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Mathematics×

Masters Degrees in Statistics

We have 104 Masters Degrees in Statistics

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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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Looking for high-quality training in statistics for research or for professional life? If so, consider KU Leuven's MSc in Statistics, an interdisciplinary programme whose teaching is grounded in internationally-recognised research. Read more

Looking for high-quality training in statistics for research or for professional life? If so, consider KU Leuven's MSc in Statistics, an interdisciplinary programme whose teaching is grounded in internationally-recognised research. Choose from a number of approaches: biometrics, social behavioural and educational statistics, business statistics, industrial statistics, general statistical methodology, or an all-round statistics profile.

What is the Master of Statistics all about?

This master’s programme is offered by the Leuven Statistics Research Centre (LStat) of KU Leuven. It is accredited by the Royal Statistical Society. You’ll be trained intensively in both the theoretical and practical aspects of statistics. The programme will also help you develop a problem-solving attitude and teach you how to apply statistical methodology.

This is an initial Master's programme and can be followed on a full-time or part-time basis.

Structure

The 120-ECTS programme consists of a common core curriculum (one semester), option-specific courses (one semester), elective courses (one semester), and a master’s thesis.

  • Common core curriculum (30 ECTS): these course are compulsory for every student.
  • Option-specific courses (min. 32 ECTS). Within your selected option, you choose courses worth a total of minimum 32 ECTS.
  • Elective courses (30 ECTS)

To tailor the programme to your needs and interests, you choose one of the following options:

  • Biometrics;
  • Social, Behavioural, and Educational Statistics;
  • Business Statistics;
  • Industrial Statistics;
  • General Statistical Methodology;
  • Official Statistics;
  • All-round Statistics.

Department

LStat hosts international experts and is a stimulating environment for multidisciplinary statistical research. LStat is a privileged meeting space for statistics researchers from a range of domains:

  • Biostatistics;
  • Social, Behavioural and Educational Statistics;
  • Business and Industrial Statistics;
  • Statistical Methodology.

Objectives

The master of Statistics:

KNOWLEDGE AND INSIGHT

  • possesses thorough knowledge of and insight in the field of Statistics
  • has a perspective on the research and consulting aspects of one or more statistical fields within at least one of the following: biometry, industrial statistics, social behavioral and educational statistics, business statistics, statistical methodology, official statistics.
  • understands similarities and differences between different statistical methodology and practice across different sub-fields of statistics.

APPLYING KNOWLEDGE AND INSIGHT

  • has the competences and the insight to take the following steps in their own scientific research within a research team
  • can handle scientific quantitative research questions in the application area, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software.
  • has the skills and the habit to assess data quality and integrity.
  • is aware of the ethical, moral, legal, policy making, and privacy context of statistics and shows conduct accordingly.

DEVELOPING AN OPINION

  • appreciates the international nature of the field of statistical science.
  • is aware of the societal relevance of statistics.
  • can critically appraise methodology and challenge proposals for reported results of data analysis.

COMMUNICATION

  • can work in an intercultural, and international team, and understands the need and importance of working in a multidisciplinary team.
  • is an effective written and oral communicator, as well as an effective negotiator, both within their own field as well as towards other disciplines in the context of multidisciplinary projects.
  • is aware of the common stakeholders and the need for assertive and empathic interaction with them.

LEARNING SKILLS

  • is capable of acquiring new knowledge.

DEPENDING ON THE CHOSEN OPTION, THE STUDENT MASTERS FOLLOWING ADDITIONAL LEARNING OUTCOMES:

  • the master can act as a statistical consultant to subject-matter scientists and practitioners or/and as a collaborative researcher in an area of specialisation.

Career perspectives

As statistician, you'll be recruited by industry, banks or government institutions. You may find yourself designing clinical trials and supporting the biomedical sector, coaching research for new medicines, setting up and analysing psychological tests and surveys, performing financial risk analyses, statistically managing R&D projects and quality controls, or developing statistical software. And don't forget the academic world. The applications of statistics are very diverse, just like your professional options. 



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The MSc Statistics (Social Statistics) aims to provide high-level training in the theory and application of modern statistical methods, with a focus on methods commonly used in the social sciences. Read more

About the MSc programme

The MSc Statistics (Social Statistics) aims to provide high-level training in the theory and application of modern statistical methods, with a focus on methods commonly used in the social sciences. You will gain insights into the design and analysis of social science studies, including large and complex datasets, study the latest developments in statistics, and learn how to apply advanced methods to investigate social science questions.

