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Masters Degrees (Statistical)

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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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Data science brings together computational and statistical skills for data-driven problem solving, which is in increasing demand in fields such as marketing, pharmaceutics, finance and management. Read more

Data science brings together computational and statistical skills for data-driven problem solving, which is in increasing demand in fields such as marketing, pharmaceutics, finance and management. This MSc will equip students with the analytical tools to design sophisticated technical solutions using modern computational methods and with an emphasis on rigorous statistical thinking.

About this degree

The programme combines training in core statistical and machine learning methodology, beginning at an introductory level, with a range of optional modules covering more specialised knowledge in statistical computing and modelling. Students choosing the statistics specialisation will take one compulsory module and up to two additional modules in computer science, with the remaining modules (including the research project) taken mainly from within UCL Statistical Science.

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 dissertation/report (60 credits).

Core modules

  • Introduction to Statistical Data Science
  • Introduction to Machine Learning
  • Statistical Design of Investigations
  • Statistical Computing

Optional modules

At least two from a choice of Statistical Science modules including:

  • Applied Bayesian Methods
  • Decision & Risk
  • Factorial Experimentation
  • Forecasting
  • Quantitative Modelling of Operational Risk and Insurance Analytics
  • Selected Topics in Statistics
  • Stochastic Methods in Finance I
  • Stochastic Methods in Finance II
  • Stochastic Systems

Up to two from a choice of Computer Science modules including:

  • Affective Computing and Human-Robot Interaction
  • Graphical Models
  • Statistical Natural Language Processing
  • Information Retrieval & Data Mining

Dissertation/report

All students undertake an independent research project, culminating in a dissertation usually of 10,000-12,000 words. Workshops running during the teaching terms provide preparation for this project and cover the communication of statistics.

Teaching and learning

The programme is delivered through a combination of lectures, tutorials and classes, some of which are dedicated to practical work. Assessment is through written examination and coursework. The research project is assessed through the dissertation and a 15-minute presentation.

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

Careers

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

The Data Science MSc is a new programme with the first cohort of students due to graduate in 2017. Recent career destinations for graduates of the related Statistics MSc include:

  • Management Associate, HSBC
  • Statistical Analyst, Nielsen
  • PhD in Statistics, UCL
  • Mortgage Specialist, Citibank
  • Research Assistant Statistician, Cambridge Institute of Public Health

Employability

Data science professionals are likely to be increasingly sought after as the integration of statistical and computational analytical tools becomes essential in all kinds of organisations and enterprises. A thorough understanding of the fundamentals is to be expected from the best practitioners. For instance, in applications in marketing, the healthcare industry and banking, computational skills should should be accompanied by statistical expertise at graduate level. Data scientists need a broad background knowledge so that they will be able to adapt to rapidly evolving challenges. Recent graduates from the related Statistics MSc have been offered positions as research analysts or consultants, and job opportunities in these areas are increasing.

Why study this degree at UCL?

UCL Statistical Science has a broad range of research interests, but has particular strengths in the area of computational statistics and in the interface between statistics and computer science.

UCL's Centre for Computational Statistics and Machine Learning, in which many members of the department are active, has a programme of seminars, masterclasses and other events. UCL's Centre for Data Science and Big Data Institute are newer developments, again with strong involvement of the department, where emphasis is on research into big data problems.

UCL is one of the founding members of the Alan Turing Institute, and both UCL Statistical Science and UCL Computer Science will be playing major roles in this exciting new development which will make London a major focus for big data research.

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: Statistical Science

82% 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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Statistical science skills are powerful tools that play a valuable role in all pure and applied sciences as well as in finance, law and marketing. Read more

Statistical science skills are powerful tools that play a valuable role in all pure and applied sciences as well as in finance, law and marketing. The quantitative skills training provided by this MSc can lead to new and exciting opportunities in industry, medicine, government, commerce or research.

About this degree

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

Students undertake modules to the value of 180 credits.

