Course summary

Interested in playing a critical role at the interface of physical, computer and environmental science? Analysing satellite data is key to monitoring air quality, land use and deriving climate change indicators and has many commercial uses. This course teaches the principles and applications of remote sensing and data science techniques. As a trained data scientist many careers are open to you. This interdisciplinary MSc in Satellite Data Science trains you in the development and use of satellite observations of the earth and its atmosphere for understanding the natural environment and how it is being impacted by human behaviour. The course is accessible to students without a strong computing and mathematical background. You will be taught the basic operational principles of different remote sensing/earth observation platforms and sensors, learn how to use a variety of data- and image-processing techniques and software, and gain practical experience of using satellite data and data products in a number of applied terrestrial and atmospheric contexts. The uptake, use, and commercialisation of satellite data has created a demand for expertise in Data Science and you will be trained in a broad range of data science techniques. The course is structured so that transferable data science skills taught and developed within a strong disciplinary and vocational context. These skills are not limited to a career focused on satellite observations. You will gain experience in coding that will help you to process large amounts of data that are often needed for a number of data science methods (such as machine learning). They address the shortage of qualified data scientists in the UK and abroad and will place you in a strong position for a career in a range of data science sectors. Students taking the ‘with industry’ version of the degree can seek placements with a range of commercial organisations associated with Earth Observation and data science sectors of the economy. Students seeking placements are supported by two Placement modules, one taken in each semester.