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This course introduces students to the field of spatial data science and is designed to develop students' understanding of some fundamental algorithms and code libraries that are used to manipulate, analyse, and map spatial data, and to explore how they are implemented in software. Students will use Python and Javascript programming languages. The course is largely lab and project based, with context and theoretical frameworks presented in lectures and tutorials in order to guide hands-on development.
This course focuses on expanding your geospatial data science skills, deepening your understanding of how analysis works and developing background knowledge of geospatial research.
Understand best practices to access, process, and visualise spatial data in Python Jupyter Notebooks Identify geospatial methods, packages, and tools to accomplish workflow tasks Gain programming literacy to understand and modify code in programming environments to meet your geospatial analysis needs
This course will provide students with an opportunity to develop the Graduate Attributes specified below:
Critically competent in a core academic discipline of their award
Students know and can critically evaluate and, where applicable, apply this knowledge to topics/issues within their majoring subject.
Employable, innovative and enterprising
Students will develop key skills and attributes sought by employers that can be used in a range of applications.
Globally aware
Students will comprehend the influence of global conditions on their discipline and will be competent in engaging with global and multi-cultural contexts.
GISC401 or COSC121 or COSC480 or equivalent
Students must attend one activity from each section.
There is a weekly lecture, seminar style discussion, and lab.
Carolynne Hultquist
Vanessa Bastos
Recommended textbook(s):J. VanderPlas, Python Data Science Handbook: Essential Tools for Working with Data. (Second ed.) 2023. Python for Geospatial Data Analysis: Theory, Tools, and Practice for Location Intelligence Bonny Clain
Prerequisites: GISC401 or COSC121 or COSC480 or equivalentRecommended preparation: Experience in Python is highly recommended. GISC401 is the preferred preparation course, as it provides a foundation for working with spatial data in Python. Otherwise, preparation could involve COSC121 or COSC131 or COSC480, or an equivalent introductory programming course.
Domestic fee $1,145.00
International Postgraduate fees
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see School of Earth and Environment .