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Mathematics×

Masters Degrees in Statistical Modelling

We have 52 Masters Degrees in Statistical Modelling

Masters degrees in Statistical Modelling equip postgraduates with the skills to apply methods of statistics to the modelling and simulation of real-world systems and processes. These models then inform the decision-making process in many economic and political practices.

Related postgraduate specialisms include Data Visualisation and Modelling, and Statistics with Data Science. Entry requirements normally involve an undergraduate degree related to Mathematics.

Why study a Masters in Statistical Modelling?

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The two-year master’s programme Statistical Science for the Life and Behavioural Sciences provides you with a thorough introduction to the general philosophy and methodology of statistical modelling, data analysis and data science. Read more

The two-year master’s programme Statistical Science for the Life and Behavioural Sciences provides you with a thorough introduction to the general philosophy and methodology of statistical modelling, data analysis and data science.

What does this master’s programme entail?

The two-year master’s programme in Statistical Science provides you with a thorough introduction to the general philosophy and methodology of statistical modelling and data analysis. The programme consists of a core programme shared by all students, and specialisation specific courses, electives, an internship or research project and master’s thesis. You can specialise in either life and behavioural sciences, where the emphasis is on the application in multidisciplinary environments, or in data sciences where you focus more on data mining, pattern recognition and deep learning.

Read more about the Statistical Science for the Life and Behavioural Sciences programme.

Why study Statistical Sciences for the Life and Behavioural Sciences at Leiden University?

  • Each specialisation offers you a unique combination of knowledge and expertise. These allow for a thorough preparation for a career as a data scientist, researcher or statistician anywhere.
  • Job perspectives after graduation are great: statisticians and data scientists are highly sought after in various industries such as academia, marketing, banking, government, official statistics, healthcare, bioinformatics and more.
  • The Statistical Science programme is a collaborative effort. Four Leiden University Institutes closely collaborate with top research institutes such as Wageningen UR and VUMC, which means that your education is provided by experts in their respective fields.

Find more reasons to choose Statistical Science for the Life and Behavioural Sciencese at Leiden University.

Statistical Sciences for the Life and Behavioural Sciences: the right master’s programme for you?

The field of statistics, like other areas of applied mathematics, often attracts students who are interested in the analysis of patterns in data: developing, understanding, abstracting, and packaging analytical methods for general use in other subject areas. Statistics is also, by definition, an information science. Imaginative use of both computing power and new computing environments drives much current research - so an interest in computation and/or computer science can also be a start for a statistician. With the growing importance of data within our society, you’ll be highly in demand with a degree in Statistical Sciences.

Read more about the entry requirements for the Statistical Science programme.



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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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International Master's in Statistics - MSc. https://www.kent.ac.uk/courses/postgraduate/163/international-masters-statistics. Read more
International Master's in Statistics - MSc: https://www.kent.ac.uk/courses/postgraduate/163/international-masters-statistics

Overview

The International Master’s in Statistics develops your practical, statistical and computing skills to prepare you for a professional career in statistics or as a solid basis for further research in the area.

The programme has been designed to provide a deep understanding of the modern statistical methods required to model and analyse data. You will benefit from a thorough grounding in the ideas underlying these methods and develop your skills in key areas such as practical data analysis and data modelling.

It has been accredited by the Royal Statistical Society (RSS) and equips aspiring professional statisticians with the skills they need for posts in industry, government, research and teaching. It also enables you to develop a range of transferable skills that are attractive to employers within the public and private sectors.

Students whose mathematical and statistical background is insufficient for direct entry on to the appropriate programme, may apply for this course. The first year of the programme gives you a strong background in statistics, including its mathematical aspects, equivalent to the Graduate Diploma in Statistics. This is followed by the MSc in Statistics.

International Master's in Statistics with Finance - MSc: https://www.kent.ac.uk/courses/postgraduate/164/international-masters-statistics-finance

Overview

This programme, accredited by the Royal Statistical Society (RSS), equips aspiring professional statisticians with the skills they will need for posts in industry, government, research and teaching. It is suitable preparation too for careers in other fields requiring a strong statistical background.