The programme includes two core courses which provide training in fundamental aspects of probability and statistical theory and methods, the theory and application of generalised linear models, and programming and data analysis using the R and Stata packages. These courses together provide the foundations for the optional courses on more advanced statistical modelling, computational methods and statistical computing. Options also include specialist courses from the Departments of Methodology, Economics, Geography and Social Policy. Students on the taught master’s programme will take optional courses to the value of two units, while those on the research track will substitute one unit with a dissertation.

Graduate destinations

The programme will prepare graduates for work within the public sector, market research organisations and survey research organisations, or for further study.

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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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When you study mathematics and statistics at the University of Helsinki, some of the best mathematicians and statisticians in the world will be your instructors. Read more
When you study mathematics and statistics at the University of Helsinki, some of the best mathematicians and statisticians in the world will be your instructors. Studies in this Master’s programme will give you a solid basis for maths and statistics applications. Graduates of this Master’s programme find employment as researchers, teachers, and in demanding expert posts in the public and private sectors in Finland and abroad.

The Master’s programme in mathematics and statistics is based on top research. The teaching within the sub-programmes at the University of Helsinki follows a high standard and is highly valued, not just within Finnish academia but in global university rankings. Upon graduating from this Master’s programme, you will:
-Be an expert in the methods of mathematics or statistics.
-Have mastered the basics of another scientific discipline.
-Be able to apply scientific knowledge and methods.
-Be able to follow developments in mathematics and statistics.
-Know how to think critically, argue a point, and solve problems.
-Have excellent interaction skills and be assertive and creative.
-Understand the principles of ethical and sustainable development.
-Be well prepared to work as an expert and developer in your field.
-Be prepared for scientific postgraduate studies.

The University of Helsinki will introduce annual tuition fees to foreign-language Master’s programmes starting on August 1, 2017 or later. The fee ranges from 13 000-18 000 euros. Citizens of non-EU/EEA countries, who do not have a permanent residence status in the area, are liable to these fees. You can check this FAQ at the Studyinfo website whether or not you are required to pay tuition fees.

Programme Contents

The Master’s programme consists of courses in mathematics, applied mathematics, and statistics. The courses include group and lecture instruction, exercises, literature, and workshops. Most courses also include exams or project assignments. In addition, you can complete some courses independently, by taking literature-based exams. The instructors in this programme have received prizes for their high standard of teaching. The teaching methods used in the subjects in this Master's programme have been widely recognised in the media.

Selection of the Major

The specialisation subjects within the programme are:
-Analysis
-Mathematical physics and stocastics
-Applied analysis
-Computational science
-Mathematical logic
-Mathematical modelling
-Insurance and financial mathematics
-Algebra and topology
-Statistics
-European Master in Official Statistics, EMOS (based on the statistics education in the Faculty of Social Sciences).

You will select your specialisation subject during your first year.

Programme Structure

The Master’s programme comprises 120 credits, which you can complete in two years. The degree in mathematics includes:
-85 credits of advanced courses, including the Master’s thesis (Pro gradu, 30 credits).
-35 credits of other courses from your own or other programmes.
-Working-life orientation and career planning.
-Personal study plan.

The degree in statistics includes:
-25 credits of advanced mathematics courses.
-60 credits of advanced statistics courses, including the Master’s thesis (Pro gradu, 30 credits).
-35 credits of other courses e.g. more advanced courses in statistics, or intermediate courses in some other subject, in which you included basic courses in your BSc degree, or, module/s from other university programmes.
-Working-life orientation and career planning.
-Personal study plan.

The European Master in Official Statistics sub-programme includes:
-85 credits of advanced courses in statistics or mathematics, including the Master’s thesis (Pro gradu, 30 credits) and a traineeship.
-35 credits of other courses from your own or other programmes.
-Working-life orientation and career planning.
-Personal study plan.

Career Prospects

Graduates of the Master’s programme can find employment outside the university or continue with one of the doctoral programmes in mathematics and statistics. The Master’s programme will give you excellent capabilities for work in the public or private sector as an expert in mathematics and statistics, skills that are very sought after in the job market both in Finland and abroad. The banking, investment, and insurance fields, for instance, value mathematicians and statisticians very highly internationally. Many of our graduates work in research and development or as teachers in various educational institutions. Graduates from this programme have excellent chances to find employment corresponding to their education.

Internationalization

The international nature of the programme is implemented in many ways:
-Research within the disciplines of the degree programme is of high international standard and is highly regarded.
-Teaching staff and research collaboration within the programme are international.
-The atmosphere of the programme is international, several international students are admitted each year.
-Theses and projects may be completed within international projects.
-There are opportunities for a student exchange period in many foreign universities.

