Designing a User-Centric Data Science Product
Designing a User-Centric Data Science Product


This presentation will give a crash course in user-centric design, present arguments for why data scientists should care about design, and provide data scientists who build internal tools with design best practices. User-centric design, at its core, is a framework for understanding the user and his or her problems, and using those as a focal point for product design. Leading product companies spend huge resources to design their products well, but not all organizations with data scientists have designers to help them. At the same time, though, data scientists are increasingly building internal products for their organizations that embed data science into decision-making processes, so data scientists who want to see their tools get used should learn the best practices from user-centric design. This talk, co-presented by a data scientist and a designer, will start with design basics like building an understanding of the user, and then move on to more specific examples of user-centric data science products we’ve built.

Who is this presentation for: This talk is for data scientists, especially those building tools that others in their organizations will use to solve business problems with data science. The ideal audience member wants to understand how to better build tools that actually get used, in particular by making tools that are well-designed to solve an important problem.
What will they be able to take away: After the talk, the audience member will walk away with an understanding of what user-centric design is and why it matters, a few real-world examples of good design in data science, and pointers for incorporating good design practices into his or her own data science deliverables.
What prerequisite knowledge do they need: There is no prerequisite knowledge required.


Katie Malone is Director of Data Science at Tempus, a technology company that is making precision medicine a reality by gathering and analyzing clinical and molecular data at scale. Prior to working at Tempus she spent four years in the data science research and development team at Civis Analytics, a data science consulting and software company. She completed a PhD in physics at Stanford, working at CERN on Higgs boson searches. She was also the instructor of Udacity’s Introduction to Machine Learning course, and hosts Linear Digressions, a weekly podcast on data science and machine learning.

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