University College London (UCL)

Data Science 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. Data analysis demonstrations and exercises are an essential component of the core modules and 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 the modular level during the academic year in which the module is taken. Most Statistical Science and Computer 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. Data analysis 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 methodology is delivered through a foundation module (to revise basic concepts in probability and statistics) and further compulsory modules, and illustrated with a variety of applications. 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 areas of application 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 compulsory module Introduction to Statistical Data Science (STAT0032) 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
• Introduction to Machine Learning
• Foundation Fortnight
• Statistical Design of Investigations
• Statistical Computing
• Introduction to Statistical Data Science
• Research Project
• Optional modules
• Stochastic Systems
• Forecasting
• Decision and Risk
• Stochastic Methods in Finance
• Stochastic Methods in Finance II
• Quantitative Modelling of Operational Risk and Insurance Analytics
• Applied Bayesian Methods
• Inference at Scale
• Graphical Models
• Applied Machine Learning
• Information Retrieval and Data Mining
• Statistical Natural Language Processing
• Applied Deep Learning

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.

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

What this course will give you

• 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 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.
• 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

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 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.

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

Areas of employment include IT, Technology and Telecoms, and Accountancy and Financial Services with graduates securing positions with a range of employers including Deloitte and Huawei.

Networking
The Department offers world-class expertise along with strong links to practitioners, and its position within UCL provides students with a 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 the possibility of external organisations delivering technical lectures and seminars while the MS research project list usually includes some collaborative projects 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.