Students whose mathematical and statistical background is insufficient for direct entry on to the appropriate programme, may apply for this course. The first year of the programme gives you a strong background in statistics, including its mathematical aspects, equivalent to the Graduate Diploma in Statistics. This is followed by the MSc in Statistics with Finance.

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.

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.

Professional recognition

The taught programmes in Statistics and Statistics with Finance provide exemption from the professional examinations of the Royal Statistical Society and qualification for Graduate Statistician status.

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

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While practically orientated, this postgraduate degree in applied statistics and financial modelling proceeds from a strong theoretical background so as to develop your ability to tackle new and non-standard problems with confidence. Read more
While practically orientated, this postgraduate degree in applied statistics and financial modelling proceeds from a strong theoretical background so as to develop your ability to tackle new and non-standard problems with confidence. The mutual dependence of practice and theory is emphasised wherever possible.

The programme is ideal if you are considering a career move into statistics, or if your work already involves aspects of data collection and exploration, the interpretation of statistics, or the use of advanced stochastic modelling techniques in the area of quantitative finance.

The programme has been specially designed to meet the personal and career development needs of people who want to continue working while also studying in the evening. Many of our students, as part of their everyday work, are involved in data analysis, the interpretation of statistics, the optimal design and control of systems, and the modelling and prediction of time-dependent phenomena. They bring a wealth of knowledge and experience into the classroom, and you’ll find yourself surrounded by committed, enthusiastic students from all backgrounds, careers and cultures.

Why study this course at Birkbeck?

Covers both theory and application of stochastic and statistical modelling techniques required to solve applied problems in industry, the public services, scientific research and commerce.
Accredited by the Royal Statistical Society - graduates are normally granted Graduate Statistician (GradStat) status.
Birkbeck brings together research and teaching across economics and finance, mathematics and statistics in a single department, which creates significant interdisciplinary synergies.
Our teaching is informed by the needs of employers and you will be taught by academics who are professional practitioners involved in the world of economics and international finance. They provide specialist advice and in-house training for government departments, City firms and banks.
Our Department houses 5 research groups, in Applied Mathematics and Finance, Econometrics and Statistical Science, Macroeconomics, Microeconomics, and Pure Mathematics, which host visiting speakers and organise seminars. The Birkbeck Centre for Applied Macroeconomics and the Commodities Finance Centre are our 2 research centres, which disseminate research, host events and house visiting academics.
You will have access to a wide range of study resources, including University of London seminar programmes in probability and statistics, and excellent library facilities close by in Bloomsbury. Extensive computing facilities include PCs and UNIX platforms.

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This Masters in Environmental Statistics will provide you with knowledge and experience of the principles, theory and practical skills of statistics; previous study of statistics is not required. Read more

This Masters in Environmental Statistics will provide you with knowledge and experience of the principles, theory and practical skills of statistics; previous study of statistics is not required.

Why this programme

  • The Statistics Group at Glasgow is a large group, internationally renowned for its research excellence.
  • Our Statistics MSc programmes benefit from close links lecturers have with industry and non-governmental organisations such as NHS and SEPA.
  • Our expertise spans topics including: biostatistics and statistical genetics; environmental statistics; statistical methodology; statistical modelling and the scholarship of learning and teaching in statistics.
  • 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.

Core courses (compulsory)

  • Bayesian statistics 
  • Data analysis 
  • Environmental statistics 
  • Generalised linear models 
  • Introduction to R programming
  • Principles of GIS (10)
  • Probability 1
  • Regression models 
  • Spatial statistics 
  • Statistical inference 1
  • Time series 
  • Topographic mapping and landscape monitoring
  • Statistics project and dissertation.

1 Any student who, in the course of study for his or her first degree, has already completed the equivalent of the Probability and/or Statistical inference courses can substitute these courses by any other optional course (including optional courses offered as part of the MRes in Advanced Statistics). The choice of substituting course is subject to approval by the Programme Director.

Summer (May – August)

Statistics project and dissertation (60) - applying statistical methods and modelling to data collected from research in environmental science, 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.

