I teach people to work with data. I believe that everyone – everyone – can use data to help them do the things that are important to them and to do those things better. And so I teach people the principles and the practices that will help them get useful insights and actionable direction from their data. I teach online via my own company, datalab.cc, through LinkedIn Learning, and on YouTube. I teach in person as a conference speaker, as a corporate trainer, and as a professor at Utah Valley University. And I teach through the books that I have written, the statistics textbook Data Sense and the free introduction to R, R Succinctly.
You need familiarity with concepts and tools to work well with data. Basic tools include programming languages, specialized statistical and visualization applications, and everyday tools like Microsoft Office and Google Drive. I have taught people to use these tools through online channels, in-person settings, and in print media. The goal is never to master all of the tools or every aspect of a tool but, rather, to be sufficiently familiar with an adequate range of tools that you can easily and efficiently accomplish the tasks before you.
Data Science Foundations: Fundamentals
Data Science Foundations: Data Mining
Big Data Foundations: Techniques and Concepts
Data Fluency: Exploring and Describing Data
AI Accountability Essential Training
R Statistics Essential Training
Introduction to jamovi
SPSS Statistics Essential Training
Julia for Data Scientists First Look
Processing: Interactive Data Visualization
Creating Projects for Interactive Data Visualization with Processing
The Data Science of Sports Management
The Data Science of Economics, Banking, and Finance
The Data Science of Retail, Sales, and Commerce
The Data Science of Healthcare, Medicine, and Public Health
The Data Science of Educational Management and Policy
The Data Science of Government and Political Science
The Data Science of Media and Entertainment
The Data Science of Nonprofit Service Organizations
I love art. I believe that practicing art makes for a better data science practice. But mostly I love it because it’s art: it’s beautiful, rewarding, expressive, and fun. I have deep connections to art: I studied design for most of my undergraduate career; I’ve been involved with modern dance for years; I love poetry; I’m a regular patron of the opera; I play the saxophone – of which Gioachino Rossini exclaimed “This is the most beautiful sound I have ever heard!” – and I have a long-term plan for computer-assisted composition and live looping in the indie classical genre.
But it was only a few years ago, when I was on a sabbatical to learn more about data visualization, that I (re)discovered the connection between my data work and my aesthetic interests. I learned about “creative coding” and how to use visualization tools – Processing, in particular – to create art. I also learned how to use Max/MSP/Jitter to create music and, at the same time, capture, analyze, and transform video. These developments led to a surprising turn for a data person: showings in two galleries, a commission for a modern dance performance – in partnership with choreographer Jacque Bell (and my wife) – and two years at Utah Valley University working with a Presidential Fellowship to devise methods for live video looping in modern dance. And, through it all, I have maintained an abiding interest in using the lessons learned to enrich my data practice and teaching.