in the Energy Industry
  • Python Programming
  • Risk and Logistics
  • Scientific Computing
  • Statistical Consultancy
  • Stochastic Modelling
  • Time Series
  • Topics in Applied Operational Research
  • Topics in Applied Optimization

Career opportunities

This programme is ideal for students who wish to apply their statistics and operational research knowledge within a wide range of sectors including the medical and health sector, government and finance. The advanced problem-solving skills you will develop will be highly prized by many employers.

Industry-based dissertation projects

The dissertation projects of approximately half the students on this programme take place in public and private sector organisations. Other students choose a University-based project.



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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 MSc in Data and Computational Science is designed for students from highly quantitative disciplines who wish to work in data analytics or computational science. Read more
The MSc in Data and Computational Science is designed for students from highly quantitative disciplines who wish to work in data analytics or computational science.

Computational science is at the crossroads between modern applied mathematics and statistics, and our programme recognizes this fact by combining aspects of both in a unique set of tailored modules including scientific computing, mathematical modelling, and data analytics.

- The programme will equip you to solve complex scientific problems and analyse large data sets using a range of theoretical tools, from deterministic mathematical modelling to Bayesian analysis.

- The intensive programming modules will allow you develop a range of sought-after skills in practical programming and data analytics, including applications in high-performance computing.

- Topical application areas are offered each year, including cryptography, numerical weather prediction, and financial mathematics. The dissertation will give you further hands-on experience in computational science and will allow you to apply the key theoretical and practical skills by working on a challenging research topic.

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This unique Masters in Applied Statistics in Health Sciences provides an opening to a career as an applied statistician, without having previously studied statistics. Read more
This unique Masters in Applied Statistics in Health Sciences provides an opening to a career as an applied statistician, without having previously studied statistics.

The course is run in collaboration with the Animal and Plant Health Agency (APHA), an Executive Agency of the Department for Environment, Food & Rural Affairs (Defra). Statisticians from APHA, as well as those who have extensive experience in working with the National Health Service in Scotland, will provide lectures based around real-life problems and data from the health sciences.

Although the programme is focused on health, the skill set provided will also equip you with the necessary training to work as an applied statistician in other areas such as insurance, finance and commerce.

What you'll learn

The three compulsory modules covered in Semester 1 will equip you with fundamental probability and data analysis skills. In Semester 2 there's four compulsory modules, each focusing on a different applied element of being a statistician. The course concludes with a research project which will involve the analysis of a real-life data set.

Programme skills set
On the programme you'll acquire:
-in-depth knowledge of modern statistical methods that are used to analyse and visualise real-life data sets and the experience of how to apply these methods in a professional setting
-skills in using statistical software packages that are used in government, industry and commerce
-the ability to interpret the output from statistical tests and data analyses and communicate your findings to a variety of audiences including health professionals, scientists, government officials, managers and stakeholders who may have an interest in the problem
-problem solving and high numeracy skills that are widely sought after in the commercial sector
-practical experience of statistical consultancy and how to interact with professionals who require statistical analyses of their data
-through the contacts with APHA and NHS staff, an understanding of what it's like to work as an applied statistician in practice including, for example, during disease outbreaks

Guest lectures
Several modules will be taught by academics who also work for other organisations including government and health services.

Facilities

The Department of Mathematics & Statistics has teaching rooms which provide you with access to modern teaching equipment and computing laboratories that are state-of-the-art with all necessary software available. You'll also have a common room facility, a modern and flexible area which is used for individual and group study work, and also a relaxing social space.

The Department of Mathematics & Statistics

At the heart of the Department of Mathematics & Statistics is the University’s aim of developing useful learning. We're an applied department with many links to industry and government. Most of the academic staff teaching on this course hold joint-appointments with, or are funded by, other organisations, including APHA, Public Health and Intelligence (Health Protection Scotland), Greater Glasgow and Clyde Health Board and the Marine Alliance for Science and Technology Scotland (MASTS). We bridge the gap between academia and real-life. Our research has societal impact.

Course content

Semester 1 Compulsory classes:
The three compulsory classes covered in semester 1 will equip you with fundamental probability and data analysis skills.

-Foundations of Probability & Statistics
-Data Analytics in R
-Applied Statistical Modelling

Semester 2 Compulsory classes:
Each class focuses on a different applied element of being a statistician.

