Webinar: Bridging the Gap: Statistical Rigor and the Promise of Data Science

About this event

📢 Join us for DataPhilly’s first event of the new year—a virtual session exploring how statistical foundations and modern data science can (and must) work together to build trustworthy, high-value analytics and AI systems.
This 45-minute webinar will unpack the tension between statistical rigor and the fast-moving promise of data science, offering practical insights for practitioners, leaders, and anyone navigating today’s rapidly evolving data landscape. We’ll wrap up with a 15-minute open discussion so you can ask questions and connect directly with the speaker.

Abstract
As data science increasingly takes center stage in applied estimation problems, a critical tension emerges: while data science offers powerful tools and better point estimates, it often does so by sacrificing the rigorous uncertainty quantification that traditional statistical methods provide. This talk explores how data science, particularly modern machine learning techniques like XGBoost, can augment—rather than replace—statistical theory to improve estimation practices.
We will begin by acknowledging a key limitation of data science: its theoretical foundations remain underdeveloped compared to classical statistics. As a result, despite its empirical strengths, data science still relies on statistical theory to ensure principled inference, especially when dealing with uncertainty. Through examples from statistical consulting and simulation-based modeling, we will illustrate why understanding convergence and variability is essential for sound decision-making.
Importantly, we will also examine practical trade-offs. Data science often imposes fewer assumptions than traditional statistical models, allowing greater flexibility and performance. However, this comes at the cost of interpretability and reliable confidence intervals—an issue particularly concerning in high-stakes fields like medicine, where uncertainty matters deeply. Ultimately, this talk advocates for a hybrid approach: using statistical theory as a foundation, and data science as a powerful augmentation. While not all traditional statistical tools remain essential, many do—and knowing when and how to combine them with modern techniques is the path forward.

Speaker Bio:
Raymond A. McCollum, Ph.D., is a data scientist and statistician with over 25 years of experience spanning defense, aerospace, and financial industries. His work includes applying statistics and data science to anti-submarine warfare for the U.S. Navy, conducting uncertainty analysis in heat shield measurements for NASA’s Mars Lander, and developing automated underwriting systems for Prudential Financial. Over his career—from entry-level analyst to Vice President of Data Science—he has been recognized for innovative problem-solving and creative analytic design. He discovered that the mathematics underlying data analysis remains consistent across disciplines, forming the foundation for both classical statistics and modern data science. As Founder and CEO of Statistical Data Science Solutions (StatDSS), he focuses on advancing statistical rigor in real-world data science applications.

🔗 Webinar access:
Link will be emailed to attendees who RSVP and posted on the meetup event page a few days before the event.

Reserve your spot, settle in wherever you are, and kick off the year with thoughtful conversation and learning!