Turbocharging Analytics at Uber with Data Science Workbench

Millions of Uber trips take place each day across nearly 80 countries, generating information on traffic, preferred routes, estimated times of arrival/delivery, drop-off locations, and more that enables us to facilitate better experiences for users.

To make our data exploration and analysis more streamlined and efficient, we built Uber’s data science workbench (DSW), an all-in-one toolbox for interactive analytics and machine learning that leverages aggregate data. DSW centralizes everything a data scientist needs to perform data exploration, data preparation, ad-hoc analyses, model exploration, workflow scheduling, dashboarding, and collaboration in a single-pane, web-based graphical user interface (GUI).

Leveraged by data science, engineering, and operations teams across the company, DSW has quickly scaled to become Uber’s go-to data analytics solution. Current DSW use cases include pricing, safety, fraud detection, and navigation, among other foundational elements of the trip experience. In this article, we discuss two main themes: 1) the challenges we had building and, in particular, encouraging mass adoption of DSW, and 2) how the workbench made data science at Uber more streamlined and scalable than ever before.

The DSW stack

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