Visualizing data is crucial because humans interpret images better than numbers, as demonstrated by Anscombe's Quartet where identical statistics hide vastly different datasets.
Data mapping is the core process of converting data attributes into visual representations like position, size, shape, and color to create effective charts.
An overview of the visualization landscape covers fully-featured applications like Tableau and Power BI, as well as programming libraries for Python, R, and JavaScript.
Web-based visualizations are primarily built using SVG for its vector graphics and native interactivity, while Canvas offers more complex drawing capabilities with WebGL.
D3.js is a powerful and flexible open-source JavaScript library that binds data to DOM elements, enabling a wide range of custom and interactive visualizations.
Data visualization serves different use cases like exploration and presentation, and a comparative study shows how tools like D3.js excel at flexible presentation.
Web-based visualizations must address performance issues with large datasets through aggregation and sampling, and handle the security implications of client-side data transfer.
To succeed with web-based visualization, it is crucial to know your users and use case, carefully select the data to be shown, and leverage open-source libraries.
50:38Radovan Kavický
14:44Chris Heilmann
43:20Michael Hladky & Stefan Baumgartner
34:34Daniel Madalitso Phiri
51:10Laurie Voss
40:24Clemens Helm
40:19Daisy Muyldermans
32:56Laurie Voss
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Jobs that call for the skills explored in this talk.
Slalom GmbH
Düsseldorf, Germany
Slalom GmbH
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Maximum Personalmanagement GmbH
tarent solutions GmbH
tarent solutions GmbH
tarent solutions GmbH