Research Focus

The research focus within the disciplines in the degree programme are e.g.
-Geometric analysis and measurement theory
-Analysis in metric spaces
-Partial differential equations
-Functional analysis
-Harmonic analysis
-Mathematical physics
-Stochastics
-Inversion problems
-Mathematical logic and set theory
-Biomathematics
-Time series analysis
-Biometry
-Econometry
-Psychometrics
-Social statistics

The programme is part of the Analyysin ja dynamiikan (Analysis and dynamics) and the Inversio-ongelmien (Inversion problems) centres of excellence.

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The MSc in Medical Statistics combines in-depth training in mainstream advanced statistical modelling with a specialisation in medical applications. Read more

The MSc in Medical Statistics combines in-depth training in mainstream advanced statistical modelling with a specialisation in medical applications.

This flexible degree programme allows you to blend theoretical and applied statistical disciplines, ideal for training in medical statistics. It combines compulsory and optional modules allowing you to train in a range of statistical techniques (and transferable skills) suitable for either careers in medical statistics and research-related professions, or for further academic research.

Options within the course vary from mainstream topics in statistical methodology to more specialised areas such as epidemiology and biostatistics.

You can also study this programme part time over 24 months.

If you do not meet the full academic entry requirements then you may wish to consider the Graduate Diploma in Mathematics. This course is aimed at students who would like to study for a mathematics related MSc course but do not currently meet the entry requirements. Upon completion of the Graduate Diploma, students who meet the required performance level will be eligible for entry onto a number of related MSc courses, in the following academic year.

Accreditation

Accreditation from the Royal Statistical Society is pending.

Course content

The first two semesters of your course will consist of taught modules, and in the third semester you’ll devote your time to a major dissertation in statistics or a research project in applied epidemiology and biostatistics. Within each semester you have the opportunity to choose from a range of optional modules, allowing you to specialise in the area of study of most interest to you.

You’ll be taught by experts from the School of Mathematics, The Centre for Epidemiology and Biostatistics, and The Clinical Trials Research Unit at Leeds, each bringing a different perspective to the subject of medical statistics.

You’ll be supervised for both your taught modules and your research project by professionals across the teaching units and you will be given the opportunity to utilise existing links with individual clinicians and medical research groups in the University of Leeds, Leeds NHS trust, and the Department of Health’s Information Centre in Leeds.

Throughout the course you’ll learn about new developments in statistics and be provided with the opportunity to undertake data analysis for a wide variety of statistical problems. You’ll build an appreciation of theoretical and practical perspectives on issues in medical statistics, whilst developing the ability to select and apply appropriate statistical methods for the analysis of medical data using suitably chosen software packages.

Course structure

Compulsory modules

  • Introduction to Clinical Trials 15 credits
  • Core Epidemiology 15 credits
  • Introduction to Modelling 15 credits
  • Statistical Computing 15 credits

Optional modules

  • Research Project 60 credits
  • Multilevel and Latent variable Modelling 15 credits
  • Professional Spine 15 credits
  • Independent Learning Skills in Epidemiology and Biostatistics 15 credits
  • Advanced Modelling Strategies 15 credits
  • Advanced Epidemiological Techniques 15 credits
  • Linear Regression and Robustness 15 credits
  • Statistical Theory 15 credits
  • Multivariate Analysis 10 credits
  • Time Series 10 credits
  • Bayesian Statistics 10 credits
  • Generalised Linear Models 10 credits
  • Introduction to Statistics and DNA 10 credits
  • Linear Regression and Robustness and Smoothing 20 credits
  • Multivariate and Cluster Analysis 15 credits
  • Time Series and Spectral Analysis 15 credits
  • Bayesian Statistics and Causality 15 credits
  • Generalised Linear and Additive Models 15 credits
  • Independent Learning and Skills Project 15 credits
  • Dissertation in Statistics 60 credits
  • Statistics and DNA 15 credits

For more information on typical modules, read Medical Statistics MSc in the course catalogue

Learning and teaching

This course is taught by experts from the School of Mathematics, the Centre for Epidemiology and Biostatistics, and the Clinical Trials Research Unit at Leeds. You’ll study a mixture of modules taught by specialists in each area depending on your chosen optional modules. Teaching is done through a combination of lectures, small group workshops and a small number of practical exercises.