Graduates of this programme have gone on to positions such as:

Research Officer Medical Statistics at Kenya Medical Research Institute (KEMRI) Welcome Trust.



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This programme introduces you to the methods of statistical analysis, together with the underlying theory and some of the associated mathematics. Read more
This programme introduces you to the methods of statistical analysis, together with the underlying theory and some of the associated mathematics. The graduate diploma gives you the chance to study one or more specific areas of statistics in greater depth.

You will gain an understanding of statistical methods and will be able to apply them to the analysis of real-world data sets. You will also learn how to use statistical computer packages.

Visit the website http://www.bbk.ac.uk/study/2016/postgraduate/programmes/GDGSTATI_C/

Our research

Birkbeck is one of the world’s leading research-intensive institutions. Our cutting-edge scholarship informs public policy, achieves scientific advances, supports the economy, promotes culture and the arts, and makes a positive difference to society.

Birkbeck’s research excellence was confirmed in the 2014 Research Excellence Framework (http://www.bbk.ac.uk/news/ref-results/), which placed Birkbeck 30th in the UK for research, with 73% of our research rated world-leading or internationally excellent.

Read about Birkbeck research offering insights and expertise to inform business, contribute to economic success and develop ground-breaking technologies (http://www.bbk.ac.uk/business/our-research).

Why study this course at Birkbeck?

- Provides an introduction to the main methods of statistical analysis used in business and scientific research.

- Ideal as a way to top up existing knowledge, as preparation for further graduate study or as a stand-alone course.

- Watch videos of our postgraduate students discussing their experience of studying at Birkbeck (http://www.bbk.ac.uk/mybirkbeck/get-ahead-stay-ahead/student-experience-videos).

Course structure

You take 2 compulsory year-long modules, which form the Graduate Certificate in Statistics, designed to give you a thorough grounding in mathematical and statistical methods as a basis for the postgraduate study of statistics.

Then you take 2 further modules, including at least 1 module from: Statistical Modelling; or Probability Models and Time Series.

Compulsory modules:
Advanced Mathematical Methods
Statistics: Theory and Practice

Option modules:
Probability Models and Time Series
Statistical Modelling

Teaching and assessment

Teaching
Mainly by lectures, but you will take part in practical sessions using a statistical package as part of the Statistics: Theory and Practice module.

Assessment
Coursework makes up 20% of the assessment of all modules. The rest of the assessment is by examinations taken in the summer term.

Careers and employability

Graduates can pursue careers in data collection, research, and analysis, modelling and forecasting. Possible professions include statistician, operational researcher, or research scientist (maths). This degree may also be useful in becoming a forensic statistician or high education lecturer.

Find out more about these professions (http://www.prospects.ac.uk/options_with_your_subject.htm).

Find out more about the destinations of graduates in this subject (http://www.bbk.ac.uk/prospective/careers-and-employability/department-of-economics-mathematics-and-statistics).

We offer a comprehensive Careers and Employability Service to help you advance your career, while our in-house, professional recruitment consultancy, Birkbeck Talent, works with London’s top employers to help you gain work experience that fits in with your evening studies.

Find out how to apply here - http://www.bbk.ac.uk/prospective/postgraduate/apply

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The Probability and Statistics group (4 Professors, 3 Senior Lecturers and 10 Lecturers) in the School of Mathematics have a long-standing reputation… Read more

The Probability and Statistics group (4 Professors, 3 Senior Lecturers and 10 Lecturers) in the School of Mathematics have a long-standing reputation and experience of offering one year, high quality taught courses in areas of Statistics leading to the degree of MSc.These courses have aimed to offer a thorough professional training which prepare students to embark on statistical careers in a variety of areas. (There is a shortage of statisticians trained to postgraduate level in the UK and the employment prospects for such people remain good.)   They have also provided a very good foundation for further study at PhD level.

Our current MSc programme in Statistics allows students to take one of two different MSc degrees, depending on their interests and career aspirations. There is the main programme in Statistics and one associated pathway in Financial Statistics. Each is built around a common core of five modules and then students study an additional set of three specialist modules to make a total of eight in all. 