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

Core modules

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

Optional modules

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

Dissertation/report

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

Teaching and learning

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

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

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

Careers

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

Recent career destinations for this degree

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

Employability

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

Why study this degree at UCL?

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

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



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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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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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Medical statistics is a fundamental scientific component of health research. Medical statisticians interact with biomedical researchers, epidemiologists and public health professionals and contribute to the effective translation of scientific research into patient benefits and clinical decision-making. Read more

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

About this degree

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

Students undertake modules to the value of 180 credits.

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

Core modules

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

Optional modules

  • At least one from:
  • Statistics for Interpreting Genetic Data
  • Bayesian Methods in Health Economics
  • and at least one from:
  • Epidemiology
  • Statistical Design of Investigations

Dissertation/report

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

Teaching and learning

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

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

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

Careers

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

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

Recent career destinations for this degree

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

Employability

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

Why study this degree at UCL?

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

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



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The course trains students from a variety of academic backgrounds to work as statisticians in various sectors including higher education, research institutions, the pharmaceutical industry, central government and national health services. Read more

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

The PSI Andrew Hewett Prize is founded in memory of Andrew Hewett, an alumnus of the School and awarded by the PSI (Statisticians in the Pharmaceutical Industry) to the best student on the course.

Duration: one year full-time or part-time over two years. Modes of study explained.

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

Visit the website https://www.lshtm.ac.uk/study/masters/medical-statistics

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

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

Intercalating this course

(https://www.lshtm.ac.uk/study/courses/ways-study/intercalating-study-masters-degree)

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

Why intercalate with us?:

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

- World’s leading research-focused graduate school (Times Higher Education World Rankings, 2013)

- Third in the world for social science and public health (US News Best Global Universities Ranking, 2014)

- Second in UK for research impact (Research Exercise Framework 2014)

- Top in Europe for impact (Leiden Ranking, 2015)

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

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

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

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

Objectives

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

- select appropriate study designs to address questions of medical relevance

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

- use various software packages for statistical analysis and data management

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

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

- explore current and anticipated developments in medical statistics

Structure

Term 1:

All students take five compulsory modules:

- Foundations of Medical Statistics

- Introduction to Statistical Computing (Stata/SAS/R)

- Clinical Trials

- Basic Epidemiology

- Robust Statistical Methods

Terms 2 and 3:

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

*Recommended modules

- Slot 1:

Generalised Linear Models (compulsory)

- Slot 2:

Statistical Methods in Epidemiology (compulsory)

- Slot 3:

Analysis of Hierarchical & Other Dependent Data*

Epidemiology of Non-Communicable Diseases

Modelling & the Dynamics of Infectious Diseases

Social Epidemiology

- Slot 4:

Survival Analysis and Bayesian Statistics (compulsory)

- Slot 5:

Advanced Statistical Modelling*

Advanced Statistical Methods in Epidemiology*

Further details for the course modules - https://www.lshtm.ac.uk/study/courses/masters-degrees/module-specifications

Project Report

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

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



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This Masters in Biostatistics 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 Biostatistics 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.

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

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

Group 1

  • Data analysis 
  • Professional skills.

Group 2

  • Data management and analytics using SAS
  • Design of experiments 
  • Functional data analysis 
  • Spatial statistics 
  • Statistical genetics 
  • 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) - applying statistical methods and modelling to data collected from research in a biomedical discipline, 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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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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With a Master's degree in Applied Statistics you will have the knowledge and the qualifications to assume a leading role in the design of statistical surveys and to contribute to the development of statistical analysis. Read more

With a Master's degree in Applied Statistics you will have the knowledge and the qualifications to assume a leading role in the design of statistical surveys and to contribute to the development of statistical analysis. Potential employers are banks and insurance companies, market research firms, as well as the industry sector, especially the pharmaceutical industry.

The Master's programme prepares students for careers as statisticians in both the private and the public sectors. Statistical methods are used all over the world and students of the Master's programme gain access to the international job market.

