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Modern data collection technologies are creating unprecedented challenges in statistics and data science related to the analysis and interpretation of massive data sets where observations exhibit patterns through time and space. This subject introduces probability models and advanced statistical inference methods for the analysis of temporal and spatio-temporal data. The subject balances rigorous theoretical development of the methods and their properties with real-data applications. Topics include inference methods for univariate and multivariate time series models, spatial models, lattice models, and inference methods for spatio-temporal processes. The subject will also address aspects related to computational and statistical trade-offs, and the use of statistical software.
Intended learning outcomes
LO1 A deep understanding of selected advanced topics in spatio-temporal statistics.
LO2 Development of the mathematical and computational skills needed for further research or applied work in statistics and data science.
LO3 Preparation for a research or industry career in statistics and data science.
LO4 Familiarity with several major texts spatio-temporal statistics.
- In addition to learning specific skills that will assist students in their future careers in science, they will have the opportunity to develop generic skills that will assist them in any future career path. These include: - problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies; - analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis; - collaborative skills: the ability to work in a team; - time-management skills: the ability to meet regular deadlines while balancing competing commitments
Last updated: 2 December 2019