Coursework and assessment

There are two teaching semesters of 12 weeks each and approximately 15 weeks of project work. Assessment for the taught part is by exams and coursework. Following the successful completion of the taught part of the programme (worth a total of 120 credits) students are then expected to work on a dissertation from June to September which is worth a further 60 credits, making 180 credits in total. Information on the various topics and projects which will be available for dissertation are provided to the students in May from which they are invited to state their preferences.  

Course unit details

The taught part of the programme is divided into two 12-week semesters, each followed by a two-week period of examinations. This in turn is followed by a period of approximately 12 weeks of research work over the summer which is supervised by a member of the academic staff and ends with submission of the MSc dissertation in September. In the taught part of the course, full-time students attend weekly lectures and support classes for four modules (4 x 15 credits) in each semester. Students are also able to enrol on a part-time basis if they wish. In this case they study over a two year period and only take two modules per semester, with the dissertation being completed at the end of the second year. Details of the programme structure are given below.

Main MSc Statistics

  Semester One:

  • Linear Models & Nonparametric Regression
  • Statistical Computing
  • Statistical Inference
  • Multivariate Statistics

 Semester Two:

  • Generalized Linear Models & Survival Analysis
  • Longitudinal Data Analysis
  • Markov Chain Monte Carlo (MCMC)
  • Design and Analysis of Experiments

 This degree is accredited by the Royal Statistical Society.

Financial Statistics Pathway

This comprises a core of five modules which are common to the main programme, plus three specialist modules in financial statistics.

Semester One:

  • Linear Models & Nonparametric Regression
  • Statistical Computing
  • Statistical Modelling in Finance
  • Extreme Values and Financial Risk

 Semester Two:

  • Generalized Linear Models & Survival Analysis
  • Longitudinal Data Analysis
  • Markov Chain Monte Carlo (MCMC)
  • Time Series Analysis and Financial Forecasting

This degree is also accredited by the Royal Statistical Society.

Accreditation by the Royal Statistical Society (RSS) provides reassurance that our MSc programme produces graduates with the technical skills and subject knowledge required of a statistician. This provides our graduates with a competitive edge in the job market and provides employers with an assurance of quality of our degree.

Dissertation  Following the successful completion of the taught part of the programme (worth a total of 120 credits) students are then expected to work on a dissertation from June to September which is worth a further 60 credits, making 180 credits in total. Information on the various topics and projects which will be available for dissertation are provided to the students in May from which they are invited to state their preferences.  

Facilities

The School of Mathematics is the largest in the UK with an outstanding research reputation andfacilities .

Disability support

Practical support and advice for current students and applicants is available from the Disability Advisory and Support Service. Email: 

Career opportunities

These programmes will prepare students for a broad range of statistical careers, particularly in the financial, medical, pharmaceutical and industrial sectors of the economy, but also with local and national government agencies, as well as in other areas. They will also provide an excellent foundation for students wishing to pursue advanced postgraduate research in statistics.



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

This Masters in 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.

Core courses (compulsory)

  • Bayesian statistics
  • Generalised linear models 
  • Introduction to R programming 
  • Probability 1
  • Regression models 
  • Statistical inference 1
  • Statistics project and dissertation.

Optional courses (six chosen, but at least one course must be from Group 1)

Group 1

  • Data analysis 
  • Professional skills.

Group 2

  • Biostatistics 
  • Computational inference 
  • Data management and analytics using SAS 
  • Design of experiments 
  • Environmental statistics 
  • Financial statistics 
  • Functional data analysis 
  • Machine learning
  • Multivariate methods 
  • Spatial statistics 
  • Statistical genetics 
  • Stochastic processes 
  • Time series.

1 Any student who, in the course of study for his or her first degree, has already completed the equivalent of the Probability and/or Statistical inference courses can substitute these courses by any other optional course (including optional courses offered as part of the MRes in Advanced Statistics). The choice of substituting courses is subject to approval by the Programme Director.

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 finance, medical research, the pharmaceutical industry and government statistical services, while others have continued to a PhD.