The programme gives students training for the profession of statistician. The programme also prepares students for studies at the doctoral level. The training covers many areas of statistical theory giving opportunities to work in different fields of application of statistical methodology, although a focus of the programme is on applications within the economic and social sciences. The Master in Applied Statistics gives deep and wide theoretical knowledge with a focus on practical application of theory and methodology. Statisticians usually work closely with colleagues who have training in other subjects than statistics, in particular experts on the actual area of application. Here the statistician is considered as a special resource for implementation of surveys and statistical analysis. The ability to communicate with non-statisticians is therefore important. This is due in part to the need to identify the information requirements and the restrictions surrounding the statistical study, but also in order to communicate the design chosen for the study and the results obtained. Communication with non-statisticians is practised throughout the programme.

The programme is made up of four semesters. The programme starts with a course in mathematics and a course in statistical theory. During the first year, students will also take two courses in econometrics, two courses on the theories and methods in the area of survey methodology and courses in computational statistics and Bayesian statistics. The third and the fourth semesters each includes two courses and one 15 credit Master's thesis. The student writes two Master's theses on the programme, one during the third semester and one during the fourth semester. After completion of the programme, the student has the training needed to take a leading role in the design and implementation of statistical surveys and analyses, as well as the ability to contribute to the development of statistical methodology.



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This is a part-time, modular based programme for health professionals who wish to strengthen their statistical skills and ability to analyse data. Read more
This is a part-time, modular based programme for health professionals who wish to strengthen their statistical skills and ability to analyse data.

Students will gain the confidence in carrying out the methods that are widely used in medical statistics, and interpreting the results for the practice of evidence-based health care. The flexible modular structure has been devised for busy professionals and to fit with the structure of specialist training. The regulations allow students to take up to four years to complete the MSc.

This is a joint programme between the Nuffield Department of Primary Care Health Sciences and the Department for Continuing Education's Continuing Professional Development Centre. The Programme works in collaboration with the renowned Centre for Evidence-Based Medicine in Oxford.

This course is designed for doctors, nurses, pharmacists, midwives and other healthcare professionals, seeking to consolidate their understanding and ability in medical statistics. Core modules introduce the students to methods for observational and clinical trials research. Optional modules offer the students skills in growth areas such as systematic review, meta-analysis, and big data epidemiology, or specialist areas such as statistical computing, diagnosis and screening research and others. Teaching is tailored to non-statisticians and delivered by an experienced team of tutors from University of Oxford who bridge the disciplines of medical statistics and evidence-based health care.

This programme guides students through core and optional modules and a dissertation to a qualification in the application of medical statistics to evidence-based health care. Compared to the main EBHC programme, this will suit those with basic statistical understanding who seek training who now seek deeper understanding on a broader base of statistical methods.

Visit the website https://www.conted.ox.ac.uk/about/msc-in-ebhc-medical-statistics

Course aims

The course aims to give healthcare professionals high competence in the concepts, methods, terminology and interpretation of medical statistics; and hence, enhance their ability to carry out their own research and to interpret published evidence.

• Gain competence in execution and interpretation of core statistical techniques used by medical statisticians (outside the context of clinical trials), particularly those used in multivariable analyses: multiple linear regression, logistic regression, and survival modelling; statistical analysis plans and statistical reporting.
• Gain competence in execution and interpretation of core statistical techniques used by medical statisticians in clinical trials.
• Gain competence in execution and interpretation of four other areas, selected by the student from the following options: meta-analysis; systematic review; big data epidemiology; statistical computing; diagnosis and screening; study design and research methods.
• Gain hands-on experience, supervised by a senior member of our medical statistics team, of the analysis or meta-analysis of healthcare data, in order to address a question in evidence-based health care.

Programme details

The MSc in EBHC Medical Statistics is a part-time course.

There are two compulsory modules, four option modules (two from group 1 and two more either from either group 1 or 2) and a dissertation.