Assessment

The taught course is primarily assessed by end-of-semester examinations with a small component of continuous assessment. The project is assessed by a written dissertation and a short oral presentation.

Career opportunities

There is a shortage of well-qualified statisticians in the UK and other countries. Numeracy, in general, is an attribute keenly sought after by employers.

The emergence of data mining and analysis means that demand for statisticians is growing across a wide range of professions - actuarial, betting and gaming industries, charitable organizations, commercial, environmental, financial, forensic and police investigation, government, market research, medical and pharmaceutical organisations. The course is designed specifically to meet this demand.

As a graduate of medical statistics you will have specialist knowledge that will help you progress your career into areas such as medical or epidemiological research. There are several aims to medical research, all of which involve a significant amount of statistics, monitoring and surveillance of health and disease, establishing causes of disease or factors associated with death or disease, detecting disease, preventing death or disease and evaluating treatments for disease. Medical statisticians looking to follow a career in medical research are mainly employed by pharmaceutical companies, university medical schools, research units and the NHS.

A medical statistician could also go into consultancy giving advice to researchers looking to set up clinical trials and needing their project to be assessed before funding is granted.

Careers support

We encourage you to prepare for your career from day one. That’s one of the reasons Leeds graduates are so sought after by employers.

The Careers Centre and staff in your faculty provide a range of help and advice to help you plan your career and make well-informed decisions along the way, even after you graduate. Find out more at the Careers website.




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

Degree information

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

Students undertake modules to the value of 180 credits.

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

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

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

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

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

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

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

Careers

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

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

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

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

Why study this degree at UCL?

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

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

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

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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.

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

Why this programme

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

Programme structure

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

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

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

Career prospects

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

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The Financial Statistics stream of the MSc Statistics programme is mainly intended for students wishing to pursue careers in the finance industry or as a stepping stone towards PhD study in statistics for finance. Read more

About the MSc programme

The Financial Statistics stream of the MSc Statistics programme is mainly intended for students wishing to pursue careers in the finance industry or as a stepping stone towards PhD study in statistics for finance. It provides high-level training in statistics with applications in finance and econometrics. You will learn to analyse and critically interpret data, build statistical models of real situations, and use statistical software packages.

There are three compulsory courses. Statistical Inference: Principles, Methods and Computation will provide comprehensive coverage of fundamental aspects in probability and statistical methods and principles, while Time Series will provide solid training in statistical time series analysis. Modern data analytics, together with topics such as financial time series, asset pricing and portfolio choice, and some aspects of continuous-time finance will be provided in the course Financial Statistics. You will also learn to code in the R statistical computing environment.

Students on the taught master’s programme will take optional courses to the value of two units, including selected options from the Department of Finance. Those on the research track will substitute one unit with a dissertation, making the research track a 12 month programme.

Graduates of the programme are awarded Graduate Statistician (GradStat) status by the Royal Statistical Society if a specific combination of modules is taken.

Graduate destinations

The programme provides excellent opportunities for employment and further study. The programme is also conditionally accredited by the Royal Statistical Society. This means that although an accreditation is given, it will only lead to the award of ‘Graduate Statistician’ when students have taken a specific combination of modules.

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The MSc in Statistics is a flexible degree programme enabling students from a wide range of backgrounds to both broaden and deepen their understanding of statistics. Read more

The MSc in Statistics is a flexible degree programme enabling students from a wide range of backgrounds to both broaden and deepen their understanding of statistics.

The programme combines in-depth training in mainstream advanced statistical modelling with a broad range of specialisations - from financial mathematics to statistical bioinformatics; from shape analysis to risk management. You’ll also develop your understanding of research methods in statistics from writing styles to programming skills, preparing you for a wide range of careers in different sectors – and then apply them to a substantial research project of your own.

If you do not meet the full academic entry requirements then you may wish to consider the Graduate Diploma in Mathematics. This course is aimed at students who would like to study for a mathematics related MSc course but do not currently meet the entry requirements. Upon completion of the Graduate Diploma, students who meet the required performance level will be eligible for entry onto a number of related MSc courses, in the following academic year.

Accreditation

Royal statistical Society Accreditation

On graduation you will be eligible for the Graduate Statistician (GradStat) status and after five years’ experience this can be converted into the professional status of Chartered Statistician (CStat).

Being a chartered statistician provides employers, contractors and collaborators of statisticians a level of assurance that you are at the forefront of your field and is a mark of accomplishment to society.

Course content

The first two semesters of your course will consist of taught modules and in the third semester you will devote your time to a major dissertation.

