Course Overview

Our Postgraduate Certificate in Geographic Data Science (Intensive) will provide you with advanced scientific and technical training in capturing, analyzing, and visualizing geospatial information. Thanks to increases in computing power and the availability of GPS, we can record and store an unprecedented amount of information about the world. Geographic information plays an increasingly crucial role in our society, transforming sectors as diverse as transportation, insurance, banking, telecommunications, logistics, energy, retail, agriculture, healthcare, urban planning, and scientific research. We will introduce you to the principles and applications of geographic data science at an advanced level through the development of scientific knowledge, technical expertise, and practical experience. The emphasis throughout this course is on enabling you to develop new skills that have real-world applications for your work and career. You will develop the relevant theoretical background knowledge and a conceptual understanding of geographic data science whilst also acquiring key data science skills that are in high demand in the industry. The modules will cover important tools for research in the field: geographic information systems, such as ArcGIS and QGIS; map-making and cartography; big data management with spatial databases, such as SQL and NoSQL. This program will focus on methodological understanding and analytics.

General Eligibility

At least a second-class honors degree (2:2) or equivalent in geography, computer science, or a cognate discipline; we will consider the professional experience in place of a degree. Some experience in Geographic Information Systems is desirable but is not a requirement.

Part Time Work Details

Language Requirement

• If English is not your first language or you have not previously studied in English, the requirement for this program is the equivalent of an International English Language Testing System (IELTS Academic Test) score of 6.5, with not less than 6.0 in each of the sub-tests.