Applied Data Science for UX

Applied Data Science for UX#

Welcome!

This site offers a hands-on guide to applying data science techniques in UX research. It’s designed to bridge the gap between qualitative insights and quantitative rigor with beginner-friendly explanations, code, fun examples, and practical workflows.

Who Is This for?#

This site is for:

  • UX researchers looking to add some basic data science skills.

  • UX designers curious about analytics.

  • Anyone else interested in exploring the intersection of user experience and data.

What You Will Learn#

  • How to set up your digital workspace

  • Methods for preparation, exploration, and visualization of data

  • Techniques for common UX research goals:

    • Uncovering relationships

    • Classifying observations

    • Extracting deeper meaning from open-ended feedback

    • Finding connections across users or concepts

Why Have I Created This Site?#

So, to get a bit personal…

As of the time I’m creating this site (April 2025), UX is undergoing a lot of shifting and uncertainty largely due to companies pushing AI regardless of whether they have a viable use case for it. In my industry there’s a greater need for UX research now, and for that research to include legitimate quantitative methods. However, researchers need to ensure those methods go far enough. Product leaders want deeper insights into attitudes and behaviors, and how those manifest in the digital spaces companies create. UX can’t provide that with parametric tests, gap analysis or the occasional semantic analysis alone. As practitioners we need to become comfortable with the broader suite of analytics methods that can if we want to provide such insights.

This site is not about democratizing product analytics or diminishing the skills of product analysts or product data scientists. Rather, it’s my way of encouraging UX research practitioners to consider acquiring additional training and moving into those roles so we can bring our perspective to the interpretation of user and product data.

Also, I just really love analytics and data science! Especially network science.

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