Webinar: User Experience 101 for Data Science and Research Software Engineers

What Research Software Engineers Need to Know About User Experience (UX)

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Image of UX Design in large font surrounded by blue bubbles with related keywords Usability,Interface, Design, Navigation, User Research, etc.

When

10 to 11 a.m., April 19, 2024

Webinar Videorecording

Presenter's Slides

 

About the webinar

Even if you can write scripts or know your way around the command-line, the principles and practices of good User Experience (UX) is invaluable and useful information for any Research Software Engineer (RSE) to know. In this webinar, Mariah Wall, CyVerse's User Interface and Application Developer, shares important principles and practices that anyone developing Apps or interfaces should know, especially in the field of Data Science. In this webinar, Mariah will explain what good UX is, how good UX is more than a pretty face, and why good UX can do more for a user's success and satisfaction than almost any amount of documentation or tutorials. Join us for this webinar from our Research Software Professional Mariah Wall to help you discover both the art and the science in making your app interfaces work better and your users happier!

What you'll learn

  • What is UX and why UX is important
  • The principles and elements of good UX
  • Common UX traps and how to avoid them
  • Best practices on how to apply UX to existing or new projects
  • How the UX process applies in Agile software development
  • Helpful ideas and tools for evaluating and improving your app interfaces

About the presenter

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headshot of Mariah Wall

Mariah Wall is CyVerse's User Interface and Application Developer. She received her Masters of Science in User Experience last year from Arizona State University. Mariah specializes in UX and UI design and has applied her expertise and talent to designing the interface for CyVerse's CACAO and recently for Chatur, a LLM collaborative project with DSI, ICDI and Computer Science.

She has also done UX/UI work for HydroGEN, a machine learning collaborative project with the UArizona, based at Princeton University.

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