Interactive visualization for data science
One of the challenges in traditional data visualization is that they are static and have bounds on limited physical/pixel space. Interactive visualizations allows us to move beyond this limitation by adding layers of interactions. Bargava Subramanian and Amit Kapoor teach the art and science of creating interactive data visualizations.
|Interactive visualization for data science
|Bargava Subramanian (Binaize), Amit Kapoor (narrativeVIZ)
|Strata + Hadoop World
|Make Data Work
|December 6-8, 2016
“A picture is worth a thousand words. An interface is worth a thousand pictures.”—Ben Shneiderman Ever-increasing computational capacity has enabled us to acquire, process, and analyze larger and larger datasets and information. However, the human memory and attention required to use this data is limited and has remained relatively constant. Data visualization can enable us to compress data and encode it in ways that aid perceptual, cognitive, and emotional capacity required to comprehend, retain, and make decisions using this data. One of the challenges in traditional data visualization is that they are static and have bounds on limited physical/pixel space. Interactive visualizations allows us to move beyond this limitation by adding layers of interactions. We’ve all seen wonderful interactive data visualizations on the web, such as those from the New York Times’s Upshot or FiveThirtyEight, and may want to bring similar interaction principles to our business dashboards. But crafting an interactive data visualization on the web is hard, especially if you have limited web programming background. More often than not, data scientists want to demonstrate or showcase their work as a dashboard and are required to get approval from stakeholders before the dashboard is moved to production by frontend engineers. Bargava Subramanian and Amit Kapoor teach the art and science of creating interactive data visualizations, providing hands-on experience with using simple tools in the browser, including visdown and polestar, to conduct exploratory data analysis for large datasets and visually communicate insights from data. Outline Grammar of interactive graphics: The four layers of abstraction Tools landscape Creating a static visualization Adding an interaction layer Creating an interactive data visualization Additional pointers, wrap-up, and Q&A