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About the MSc programme. The MSc Statistics provides intensive training in statistics applicable to the social sciences, economics and finance. Read more

About the MSc programme

The MSc Statistics provides intensive training in statistics applicable to the social sciences, economics and finance.

The aim of the programme is to foster an interest in theoretical and applied statistics and equip you for work as a professional statistician. You will learn to analyse and critically interpret data, build statistical models of real situations, and use programming tools and statistical software packages. 

The compulsory course will provide you with comprehensive coverage of fundamental aspects of probability and statistical methods and principles. It provides the foundations for the optional courses on more advanced statistical modelling, computational methods, statistical computing and advanced probability theory. Options also include specialist courses from the Departments of Methodology, Management, Mathematics, Economics and Social Policy. 

Graduates of the programme are awarded Graduate Statistician (GradStat) status by the Royal Statistical Society. 

MSc Statistics (Research)

The research stream is similar to the MSc Statistics nine-month programme but involves a compulsory dissertation which replaces one unit's worth of optional courses and extends the length of the programme to 12 months. 

Graduates of the programme are awarded Graduate Statistician (GradStat) status by the Royal Statistical Society.

Graduate destinations

Students on this programme have excellent career prospects. Former students have taken up positions in consulting firms, banks and in the public sector. Many go on to take higher degrees. Graduates of the MSc are awarded Graduate Statistician (GradStat) status by the Royal Statistical Society.

Further information on graduate destinations for this programme



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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. Read more
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. Recently, we have updated our MSc in Statistics and introduced a new MSc in Statistics with Finance. Both programmes are accredited by the Royal Statistical Society.

*This course will be taught at the Canterbury campus*

Visit the website: https://www.kent.ac.uk/courses/postgraduate/167/statistics-with-finance

Course detail

The MSc in Statistics with Finance is accredited by the Royal Statistical Society (RSS) and is excellent preparation for careers in any field requiring a strong statistical background.

Purpose

This programme trains students for careers using statistics in the financial services industry. You study the statistical modelling underpinning much modern financial engineering combined with a deep understanding of core statistical concepts. The programme includes modelling of financial time series, risk and multivariate techniques.

Format and assessment

You undertake a substantial project in the area of finance or financial econometrics, 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 course includes training in the computer language R.

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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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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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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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.

About this degree

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 two core modules (30 credits), four to six optional modules (60 to 90 credits), up to two elective modules (up to 30 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 (15 credits)
  • Statistical Modelling and Data Analysis (15 credits)

Optional modules

Students must choose 15 credits from Group One Options. Of the remaining credits, students must choose a minimum of 30 and a maximum of 60 from Group Two, 15 credits from Group Three and a maximum of 30 credits from Electives.

Group One Options (15 credits)

  • Graphical Models (15 credits)
  • Probabilistic and Unsupervised Learning (15 credits)

Group Two Options (30 to 60 credits)

  • Advanced Deep Learning and Reinforcement Learning (15 credits)
  • Advanced Topics in Machine Learning (15 credits)
  • Applied Machine Learning (15 credits)
  • Approximate Inference and Learning in Probabilistic Models (15 credits)
  • Information Retrieval and Data Mining (15 credits)
  • Introduction to Deep Learning (15 credits)
  • Machine Vision (15 credits)
  • Statistical Natural Language Processing (15 credits)

Group Three Options (15 credits)

  • Applied Bayesian Methods (15 credits)
  • Statistical Design of Investigations (15 credits)
  • Statistical Inference (15 credits)

Please note: the availability and delivery of optional modules may vary, depending on your selection.

A list of acceptable elective modules is available on the Departmental page.

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.

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

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.

Recent career destinations for this degree

  • Data Scientist, Interpretive
  • Software Engineer, Google
  • Data Scientist, YouGov
  • Research Engineer, DeepMind
  • PhD in Computer Science, UCL

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.

Careers data is taken from the ‘Destinations of Leavers from Higher Education’ survey undertaken by HESA looking at the destinations of UK and EU students in the 2013–2015 graduating cohorts six months after graduation.

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 the 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.

Research Excellence Framework (REF)

The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.