-Medical Statistics
-Bayesian Spatial Statistics
-Effective Statistical Consultancy
-Risk Analysis
-Survey Design & Analysis
-Semester 3

Learning & teaching

Classes are delivered by a number of teaching methods:
-lectures (using a variety of media including electronic presentations and computer demonstrations)
-tutorials
-computer laboratories
-coursework
-projects

Teaching is student-focused, with students encouraged to take responsibility for their own learning and development. Classes are supported by web-based materials.

Assessment

The form of assessment varies for each class. For most classes the assessment involves both coursework and examinations.

How can I fund my course?

A number of scholarships are available for outstanding UK, EU and international applicants. For details, please visit our scholarship search: https://www.strath.ac.uk/studywithus/scholarships/

Scottish students:
Students living in Scotland can find out more about funding from the Student Awards Agency Scotland.

English/EU students:
Students ordinarily resident in England may be eligible to apply for a loan of up to £10,000 to cover their tuition fees and living costs. Students resident in the EU may also apply.

Careers

There are many exciting career opportunities for graduates in applied statistics. The practical, real-life skills that you'll gain means you'll be much in demand in international organisations. A report by the Association of the British Pharmaceutical Industry identified statistics and data mining as “two key areas in which a 'skills gap' is threatening the UK's biopharmaceutical industry.”

Typical employers of statisticians and data analysts include:

-Government
-Health services
-Pharmaceutical companies
-Human, animal, plant and environmental research institutes
-Insurance companies
-Banks
-Internet information providers such as Google
-Retailers

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The techniques we use to model and manipulate data guide the political, financial and social decisions that shape our modern society and are the basis of growth of the economy and success of businesses. Read more
The techniques we use to model and manipulate data guide the political, financial and social decisions that shape our modern society and are the basis of growth of the economy and success of businesses. Technology is growing and evolving at an incredible speed, and both the rate of growth of data we generate and the devices we use to process it can only increase.

Data science is a growing and important field of study with a fast-growing number of jobs and opportunities within the private and public sector. The application of theory and methods to real-world problems and applications is at the core of data science, which aims especially to use and to exploit big data.

If you are interested in solving real-world problems, you like to develop skills to use smart devices efficiently, you want to use and to foster your understanding of mathematics, and you are interested and keen to use statistical techniques and methods to interpret data, MSc Data Science at Essex is for you. You study a balance of solid theory and practical application including:
-Computer science
-Programming
-Statistics
-Data analysis
-Probability

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 also benefit from being taught in our School of Computer Science and Electronic Engineering, who are ranked Top 10 in the UK in the 2015 Academic Ranking of World Universities, with more than two-thirds of their research rated ‘world-leading’ or ‘internationally excellent’ (REF 2014).

The collaborative work between our departments has resulted in well-known research in areas including artificial intelligence, data analysis, data analytics, data mining, data science, machine learning and operations research.

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.

The academic staff in our School of Computer Science and Electronic Engineering are conducting world-leading research in areas such as evolutionary computation, brain-computer interfacing, intelligent inhabited environments and financial forecasting.

Specialist staff working on data analytics include Dr Paul Scott, who researches data mining, models of memory and attention, and artificial intelligence, and Professor Maria Fasli, who researches data exploration, analysis and modelling of complex, structured and unstructured data, big data, cognitive agents, and web search assistants.

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
-We have six laboratories that are exclusively for computer science and electronic engineering students
-All computers run either Windows 7 or are dual boot with Linux
-Software includes Java, Prolog, C++, Perl, Mysql, Matlab, DB2, Microsoft Office, Visual Studio, and Project
-You have access to CAD tools and simulators for chip design (Xilinx) and computer networks (OPNET)
-We also have specialist facilities for research into areas including non-invasive brain-computer interfaces, intelligent environments, robotics, optoelectronics, video, RF and MW, printed circuit milling, and semiconductors
-We host regular events and seminars throughout the year
-Collaborate with the Essex Institute of Data Analytics and Data Science (IADS) and the ESRC Business and Local Government (BLoG) Data Research Centre of the University of Essex
-The UK Data Archive and the Institute for Social and Economic Research (ISER) at Essex contribute to our internationally outstanding data science environment

Your future

With a predicted shortage of data scientists, now is the time to future-proof your career. Data scientists are required in every sector, carrying out statistical analysis or mining data on social media, so our course opens the door to almost any industry, from health, to government, to publishing.

Our graduates are highly sought after by a range of employers and find employment in financial services, scientific computation, decision making support and government, risk assessment, statistics, education and other sectors.

