The Division of Biostatistics in the Dalla Lana School of Public Health at the University of Toronto is pleased to host the 2025 SORA-TABA Annual Workshop. The event brings together regional and local statistical communities who are interested in biostatistics, artificial intelligence, data science, financial statistics, and other applied areas of statistics. Please join us in making this event a great success!
To register and learn more about the workshop, please visit: 2025 SORA-TABA Annual Workshop.
If you have any questions, please email Yeonkyung Namkoong at biostat.dlsph@utoronto.ca.
Veridical Data Science Online Workshop
Speaker: Rebecca Barter, PhD, Research Assistant Professor, Division of Epidemiology, University of Utah
Description:
The Veridical Data Science workshop introduces the PCS (Predictability, Computability, Stability) framework, pioneered by Prof. Bin Yu, to emphasize real-world validation and the role of human judgment in data analysis. Through live content and hands-on exercises, participants will explore how reality checks can strengthen data-driven insights and how judgment calls influence conclusions. The workshop covers techniques for assessing the trustworthiness of a wide range of analytical results using predictability and stability analyses. Participants will gain a deeper understanding of how to conduct trustworthy data analysis and the critical role of transparency in decision-making. This workshop is based on the open-source book “Veridical Data Science: The Practice of Responsible Data Analysis and Decision-Making” by Bin Yu and Rebecca Barter.
The intended audience is anyone who has experience conducting data analysis. Faculty, staff, postdoctoral fellows, and graduate students are welcome. No advanced statistical, mathematical, or coding background will be required. The final segment of the workshop will include discussion about prediction models, so having a basic understanding of linear regression would be helpful for this part.
The workshop will be software agnostic. It is intended to be relevant to a wide range of data analysis software, including Excel, Stata, R, Python, etc.
Program:
Module 1: Introduction to Veridical Data Science
- Introducing Veridical Data Science
- The PCS (Predictability, Computability, and Stability) framework
- Motivating example(s)
Module 2: Predictability assessments
- Types of predictability assessments for trustworthiness evaluation
- Predictability case study
Module 3: Stability
- Types of stability assessments for trustworthiness evaluation
- Prediction perturbation intervals and stability plots
- Stability assessment case study
Module 4: Full veridical data science case study and best practices
Speaker Bio:
Dr. Rebecca Barter is a Research Assistant Professor in the Division of Epidemiology at the University of Utah. As a statistician, data scientist, and educator, Dr Barter specializes in data science education and the analysis of complex healthcare data. Dr. Barter earned her PhD in Statistics from the University of California, Berkeley, in 2019, where she co-authored the book Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making with her advisor, Professor Bin Yu. In addition to her academic work, Dr. Barter shares data science resources and insights on her blog, www.rebeccabarter.com.
Agenda:
10:00-10:15 Opening Remarks
10:15-11:15 Module 1: Introduction to Veridical Data Science (1 hour)
11:15-11:30 Morning Break (15 mins)
11:30-12:30 Module 2: Predictability assessments (1 hour)
12:30-13:30 Lunch
13:30-15:00 Module 3: Stability (1.5 hours)
15:00-15:15 Afternoon Break (15 mins)
15:15-16:45 Module 4: Full veridical data science case study and best practices (1.5 hours)
16:45-17:00 Closing Remarks