Course summary

The MSc Geographic Data Science programme is aimed at those who wish to develop their skills in the analysis of quantitative data to answer research questions on spatial and geographic social science topics. The programme will suit you if you are interested in research for academic or policy purposes, have a background in economics or other quantitative social science and are interested in developing your technical skills in analysis of spatial data. The programme combines courses in the Department of Geography and Environment, with options in data science and programming from the Department of Statistics and Department of Methodology. Skills in obtaining, managing, visualising and analysing data (data science) are in high demand and the programme combines a training in the application of these methods and the use of geographical information systems (GIS), with the chance to study topics in urban economics, economic geography, environmental economics or planning. The culmination of the programme is a dissertation in which you investigate your own research topic using the data science and GIS methods you have studied on the course. Data science topics covered include programming (in R, Python and Stata), machine learning, visualisation, spatial quantitative analysis and econometrics (depending on your interests and previous experience). There is an emphasis on the use of these methods to understand causes, inform policy and address critical, policy-relevant questions in human geography, economic geography, planning, environment and development. This programme will: • Train you in the use of data science methods appropriate for working with geographic data, with an emphasis on answering causal research questions; • Provide you with the opportunity to study Masters level courses in economic geography, environmental economics or planning; • Provide you with an opportunity to apply data science skills through an extended dissertation on a geographical topic; • Develop a set of transferable skills in independent thought, writing, data analysis, independent research, presentation and debate that are pre-requisites for future careers.