Masters degrees in Statistical Modelling equip postgraduates with the skills to apply methods of statistics to the modelling and simulation of real-world systems and processes. These models then inform the decision-making process in many economic and political practices.
Related postgraduate specialisms include Data Visualisation and Modelling, and Statistics with Data Science. Entry requirements normally involve an undergraduate degree related to Mathematics.
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.
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.
Find more reasons to choose Statistical Science for the Life and Behavioural Sciencese at Leiden University.
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.
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:
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.
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:
Previous optional courses for 2017-18 include:
At the end of this programme you will have:
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.
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.
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.
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.
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.
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.
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.
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:
Semester Two:
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:
Semester Two:
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.
The School of Mathematics is the largest in the UK with an outstanding research reputation andfacilities .
Practical support and advice for current students and applicants is available from the Disability Advisory and Support Service. Email: [email protected]
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.
This Masters in Statistics will provide you with knowledge and experience of the principles, theory and practical skills of statistics.
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.
Group 1
Group 2
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.
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.
The MSc Statistics provides intensive training in statistics applicable to the social sciences, economics and finance.
The aim of the programme is to foster an interest in theoretical and applied statistics and equip you for work as a professional statistician. You will learn to analyse and critically interpret data, build statistical models of real situations, and use programming tools and statistical software packages.
The compulsory course will provide you with comprehensive coverage of fundamental aspects of probability and statistical methods and principles. It provides the foundations for the optional courses on more advanced statistical modelling, computational methods, statistical computing and advanced probability theory. Options also include specialist courses from the Departments of Methodology, Management, Mathematics, Economics and Social Policy.
Graduates of the programme are awarded Graduate Statistician (GradStat) status by the Royal Statistical Society.
The research stream is similar to the MSc Statistics nine-month programme but involves a compulsory dissertation which replaces one unit's worth of optional courses and extends the length of the programme to 12 months.
Graduates of the programme are awarded Graduate Statistician (GradStat) status by the Royal Statistical Society.
Students on this programme have excellent career prospects. Former students have taken up positions in consulting firms, banks and in the public sector. Many go on to take higher degrees. Graduates of the MSc are awarded Graduate Statistician (GradStat) status by the Royal Statistical Society.
Further information on graduate destinations for this programme
This Masters in Advanced Statistics will provide you with knowledge and experience of the principles, theory and practical skills of statistics.
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.
Summer (May – August)
Statistics project and dissertation (60) - assessed by a dissertation
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.
The MSc in Epidemiology at Imperial College London offers training in the methodology, design, conduct and interpretation of epidemiological studies on chronic and infectious diseases. The course is based within the School of Public Health.
Epidemiology is the study of the distribution, causes, and possible prevention and control, of diseases in populations.
The MSc in Epidemiology offers training in the methodology, design, conduct and interpretation of epidemiological studies on chronic and infectious diseases. It is particularly suitable for students who wish to acquire skills in epidemiology and biostatistics, and to get involved with research projects.
Upon completion of this course, students usually develop an academic career by beginning a PhD, or move on to work for public health organisations, pharmaceutical companies or non-governmental agencies.
INTRODUCTION TO INFECTIOUS DISEASE MODELLING
This module provides an overview of infectious disease modelling and develops skills in designing and analysing infectious disease models for public health policy. Students will be taught how to represent the characteristics of an infectious disease using a mathematical model, how to simulate that model using a computer, and how to analyse that model. Students will also learn how models have been applied in public health policy, and how models are designed to address a research question. Module leader: Dr Nim Pathy.
PRINCIPLES AND METHODS OF EPIDEMIOLOGY
This module ensures students will be familiar with the core concepts of epidemiology and acquire the skills necessary to describe, analyse, interpret and appraise epidemiological studies. Further modules and projects require such knowledge, and a good grasp of these basics is thus essential for successful completion of the degree. Module leaders: Dr Amanda Cross and Dr Filippos Filippidis.
DISEASE MASTERCLASS
This module aims to provide students with an overview of core health challenges and lines of epidemiological research being undertaken, across a range of infectious and non-communicable diseases. Module leader: Professor Tim Hallett.