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

-Dissertation (optional)
-MSc Project and Dissertation (optional)
-Applied Statistics
-Machine Learning and Data Mining
-Modelling Experimental Data
-Text Analytics
-Artificial Neural Networks (optional)
-Bayesian Computational Statistics (optional)
-Big-Data for Computational Finance (optional)
-Combinatorial Optimisation (optional)
-High Performance Computing (optional)
-Natural Language Engineering (optional)
-Nonlinear Programming (optional)
-Professional Practice and Research Methodology (optional)
-Programming in Python (optional)
-Information Retrieval (optional)
-Data Science and Decision Making (optional)
-Research Methods (optional)
-Statistical Methods (optional)
-Stochastic Processes (optional)

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In recent years, finance has been one of the areas where high-calibre mathematicians have been in great demand. Read more
In recent years, finance has been one of the areas where high-calibre mathematicians have been in great demand. With the advent of powerful and yet economically accessible computing, online trading has become a common activity, but many have realised that a certain amount of mathematics is necessary to be successful in such fields.

One of our most popular courses, MSc Mathematics and Finance allows those with a background in mathematics to study finance. Since finance routinely involves modelling and evaluating risk, asset pricing and price forecasting, mathematics has become an indispensable tool for this study.

You explore topics including:
-Models and mathematics in portfolio management
-Risk management in modern banking
-Financial modelling
-Actuarial modelling
-Applied statistics

Our interdisciplinary research recognises that mathematics, including what can be very abstract mathematics, is an essential part of research in many other disciplines.

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

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=PG00610&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.

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
-We host regular events and seminars throughout the year
-Our students run a lively Mathematics Society, an active and social group where you can explore your interest in your subject with other students

Your future

There is undoubtedly a shortage of mathematicians in general, and an even greater one of those with knowledge of finance.

Our course produces graduates with a sound background in mathematics and finance. Key employability skills include computing, use of algorithms, data analysis, mathematical modelling and understanding financial statements.

Our graduates are highly sought after by a range of employers and find employment in financial services, scientific computation, decision making support and government, risk assessment, statistics, education and other sectors.

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

-Dissertation
-Research Methods
-Financial Modelling
-Mathematics of Portfolios
-Research Methods in Finance: Empirical Methods in Finance
-Stochastic Processes
-Applied Statistics (optional)
-Bank Strategy and Risk (optional)
-Bayesian Computational Statistics (optional)
-Combinatorial Optimisation (optional)
-Derivative Securities (optional)
-Economics of Financial Markets (optional)
-Financial Derivatives (optional)
-Ordinary Differential Equations (optional)
-Partial Differential Equations (optional)
-Statistical Methods (optional)
-Metric Spaces

Read less
Statistics is one of the most important fields of study in the world. The techniques we use to model and manipulate data guide the political, financial and social decisions that shape our modern society. Read more
Statistics is one of the most important fields of study in the world. The techniques we use to model and manipulate data guide the political, financial and social decisions that shape our modern society. If you are a logical person and enjoy solving problems, statistics at Essex is for you.

Our Department of Mathematical Sciences embraces pure mathematics, applied mathematics and statistics, and operational research, and our course offers you the opportunity to study statistics alongside other mathematical subjects.

Providing a balance of solid statistical theory and practical application, this course builds your knowledge in all areas of statistics, data analysis and probability. You also have the opportunity to specialise, taking optional modules in topics including:
-Survey methodology
-Operations research
-Applied mathematics
-Computer science

Our interdisciplinary research recognises that mathematics, including what can be very abstract mathematics, is an essential part of research in many other disciplines.

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

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.

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
-We host regular events and seminars throughout the year
-Our students run a lively Mathematics Society, an active and social group where you can explore your interest in your subject with other students

Your future

Working in industries such as health, business, social care and finance, graduates are consistently in demand, working on projects such as efficacy of social policy, comparable data of cardiac rehabilitation and manipulation of raw data for academic research.

Our Masters graduates have progressed into careers in banking and finance, actuarial sciences, biological sciences, market research and statistics, management and consultancy etc.

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
-Statistical Methods
-Stochastic Processes
-Applied Statistics
-Bayesian Computational Statistics
-Research Methods
-Dissertation
-Nonlinear Programming (optional)
-Financial Modelling (optional)
-Research Methods in Finance: Empirical Methods in Finance (optional)
-Machine Learning and Data Mining (optional)
-Cloud Technologies and Systems (optional)
-Time Series Econometrics (optional)
-Panel Data Methods (optional)
-Topics in Contemporary Social Theory (optional)
-Introduction to Survey Design and Management (optional)
-Applied Sampling (optional)

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