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This program will equip you with advanced knowledge of quantitative and computational approaches in geography. You'll gain proficiency in GIS applications and statistical programming languages like R or Python. Program OverviewThrough practical applications, you'll master techniques for visualizing, modeling, and statistically analyzing diverse data sources - from traditional census records and surveys to modern satellite imagery and social media data, utilizing both web-based and conventional methodologies.Data Science and Analytics in ContextModern society generates enormous quantities of digital information. Retailers compile transactional data for marketing strategies, governments collect administrative data for public services, social media creates digital footprints, and smart transit systems document our mobility patterns.Global challenges frequently have spatial dimensions: addressing climate change impacts, securing food and water resources, developing sustainable energy networks for future populations, or creating equitable urban environments. The expanding realm of big data about human systems offers unprecedented opportunities to address these issues through Geographic Data Science approaches.Target AudienceThis program is designed for individuals seeking to examine and interpret geographical influences on daily life using spatial analysis and computational methods.
If you hold a bachelor’s degree or equivalent, but don’t meet our entry requirements, you could be eligible for a Pre-Master’s course. This is offered on campus at the University of Liverpool International College, in partnership with Kaplan International Pathways. It’s a specialist preparation course for postgraduate study, and when you pass the Pre-Master’s at the required level with good attendance, you’re guaranteed entry to a University of Liverpool master’s degree.
IELTS: 6.5 overall, with no component below 5.5
TOEFL iBT: 88 overall, with minimum scores of listening 17, writing 17, reading 17, and speaking 19. TOEFL Home Edition not accepted.