SkillsCast

Building open visualisations of UK Government Expenditure data - Beginners

6th July 2017 in London at CodeNode

There are 42 other SkillsCasts available from Infiniteconf 2017 - the conference on Big Data and Fast Data

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What if you could look at the source code behind any online visualization, understand how it works, run it to check the results using the latest data and modify the parameters to explore different aspects that you are interested in?

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Building open visualisations of UK Government Expenditure data - Beginners

May Yong

May Yong is a Research Engineer at the Alan Turing Institute. She works across multiple research domains, collaborating with scientists to turn interesting ideas into useable software. Past projects include machine learning on neonatal intensive care data, creation of data standards for antibody therapy experiments and amalgamation of heterogeneous multiple sclerosis data. May is interested in data interpretation, specifically in ways of providing information in perspective in order to see the bigger picture. She is building tools to minimize data ambiguity so that data is interpreted and used the way the data collectors intended.

SkillsCast

Please log in to watch this conference skillscast.

Https s3.amazonaws.com prod.tracker2 resource 41088130 skillsmatter conference skillscast o9nohu

What if you could look at the source code behind any online visualization, understand how it works, run it to check the results using the latest data and modify the parameters to explore different aspects that you are interested in?

YOU MAY ALSO LIKE:

Thanks to our sponsors

About the Speaker

Building open visualisations of UK Government Expenditure data - Beginners

May Yong

May Yong is a Research Engineer at the Alan Turing Institute. She works across multiple research domains, collaborating with scientists to turn interesting ideas into useable software. Past projects include machine learning on neonatal intensive care data, creation of data standards for antibody therapy experiments and amalgamation of heterogeneous multiple sclerosis data. May is interested in data interpretation, specifically in ways of providing information in perspective in order to see the bigger picture. She is building tools to minimize data ambiguity so that data is interpreted and used the way the data collectors intended.