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.
The programme combines training in core statistical and machine learning methodology, beginning at an introductory level, with a range of optional courses covering more specialized knowledge in statistical computing and modelling.
Please click the headings below for further information:
- Programme Structure
Students undertake modules to the value of 180 credits. The programme consists of a foundation module (non-credit bearing), eight taught modules (120 credits) and a research dissertation (60 credits).
Further information about specific modules, including outline syllabuses and reading lists, is available via the Department's handbook for taught graduate students.
- Modes of Study
Full time, the degree will last 12 months (including a summer project).
Part time study is also available over two years. Studying the MSc Data Science on a part time basis means taking the same compulsory and optional modules over two years, but they are as timetabled for full time students (special teaching times are not offered for part time students). It is possible to arrange with the project supervisor to start to work on the research project earlier than after the exams in the second year, but there is no entitlement to a higher overall amount of supervision than full time students.
- Teaching and Assessment
Teaching is by means of a mixture of live sessions and self-study using videos and other materials prepared by the module leader. Some of the live sessions are dedicated to practical work. There is also the possibility of external organisations delivering technical lectures and seminars. The taught degree programme involves about 200 hours of live sessions altogether.
Students will be assessed on eight taught modules in total. For most modules the overall mark is based on an in-course assessment (given 20% weighting) and a final written examination sat in term three (given 80% weighting). However, there are no written examinations for the Statistical Design of Investigations and Statistical Computing modules, which are assessed entirely through coursework.
- Research Project
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. Workshops, which provide preparation for this project, will run during the teaching terms. These will cover the communications of statistics such as the presentation of statistical graphs and tables.
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 pharmaceutical companies or other industrial partners.
- Professional Accreditation
The Royal Statistical Society (RSS) accredits taught degree programmes on the basis of information supplied by the awarding institution. RSS accreditation provides reassurance that a programme produces graduates with the technical skills and subject knowledge required of a statistician.
The MSc Data Science programme has been accredited by the RSS. The current period of accreditation covers students who first enrol between September 2017 and September 2021. All students on an accredited programme will be eligible for e-Student membership of the RSS, with the potential to progress along the professional pathway of RSS membership to Graduate Statistician and Chartered Statistician status.
The RSS has attached the following condition to the accreditation: either the research project must be in statistics, or at least 45 credits of optional statistics modules must be taken. Graduates who wish to apply for Graduate Statistician status with the Society must submit a transcript to show that a satisfactory combination of modules has been taken.
- Contact Details
For more information on the programme please contact:
Ms Agnes Somogyi
stats.pgt-admissions AT ucl.ac.uk
+44 (0)20 7679 5694
Please note that all professional services staff are currently working away from the office and are therefore unable to take phone calls on the number above.