The following REF score was awarded to the department: Computer Science

96% rated 4* (‘world-leading’) or 3* (‘internationally excellent’)

Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.



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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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This course equips students with the knowledge and statistical skills to make valuable contributions to medical research as well as public health in low-, middle- and high-income countries. Read more

This course equips students with the knowledge and statistical skills to make valuable contributions to medical research as well as public health in low-, middle- and high-income countries. Epidemiological methods underpin clinical medical research, public health practice and health care evaluation to investigate the causes of disease and to evaluate interventions to prevent or control disease.

Graduates enter careers in medical research, public health and community medicine, epidemiological field studies, drug manufacturers, government or NGOs.

The Nand Lal Bajaj and Savitri Devi Prize is awarded to the best project each year. The prize was donated by Dr Subhash Chandra Arya, former student, in honour of his parents Dr Nand Lal Bajaj and Mrs Savitri Devi.

- Full programme specification (pdf) (https://www.lshtm.ac.uk/files/epi_progspec.pdf)

Visit the website https://www.lshtm.ac.uk/study/masters/epidemiology

Additional Requirements

Additional requirements for the MSc Epidemiology are:

- evidence of numeracy skills (e.g. A level Mathematics or Statistics or a module with a good mark in their university degree)

- it is preferable for a student to have some work experience in a health-related field

Any prospective student who does not meet the above minimum entry requirement, but who has relevant professional experience, may still be eligible for admission. Please contact the course directors () if you are not sure whether this is the right course for you.

Objectives

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

- demonstrate advanced knowledge and awareness of the role of epidemiology and its contribution to other health-related disciplines

- choose appropriate designs and develop detailed protocols for epidemiological studies

- enter and manage computerised epidemiological data and carry out appropriate statistical analyses

- assess the results of epidemiological studies (their own or other investigators'), including critical appraisal of the study question, study design, methods and conduct, statistical analyses and interpretation

Structure

Term 1:

All students take the compulsory modules and usually take optional modules.

Compulsory modules are:

- Clinical Trials

- Epidemiology in Practice

- Extended Epidemiology

- Statistics for Epidemiology and Population Health .

Optional modules include:

- Demographic Methods

- Molecular Epidemiology of Infectious Diseases

Terms 2 and 3:

Students take a total of five modules, one from each timetable slot (Slot 1, Slot 2 etc.).

*Recommended modules

- Slot 1:

Study Design: Writing a Proposal (compulsory)

- Slot 2:

Statistical Methods in Epidemiology (compulsory)

- Slot 3:

Epidemiology of Non-Communicable Diseases*

Medical Anthropology and Public Health*

Social Epidemiology*

Spatial Epidemiology in Public Health*

Applied Communicable Disease Control

Control of Sexually Transmitted Infections

Current Issues in Safe Motherhood & Perinatal Health

Medical Anthropology and Public Health; Nutrition in Emergencies

Tropical Environmental Health

- Slot 4:

Environmental Epidemiology*

Epidemiology & Control of Communicable Diseases*

Genetic Epidemiology*

Design and Evaluation of Mental Health Programmes

Ethics, Public Health & Human Rights; Globalisation & Health; Nutrition Related Chronic Disease

- Slot 5:

Advanced Statistical Methods in Epidemiology*

AIDS

Applying Public Health Principles in Developing Countries

Integrated Vector Management

Principles and Practice of Public Health

Further details for the course modules - https://www.lshtm.ac.uk/study/masters/epidemiology#structure

Residential Field Trip

This course has a compulsory two-day residential retreat outside London. This is held on the Wednesday and Thursday of the first week in Term 1. This is included in the £200 field trip fee.

Day field trip to Oxford

A one-day field trip to Oxford usually takes place in November during reading week. Students are encouraged to attend but it is not a compulsory part of the course.

Project Report

During the summer months (July - August), students complete a written research project on a topic selected in consultation with their tutor, for submission by early September. This can be a data-analysis of an adequately powered study, a study protocol, a systematic review or an infectious disease modelling study. Students do not usually travel abroad to collect data.

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



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