Within each semester there is one compulsory module and a range of optional modules, allowing you to specialise in the area of statistics of most interest to you. Specialist areas within the course include biological or financial applications of statistics or broad based statistical expertise.The core modules will develop your skills to lay the groundwork of the programme. You’ll learn a range of statistical computing techniques and build research skills such as academic writing, programming and literature searches. Options within the course vary from mainstream topics in statistical methodology to more specialised areas and reflect specific research interests of our academic staff - examples include statistical shape analysis, directional data, statistical genetics and stochastic financial modelling.

Course structure

Compulsory modules

  • Independent Learning and Skills Project 15 credits
  • Statistical Computing 15 credits
  • Dissertation in Statistics 60 credits

Optional modules

  • Introduction to Clinical Trials 15 credits
  • Core Epidemiology 15 credits
  • Multilevel and Latent variable Modelling 15 credits
  • Advanced Modelling Strategies 15 credits
  • Advanced epidemiological techniques 15 credits
  • Mathematical Biology 15 credits
  • Linear Regression and Robustness 15 credits
  • Statistical Theory 15 credits
  • Stochastic Financial Modelling 15 credits
  • Multivariate Analysis 10 credits
  • Time Series 10 credits
  • Bayesian Statistics 10 credits
  • Generalised Linear Models 10 credits
  • Introduction to Statistics and DNA 10 credits
  • Discrete Time Finance 15 credits
  • Continuous Time Finance 15 credits
  • Risk Management 15 credits
  • Advanced Mathematical Biology 20 credits
  • Linear Regression and Robustness and Smoothing 20 credits
  • Multivariate and Cluster Analysis 15 credits
  • Time Series and Spectral Analysis 15 credits
  • Bayesian Statistics and Causality 15 credits
  • Generalised Linear and Additive Models 15 credits
  • Statistics and DNA 15 credits

For more information on typical modules, read Statistics MSc in the course catalogue

Learning and teaching

Teaching is by lectures, tutorials, seminars and supervised research projects.

Assessment

The taught course is primarily assessed by end-of-semester examinations with a small component of continuous assessment. The semester three project is assessed by a written dissertation and a short oral presentation.

Career opportunities

There is a shortage of well-qualified statisticians in the UK and other countries. Numeracy, in general, is an attribute keenly sought after by employers.

The emergence of data mining and analysis means that demand for statisticians is growing across a wide range of professions - actuarial, betting and gaming industries, charitable organisations, commercial, environmental, financial, forensic and police investigation, government, market research, medical and pharmaceutical organisations. The course is designed specifically to meet this demand.

Many statistical careers require people educated to masters degree level. This course is designed to build on existing mathematical skills and deepen knowledge of statistics in order for you to access a variety of professions or pursue further research as a PhD student.

Careers support

We encourage you to prepare for your career from day one. That’s one of the reasons Leeds graduates are so sought after by employers.

The Careers Centre and staff in your faculty provide a range of help and advice to help you plan your career and make well-informed decisions along the way, even after you graduate. Find out more at the Careers website.



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The MSc Statistics offers a modern advanced curriculum in statistics which will enable you to broaden and deepen your understanding of the subject and its applications. Read more

Overview

The MSc Statistics offers a modern advanced curriculum in statistics which will enable you to broaden and deepen your understanding of the subject and its applications.

The programme will provide you with specific techniques and skills suitable for a professional career in statistics or as a solid basis for research in the area.

Optional topics typically include generalised linear models, Markov Chain Monte Carlo, the bootstrap, multivariate analysis, spatial statistics, time series and forecasting, multilevel models, stochastic finance, and shape and image analysis.

This course is accredited by the Royal Statistical Society.

Key facts:

- This course is informed by the work being carried out in the Statistics and Probability research group.

- The School of Mathematical Sciences is one of the largest and strongest mathematics departments in the UK, with over 50 full-time academic staff.

- In the latest independent Research Assessment Exercise, the School ranked 8th in the UK in "research power" across the three subject areas within the School of Mathematical Sciences (Pure Mathematics, Applied Mathematics, Statistics and Operational Research).

Modules

Advanced Stochastic Processes

Applications of Statistics

Computational Statistics

Fundamentals of Statistics

Medical Statistics

Statistics Dissertation

Time Series and Forecasting

Topics in Biomedical Statistics

English language requirements for international students

IELTS: 6.0 (with no less than 5.5 in any element)

Further information



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This programme combines in-depth training in mainstream advanced statistical modelling with a specialisation in financial mathematics. Read more

This programme combines in-depth training in mainstream advanced statistical modelling with a specialisation in financial mathematics.

