MSc Data Analytics for Government
Aims of the Programme
- To impart deep understanding of, and practical experience in using the current and emerging range of data science tools, techniques, technologies and methodologies that are impacting many sectors of public life, from retail to healthcare, and will come to shape the future of national statistics itself.
- To supply a robust theoretical framework, covering the essential mathematics, statistics and computational science required to understand the populations from which data are drawn, and to develop and implement effective and efficient algorithms.
- To instil the importance of good survey design, to ensure that data collected are appropriate for a particular issue of interest.
- To develop an understanding of how to manage data that are unstructured, varying in format, collected over time and/or space, and to mine data of interest from wider collections.
- To give students an understanding of the computational infrastructure (hardware and software) that supports data storage, processing and analysis.
- To hone students’ ability to collect, process, analyse and produce interpretable outputs from data, drawing upon fundamental principles of data storytelling and selecting appropriate tools for visualisation.
- To enable students to better understand and appreciate the connection between national statistics and government policy, and the new opportunities and the new opportunities data science presents at this interface.
Students pursue modules to the value of 180 credits. The programme consists of a foundation course (non-credit bearing), eight taught modules; (four compulsory, up to four selected options – totalling 120 credits), and a research dissertation (60 credits) Module titles are given below. As modules on this course have been taken from a range of existing modules which are almost all in operation across UCL already, the UCL module names are given first, with the corresponding ONS module name given in brackets.
- Core Modules
Course Title Credits Term (Statistics in Government) Analytic Methods for Policy 15 3 (Data Science Foundations) Introduction to Statistical Data Science 15 1 (Survey Fundamentals) Statistical Design of Investigations 15 1 (Statistical Programming) Programming for Business Analytics 15 1 Data Analytics for Government Dissertation 60
- Optional Modules (students choose 60 credits from the below)
Course Title Credits Term (Introduction to Survey Research) Survey Design 15 (Regression Modelling) Statistical Models and Data Analysis 15 1 Digital Visualisation 30 2 (Survey Data Collection) Introduction to Longitudinal Data and Analysis 15 (Further Survey Estimation Methods) Statistical Inference 15 1 (Advanced Statistical Modelling) Selected Topics in Statistics 15 2 (Time Series Analysis) Forecasting 15 2 (Spatial Analysis) GIS Mapping and Spatial Stats 15 1 (Bayesian probabilistic methods) Applied Bayesian Methods 15 2 Foundations of Machine Learning and Data Science 15 2 Statistical Computing 15 1 & 2
*due to capacity limitations, some optional courses may not run every year. Students are advised to choose their options as soon as possible after enrollment.
Modes of Study
It is anticipated that students will generally pursue the course flexibly over 3-5 years, and will structure their time during their chosen period of study so as to complete all the necessary modules and research dissertation. Guidance on appropriate durations of study can be sought from the course directors, in conjunction with ONS supervisors.
Delivery Format, Teaching and Assessment
The majority of taught content will be delivered face-to-face in Bloomsbury, London. Some courses will have web-based components (e.g. Moodle pages, links to online resources), but most teaching will be in London. Some teaching is dedicated to practical (computer lab-based) work. Most modules will be assessed by unseen written exams, but certain modules will be assessed significantly or entirely by coursework and/or oral presentations.
The programme does not accept new students at the moment.