University College London (UCL)

Statistics MSc

Teaching and learning

The primary method of communicating information and stimulating interest is through lectures, which provide you with a formal knowledge base from which your understanding can be developed. Understanding of lecture material is reinforced by problem classes, computer workshops and group tutorials, as well as by self-study. Peer-assisted learning, discussion with other students and individual discussion with staff also support the learning process.

Whereas lectures provide the primary vehicle for accumulating a knowledge base, your intellectual, academic and research skills will mainly be developed outside of the lecture theatre, for example, by tackling and discussing problems set on a regular (usually weekly) basis. Some coursework requires you to develop your thinking beyond rote learning, and to link ideas between different modules. You will be encouraged to reason openly through discussion of set problems in tutorials. For some modules, workshops allow you to work on problems individually or in groups, with teaching staff / assistants present to give help. Teaching staff also hold regular "office hours" during which you are welcome to come and ask questions about the material and obtain individual (one-to-one) assistance and feedback.

Practical and transferable skills are developed by the provision of opportunities for hands-on experience through regular workshops and projects. Much of the tuition for statistical computing takes place in computer workshops, which will allow you to learn through active participation. Additional workshops running during the teaching terms provide preparation for the summer research project and cover the communication of statistics, for example, the presentation of statistical graphs and tables. Project supervisors will provide guidance on how to manage an extended task effectively and you are encouraged to monitor your own working practice using a self-assessment questionnaire, as well as to monitor your own progress by self-marking of non-assessed coursework.

All summative assessment is organised at modular level during the academic year in which the module is taken. Most Statistical Science modules employ a combination of end-of-year written examination and coursework to assess your subject-specific knowledge and academic skills, although some modules are entirely coursework based. Statistical project work further assesses your intellectual, academic and research skills by means of word-processed written reports and, in the case of the summer research project, an oral presentation.

Coursework is designed to encourage you to develop your knowledge and skills as each module proceeds. Although not all coursework contributes towards formal assessment, it will provide you with the opportunity to demonstrate your intellectual and practical skills in written responses to problem sheets and in oral responses during tutorials, with feedback mainly presented through tutorials / problem classes / workshops, and on an individual basis on request.

On average it is expected that a student spends 150 hours studying for each 15-credit module. This includes teaching time, private study and coursework. Modules are usually taught in weekly two-hour sessions over 10 weeks each term.

For full-time students, typical contact hours are around 12 hours per week. Outside of lectures, seminars, workshops and tutorials, full-time students typically study the equivalent of a full-time job, using their remaining time for self-directed study and completing coursework assignments.

In terms one and two full-time students can typically expect between 10 and 12 contact hours per teaching week through a mixture of lectures, seminars, workshops, crits and tutorials. In term three and the summer period students will be completing their own research project, keeping regular contact with their supervisors.

Modules

Full-time

The core material is delivered through a foundation module (to revise basic concepts in probability and statistics) and further compulsory modules. Programming techniques are introduced within the core modules in order to allow students to code their own statistical methods. Students may then place particular emphasis on their application areas of interest by suitable choice of optional modules.

The research project is a consolidation of the MSc’s taught component. Students will normally analyse and interpret data from a real, complex problem, offering the chance to produce viable solutions. Project topics can be selected from a departmental list, or students can make their own suggestions. The list usually includes some collaborative projects available with industrial partners.

Part-time

The programme is also offered on a part-time basis over two years. The taught modules are split between the first and second years, but within each year the classes for a particular module are the same ones attended by full-time students (i.e. special teaching times are not offered for the part-time programme).

The foundation module is taken at the beginning of the first year. It is recommended that students also take the module Statistical Models and Data Analysis (STAT0028) in the first year, and module prerequisites need to be fulfilled, but otherwise there is some flexibility in the order that the remaining taught modules can be studied. Part-time students submit their project at the end of the second year. It is possible to arrange with the project supervisor to start to work on the project earlier than full-time students, but part time students are not entitled to a higher amount of supervision overall.

Compulsory modules

• Statistical Computing 
• Statistical Models and Data Analysis 
• Statistical Design of Investigations 
• Research Project 
• Foundation Fortnight 
• Applied Bayesian Methods

Optional modules

• Statistical Inference 
• Stochastic Systems 
• Forecasting 
• Decision and Risk 
• Stochastic Methods in Finance 
• Medical Statistics 1 
• Medical Statistics 2 
• Stochastic Methods in Finance II 
• Bayesian Methods in Health Economics 
• Quantitative Modelling of Operational Risk and Insurance Analytics 
• Statistical Machine Learning 
• Computational Statistics

Please note that the list of modules given here is indicative. This information is published a long time in advance of enrolment and module content and availability are subject to change. Modules that are in use for the current academic year are linked for further information. Where no link is present, further information is not yet available.

Students undertake modules to the value of 180 credits. Upon successful completion of 180 credits, you will be awarded an MSc in Statistics.

Accessibility
Details of the accessibility of UCL buildings can be obtained from AccessAble. Further information can also be obtained from the UCL Student Support and Wellbeing Services team.

What this course will give you

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.

UCL's newly founded Institute for Mathematical and Statistical Sciences aims to be London's leading centre for research, teaching and collaboration in mathematics and statistics, establishing UCL as a global leader and outward-looking centre for the mathematical sciences and its applications.

Ranked 5th in the UK by the QS World University Rankings by Subject 2024 for Statistics and Operational Research, we offer you an excellent education with high standards of teaching.

The foundation of your career

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

Areas of employment include Accountancy and Financial Services, Banking and Investment, and Consultancy with graduates securing positions with a range of employers including Vanguard and WillisTowersWatson.

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.

Networking

The Department offers world-class expertise along with strong links to practitioners, and its position within UCL provides breadth of knowledge (for example the UCL institute for Mathematical and Statistical Sciences, the UCL Centre for Computational Statistics and Machine Learning and the Alan Turing Institute). Staff members also collaborate directly with hospitals, power companies, government regulators, and the financial sector. Consequently, postgraduate students have opportunities to engage with external institutions.

There is a possibility of external organisations delivering technical lectures and seminars while the MSc research project list usually includes collaborative projects available with pharmaceutical companies and other industrial partners.

Accreditation
This MSc programme is accredited by the Royal Statistical Society.

The current period of accreditation covers students who first enrol between September 2023 and September 2028.