Rapid solution prototyping with open data and Jupyter notebook

Ms Kerri Wait1

1Monash University, Clayton, Australia, kerri.wait@monash.edu 


Open data initiatives have the potential to accelerate research activities, but with the sheer number of data formats, tools, and platforms available, it can be difficult to know where to begin and which approach to undertake. In this talk I’ll consider a hyperthetical[1] research project to acquire data on the quality of lamingtons in each Victorian local government area. I’ll show how python scripting inside a Jupyter notebook can retrieve and combine open data such as council boundaries and office locations to produce an optimised research path (i.e. where to drive to minimise distance and maximise lamington research benefits), and how much faster this approach is than manually wrangling data in spreadsheets and text files.

[1] Exaggeratedexcessivehyperbolical.


Kerri Wait is an HPC Consultant at Monash University. As an engineer, Kerri has a keen interest in pulling things apart and reassembling them in novel ways. She applies the same principles to her work in eResearch, and is passionate about making scientific research faster, more robust, and repeatable by upskilling user communities and removing entry barriers. Kerri currently works with the neuroscience and bioinformatics communities.

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