#dataviz

If you’re building a dashboard, analytics platform, or any web app where you need to give your users insight into their data, you need beautiful, custom, interactive data visualizations in your React app. But building visualizations hand with a low-level library like D3 can be a huge headache, involving lots of wheel-reinventing. In this talk, we’ll see how data viz development can get so much easier thanks to tools like Plot, a high-level dataviz library for quick
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easy charting, and Observable, a reactive dataviz prototyping environment, both from the creator of D3. Through live coding examples we’ll explore how React refs let us delegate DOM manipulation for our data visualizations, and how Observable’s embedding functionality lets us easily repurpose community-built visualizations for our own data
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use cases. By the end of this talk we’ll know how to get a beautiful, customized, interactive data visualization into our apps with a fraction of the time
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effort!

At Shopify, the Insights team creates visualization experiences that delight and inform. We've done a lot of great work prioritizing accessibility and motion design for web. Our mobile experiences though, were a bit of an afterthought, but not anymore! In this talk, we'll go through how we created our data viz components library; How we encapsulated core logic, animation, types and even UI components for web and mobile; and also why keeping things separate sometimes is better - to create awesome UX.

The expansion of data size and complexity, broader adoption of ML, as well as the high expectations put on modern web apps all demand increasing compute power. Learn how the RAPIDS data science libraries can be used beyond notebooks, with GPU accelerated Node.js web services. From ETL to server side rendered streaming visualizations, the experimental Node RAPIDS project is developing a broad set of modules able to run across local desktops and multi-GPU cloud instances.

Data visualisation is a fundamental part of Data Science. The talk will start with a practical demonstration (using pandas, scikit-learn, and matplotlib) of how relying on summary statistics and predictions alone can leave you blind to the true nature of your datasets. I will make the point that visualisations are crucial in every step of the Data Science process and therefore that Jupyter Notebooks definitely do belong in Data Science. We will then look at how maintainability is a real challenge for Jupyter Notebooks, especially when trying to keep them under version control with git. Although there exists a plethora of code quality tools for Python scripts (flake8, black, mypy, etc.), most of them don't work on Jupyter Notebooks. To this end I will present nbQA, which allows any standard Python code quality tool to be run on a Jupyter Notebook. Finally, I will demonstrate how to use it within a workflow which lets practitioners keep the interactivity of their Jupyter Notebooks without having to sacrifice their maintainability.