INTRODUCTION TO STATISTICAL THINKING AND DATA ANALYSIS
The aim of this module is to give students an understanding of the importance of statistical thinking in epidemiology, randomised trials and public health, to enable them to critically evaluate the results of standard statistical analyses published in journal articles and to carry out a range of statistical analyses using R. Module leaders: Dr Victoria Cornelius and Hilary Watt.
Visit the course page to find out more about optional modules and the individual research project.
Professor Tim Hallett - Professor of Global Health
Dr Amanda Cross - Reader in Cancer Epidemiology
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.
The programme aims to provide graduates with the foundational principles and the practical experience needed by employers in the area of machine learning and statistics. Graduates of this programme will have had the opportunity to develop their skills by tackling problems related to industrial needs or to leading-edge research.
Students undertake modules to the value of 180 credits.
The programme consists of two core modules (30 credits), four to six optional modules (60 to 90 credits), up to two elective modules (up to 30 credits) and a research project (60 credits). Please note that not all combinations of optional modules will be available due to timetabling restrictions.
Core modules
Optional modules
Students must choose 15 credits from Group One Options. Of the remaining credits, students must choose a minimum of 30 and a maximum of 60 from Group Two, 15 credits from Group Three and a maximum of 30 credits from Electives.
Group One Options (15 credits)
Group Two Options (30 to 60 credits)
Group Three Options (15 credits)
Please note: the availability and delivery of optional modules may vary, depending on your selection.
A list of acceptable elective modules is available on the Departmental page.
Dissertation/report
All MSc students undertake an independent research project, which culminates in a dissertation of 10,000-12,000 words.
Teaching and learning
The programme is delivered through a combination of lectures, discussions, practical sessions and project work. Student performance is assessed through unseen written examinations, coursework, practical application and the project assessment process.
Further information on modules and degree structure is available on the department website: Computational Statistics and Machine Learning MSc
There is a strong national and international demand for graduates with skills at the interface of traditional statistics and machine learning. Substantial sectors of UK industry, including leading, large companies already make extensive use of computational statistics and machine learning techniques in the course of their business activities. Globally there are a large number of very successful users of this technology, many located in the UK. Areas in which expertise in statistics and machine learning is in particular demand include: finance, banking, insurance, retail, e-commerce, pharmaceuticals, and computer security. Graduates have gone on to further study at, for example, the Universities of Cambridge, Helsinki, Chicago, as well as at UCL. The MSc is also ideal preparation for a PhD, in statistics, machine learning or a related area.
Recent career destinations for this degree
Employability
Scientific experiments and companies now routinely generate vast databases and machine learning and statistical methodologies are core to their analysis. There is a considerable shortfall in the number of qualified graduates in this area internationally. CSML graduates have been in high demand for PhD positions across the sciences. In London there are many companies looking to understand their customers better who have hired our CSML graduates. Similarly graduates now work in companies in, amongst others, Germany, Iceland, France and the US in large-scale data analysis. The finance sector has also hired several graduates recently.
Careers data is taken from the ‘Destinations of Leavers from Higher Education’ survey undertaken by HESA looking at the destinations of UK and EU students in the 2013–2015 graduating cohorts six months after graduation.
The Centre for Computational Statistics and Machine Learning (CSML) is a major European Centre for machine learning having coordinated the PASCAL European Network of Excellence.
Coupled with the internationally renowned Gatsby Computational Neuroscience and the Machine Learning Unit, and UCL Statistical Science, this MSc programme draws on world-class research and teaching talents. The centre has excellent links with world-leading companies in internet technology, finance and related information areas.
The programme is designed to train students in both the practical and theoretical sides of machine learning. A significant grounding in computational statistics is also provided.
The Research Excellence Framework, or REF, is the system for assessing the quality of research in UK higher education institutions. The 2014 REF was carried out by the UK's higher education funding bodies, and the results used to allocate research funding from 2015/16.
The following REF score was awarded to the department: Computer Science
96% rated 4* (‘world-leading’) or 3* (‘internationally excellent’)
Learn more about the scope of UCL's research, and browse case studies, on our Research Impact website.
SEAS is a rising world-class center for engineering research, learning, and innovation.