The MSc in Statistics with Applications to Finance is a focused degree programme which enables you to both broaden and deepen your understanding of statistics and financial applications. As well as the statistics expertise within the School of Mathematics, the MSc also draws on experience in Financial Mathematics, a joint venture between the School of Mathematics and Leeds University Business School.

If you do not meet the full academic entry requirements then you may wish to consider the Graduate Diploma in Mathematics. This course is aimed at students who would like to study for a mathematics related MSc course but do not currently meet the entry requirements. Upon completion of the Graduate Diploma, students who meet the required performance level will be eligible for entry onto a number of related MSc courses, in the following academic year.

Accreditation

Royal Statistical Society Accreditation

On graduation you will be eligible for the Graduate Statistician (GradStat) status and after five years’ experience this can then be converted into the professional status of Chartered Statistician (CStat).

Being a chartered statistician provides employers, contractors and collaborators of statisticians a level of assurance that you are at the forefront of your field and is a mark of accomplishment to society.

Course content

The first two semesters of your course will consist of taught modules and in the third semester you will devote your time to a major dissertation. Within each semester you will take three compulsory modules. However, you can tailor the course to meet your individual needs through selection of a further module in each semester from a variety of options.

In the first semester you’ll be introduced to statistical computing with an emphasis on sampling-based methods including Markov Chain Monte Carlo. You’ll also explore financial investments such as stock and shares and develop the necessary probabilistic tools to evaluate risks within the market.

In semester two you’ll look at risk assessment in detail, gaining comprehensive knowledge of mathematical and practical approaches to financial risk management. Avoiding the disastrous consequences of badly managed risk requires detailed mathematical knowledge of how to quantify financial risk. You’ll also learn about pricing financial assets and forecasting future values within a time series.

Your third semester will be taken up by a dissertation in statistics. This will consist of a three-month research project undertaken in the summer on a topic chosen in conjunction with project supervisors, culminating in a dissertation on that project.

Course structure

Compulsory modules

  • Stochastic Financial Modelling 15 credits
  • Discrete Time Finance 15 credits
  • Continuous Time Finance 15 credits
  • Risk Management 15 credits
  • Computations in Finance 15 credits
  • Time Series and Spectral Analysis 15 credits
  • Statistical Computing 15 credits
  • Dissertation in Statistics 60 credits

Optional modules

  • Linear Regression and Robustness 15 credits
  • Statistical Theory 15 credits
  • Multivariate Analysis 10 credits
  • Bayesian Statistics 10 credits
  • Generalised Linear Models 10 credits
  • Introduction to Statistics and DNA 10 credits
  • Models in Actuarial Science 15 credits
  • Linear Regression and Robustness and Smoothing 20 credits
  • Multivariate and Cluster Analysis 15 credits
  • Bayesian Statistics and Causality 15 credits
  • Generalised Linear and Additive Models 15 credits
  • Independent Learning and Skills Project 15 credits
  • Statistics and DNA 15 credits

For more information on typical modules, read Statistics with Applications to Finance MSc in the course catalogue

Learning and teaching

This programme is taught jointly between the School of Mathematics and the Leeds University Business School. You will study a mixture of modules taught by specialists in each school though a combination of lectures and small group workshops.

Assessment

The taught course is primarily assessed by end-of-semester examinations with a small component of continuous assessment. The semester three project is assessed by a written dissertation and a short oral presentation.

Career opportunities

There is a shortage of well-qualified statisticians in the UK and other countries. Numeracy, in general, is an attribute keenly sought after by employers.

The emergence of data mining and analysis means that demand for statisticians is growing across a wide range of professions - actuarial, betting and gaming industries, charitable organisations, commercial, environmental, financial, forensic and police investigation, government, market research, medical and pharmaceutical organisations. The course is designed specifically to meet this demand.

Many statistical careers require people educated to masters degree level. This course is designed to build on existing mathematical skills and deepen knowledge of statistics in order for you to access a variety of professions or pursue further research as a PhD student.

Careers support

We encourage you to prepare for your career from day one. That’s one of the reasons Leeds graduates are so sought after by employers.

The Careers Centre and staff in your faculty provide a range of help and advice to help you plan your career and make well-informed decisions along the way, even after you graduate. Find out more at the Careers website.



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

Degree information

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

Students undertake modules to the value of 180 credits.

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

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

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

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

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

Careers

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

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

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

Why study this degree at UCL?

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

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

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

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