. "Applied Mathematics"@en . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "Analytical and numerical methods"@en . . "5" . "The course aims to:\n\nProvide a basic introduction to calculus and basic statistical methods\nProvide an introduction to mathematical and computational methods for modelling applications\nIntroduce general conceptual frameworks for the problems and issues of developing forward and inverse models\nProvide practical analytical and numerical examples for both forward and inverse modelling, particularly linear v non-linear, approaches to solving and generic aspects of implementation\nProvide example applications of the techniques covered, including use of Jupyter Python notebooks\nCover generic issues arising in application of analytical and numerical approaches including the discretisation, detail vs computation time, stochastic processes etc.\nTo provide exposure to numerical tools that are used in a wide range of modelling applications, including an introduction to Bayes Theorem and Monte Carlo methods among others.\nThe module will provide an introduction to a range of fundamental concepts and principles for handling and manipulating data. The first half of the module (taught with CEGE) provides a basic introduction to stats and linear algebra, and important basic concepts; the second half covers slightly more advanced applications of methods and tools for data analysis. The module will cover:\n\nElementary differential and integral calculus and its applications (equations of motion, areas and volumes etc),\nLinear algebra and matrix methods, including computational issues (decomposition for eg) and generalised linear models\nDifferential equations and applications\nOverview of statistical methods including an intro to Bayes Theorem\nNumerical methods, model fitting, numerical optimization Monte Carlo and Metropolis-Hastings\nThe main sessions include:\n\nIntroduction to calculus methods\nIntroduction to linear algebra, matrices\nStatistics and further statistics\nLeast Squares and further least squares\nDifferential equations\nIntroduction to Bayes Theorem\nModel selection\nLinear & non-linear model inversion\nMonte Carlo methods and related numerical tools," . . "Presential"@en . "TRUE" . . "Master in Remote Sensing and Environmental Mapping"@en . . "https://www.ucl.ac.uk/prospective-students/graduate/taught-degrees/remote-sensing-and-environmental-mapping-msc#course-overview" . "60"^^ . "Presential"@en . "Students develop an all-round knowledge of remote sensing, mapping and data analysis, including fundamental principles, current technological developments and applications to local, regional and global problems. They gain highly developed, marketable practical skills, particularly coding and data analysis, written and other communication skills to enable them to take leading roles in academic, government and industrial sectors"@en . . . . "1"@en . "FALSE" . . "Master"@en . "Thesis" . "14100.00" . "British Pound"@en . "14100.00" . "None" . "Graduates are highly-employable across a wide range of sectors. Recent graduates have been employed in: international space agencies, commercial geospatial and environmental companies; new start-ups using UAVs and satellite data; government agencies; charities and NGOs. The programme is also suitable training for those wishing to undertake a PhD in a quantitative environmental discipline and a number of our graduates have gone on to become leading researchers in the UK and overseas."@en . "1"^^ . "TRUE" . "Downstream"@en . . . . . . . . . . . . . . . .