About Rebecca

Hi there! I’m Rebecca. I’m so glad you’ve made it to my page.

I’m a statistician, data scientist, educator, and communicator who has a dual passion for empowering others by teaching critical thinking and technical skills for data science, and for uncovering the hidden patterns and stories that live inside complex datasets (primarily related to healthcare and medicine). I spend most of my time working towards improving current approaches to teaching statistics, data literacy and communication, both at introductory and advanced levels, and conducting deep dives into healthcare data, developing predictive models and producing explanatory data visualizations. I am currently a Research Assistant Professor at the University of Utah in Salt Lake City, where I teach data science across campus and am working to make healthcare data more accessible.

Originally from Australia, I moved to California in 2014 and graduated from my PhD in Statistics at UC Berkeley in December 2019, during which time I was advised primarily by Prof Bin Yu. During much of my PhD I was a Data Science Fellow at the Berkeley Institute for Data Science (BIDS). My PhD research focused on data science in healthcare (such as predicting surgical site infections using electronic health records, and using causal inference to investigate the impact of liver transplant wait time on survival).

As an experienced educator, certified RStudio instructor, and certified Software Carpentry instructor, I have been the primary lecture for an undergraduate statistics course at UC Berkeley, I have taught dozens of R and data science-related workshops, and have helped hundreds of thousands of people worldwide learn R and statistics through this blog.

My primary upcoming project is a book, “Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making”, that I am co-authoring with my PhD advisor and ongoing mentor, Professor Bin Yu. Veridical Data Science combines Professor Yu’s decades-long experience in the field of data science and her innovative Veridical Data Science framework with my enthusiasm for explaining complex data science topics in a simple and intuitive way. We hope that Veridical Data Science can be a practical handbook for new (and old) Data Scientists, highlighting the role of judgment calls and critical thinking in data science, and we hope that it will play a significant role in the education of the next generation of data scientists. Veridical Data Science will be published by MIT Press sometime in 2023. Stay tuned!

When I’m not doing R or data stuff, I’m usually found hiking, climbing rocks somewhere, or working some art project or another!

If you’ve found my blogs helpful and want to buy me a coffee or tea, feel free to contribute over at https://ko-fi.com/rlbarter.

If you want to get in touch, you can email me at rlbarter at gmail dot com