Compulsory Modules

• Essential Medical Statistics
• Statistics for Clinical Trials

Optional Modules – 1

• Meta-analysis
• Big Data Epidemiology
• Statistical Computing with R and Stata (online)

Optional Modules – 2

• Introduction to Study Design and Research Methods
• Systematic Reviews
• Evidence-based Diagnosis and Screening

A module is run over an eight-week cycle where the first week is spent working on introductory activities using a Virtual Learning Environment, the second week is spent in Oxford for the face-to-face teaching week, there are then four post-Oxford activities (delivered through the VLE) which are designed to help you write your assignment. You then have a week of personal study and you will be required to submit your assignment electronically the following week.

Online modules are delivered entirely through a Virtual Learning Environment with the first week allocated to introductory activities. There are ten units to work through which are released week-by-week, you then have five weeks of personal study with use of a revision forum and then you will be required to submit your assignment electronically the following week.

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Statistics is the study of the collection, analysis, interpretation, presentation and organisation of data. Read more

About the course

Statistics is the study of the collection, analysis, interpretation, presentation and organisation of data. Statistical analysis and data analytics is listed as one of the highly desirable skills employers are looking for, and with data becoming an ever increasing part of modern life, the talent to extract information and value from complex data is scarce.

The new Statistics and Data Analytics MSc is designed to train the next generation of statisticians with a focus on the field of data analytics. Employers expect skills in both statistics and computing. This master’s programme will provide a unique and coherent blend of modern statistical methods together with the associated computational skills that are essential for handling large quantities of unstructured data. This programme offers training in modern statistical methodology, computational statistics and data analysis from a wide variety of fields, including financial and health sectors.

Aims

Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. The aim of the MSc Statistics and Data Analytics is to produce graduates that:

- Are equipped with a range of advanced statistical methods and the associated computational skills for handling large quantities of unstructured data
- Have developed a critical awareness of the underlying needs of industry and commerce through relevant case studies
- Are able to analyse real-world data and to communicate the output of sophisticated statistical models in order to inform decision making processes
- Have the necessary computational skills to build and analyse simple/appropriate solutions using statistical Big Data technologies

Course Content

Compulsory modules:

Quantitative Data Analysis
Research Methods and Case Studies
Computer Intensive Statistical Methods
Modern Regression and Classification
Data Visualisation
Big Data Analytics
Time Series Modelling
Network Models
Dissertation

Statistics with Data Analytics Dissertation
Towards the end of the Spring Term, students will choose a topic for an individual research project, which will lead to the preparation and submission of an MSc dissertation. The project supervisor will usually be a member of the Brunel Statistics or Financial Mathematics group. In some cases the project may be overseen by an external supervisor based in industry or another academic institution..

Teaching

You’ll be taught using a range of teaching methods, including lectures, computer labs and discussion groups. Lectures are supplemented by computer labs and seminars/exercise classes and small group discussions. The seminars will be useful for you to carry out numerical data analysis, raise questions arising from the lectures, exercise sheets, or self-studies in an interactive environment.

The first term provides a thorough grounding in core programming, statistical and data analysis skills. In addition to acquiring relevant statistical and computational methods, students are encouraged to engage with real commercial and/or industrial problems through a series of inspiring case studies delivered by guest speakers. Support for academic and personal growth is provided through a range of workshops covering topics such as data protection, critical thinking, presentation skills and technical writing skills.

You’ll also complete an individual student project supervised by a relevant academic on your chosen topic.

Assessment

The assessment of all learning outcomes is achieved by a balance of coursework and examinations. Assessments range from written reports/essays, group work, presentations through to conceptual/statistical modelling and programming exercises, according to the demands of particular modular blocks. Additionally, class tests are used to assess a range of knowledge, including a range of specific technical subjects.

Special Features

The Statistics Group is a growing, highly-research active group, with collaborations across industry and academia, including engineering and pharmaceutical companies, Cambridge University and Imperial College London

Brunel’s Mathematics department is a member of the London Graduate School in Mathematical Finance. This consortium of mathematical finance groups comprises Birkbeck College, Brunel University London, Imperial College London, King’s College London, London School of Economics and Political Science and University College London. 

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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 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 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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