2025 Data Science in the Classroom Conference
Current Program
Event locations and abstracts for the invited talks can now be found in our detailed agenda!
| Time | Event |
|---|---|
| 8:00-8:30 | Breakfast and Sign-In |
| 8:30-9:15 | Introductory Remarks |
| 9:15-10:15 | Keynote Address: Insight and Impact |
| 10:15-10:30 | Break and Refreshments |
| 10:30-12:00 | Invited Talks: Educator-Driven Curriculum Innovation |
| 12:00-1:30 | Lunch |
| 1:30-2:30 | Special Invited Session: Transforming the Landscape of Data Science Education |
| 2:30-2:45 | Break and Refreshments |
| 2:45-3:30 | Panel Discussion: Creating Sustainable Pathways |
| 3:30-4:00 | Closing Remarks |
Keynote Address
Mine Çetinkaya-Rundel is Professor of the Practice and the Director of Undergraduate Studies at the Department of Statistical Science and an affiliated faculty in the Computational Media, Arts, and Cultures program at Duke University. Mine’s work focuses on innovation in statistics and data science pedagogy, with an emphasis on computing, reproducible research, student-centered learning, and open-source education.
The Future of Statistics Education: A Computational Perspective
Statistics education stands at a critical juncture as we navigate the intersection of traditional statistical theory, modern computational approaches, and emerging AI technologies. This talk examines how statisticians can reimagine curricula by embracing computation as foundational elements rather than afterthoughts. While traditional statistics education has prioritized theoretical frameworks and applications, computation has emerged as the backbone of contemporary data analysis—from data acquisition and wrangling to visualization, modeling, and communication. Now, AI tools are further transforming this landscape, creating both opportunities and challenges for statistics and data science educators. The presentation will outline a forward-looking curriculum model for introductory courses that balances statistical thinking, data science methods, and explicit computational instruction.
Invited Talks
Trailblazing educators are transforming the way that students experience data science, in response to evolving student and industry needs. Join us as these instructors share curricular innovations that ensure relevance, rigor, and long-term success.
| Instructor | Institution | Department |
|---|---|---|
| Sampson Akwafuo | California State University, Fullerton | Computer Science |
| Manuchehr Aminian | California State Polytechnic University, Pomona | Mathematics and Statistics |
| Alma Castro | Cypress College | Mathematics |
| Bridget Druken | California State University, Fullerton | Mathematics |
| Jessica Kramer | Santiago Canyon College | Mathematics |
| Brian Kwan | California State University, Long Beach | Health Science |
| Sunny Le | California State University, Fullerton | Mathematics |
| Thomas May | California State University, Fullerton | Economics |
| Edward Pineda | Los Angeles City College | Mathematics |
| Quiroz Quiroz | California State University, Fullerton | Geology |
| Colin Rundel | Duke University | Statistical Science |
| Serena Zadoorian | Moreno Valley Community College | Psychology |
Special Invited Session
Solomon Russell is Professor and Chair of Computer Science at El Camino College in Los Angeles, where he leads efforts to expand access to data science and computing for diverse student populations. He is Principal Investigator on two current grants: the 2023 California Learning Lab Data Science Grand Challenge, focused on faculty development in partnership with UC Berkeley, and a 2024 NSF grant supporting data science pathways at El Camino College. Dr. Russell has extensive experience in curriculum development, teacher professional development, and statewide policy, including serving on the California Computer Science Strategic Implementation Advisory Panel. He completed his doctorate at the University of Southern California in 2023, with research focused on community college data science curriculum design.
Data Science as Storytelling: Building Pathways, Programs, and Possibilities at El Camino College
Data science isn’t just about numbers or code—it’s about stories. Stories about people, communities, and decisions. At El Camino College, we’ve embraced this idea as the foundation for building our data science ecosystem. Our approach has combined computing, statistics, and storytelling to create meaningful opportunities for both students and faculty.
This special session talk will share how a community college became a hub for data-driven learning through intentional program design, grant-funded initiatives, and a commitment to access and equity. I will highlight our work developing a certificate in data science, creating new courses - including a practical data science course modeled after Berkeley’s Data 100 - and building a dual enrollment pathway that introduces high school students to data science early.
Equally important is our focus on faculty development. Through two major grants - the California Educational Learning Lab’s Data Science Grand Challenge and a National Science Foundation grant—we have supported instructors in learning how to teach data science in ways that center real-world stories and applications.
This talk will explore how data science education is ultimately about helping students - and educators - find, interpret, and tell stories with data. It’s about making meaning, building skills, and creating pathways to opportunity.
Panel Discussion
Without clear articulation and support structures, classroom innovation alone cannot endure. To build lasting programs, we must identify and address key bottlenecks at the student, faculty, and administrative levels.
How do we create sustainable pathways to help all students achieve their diverse educational and career goals? Our panel discusses opportunities for alignment among data science programs at community colleges, data science programs at the CSU/UC, and the industries into which graduates of those programs are entering.
| Panelist | Institution |
|---|---|
| Sam Behseta | Kaiser Permanente School of Medicine |
| Brenda Harlow | Rio Hondo College |
| Veronica Holbrook | Rio Hondo College |
| Jessica Jaynes | California State University, Fullerton |
| Babak Shahbaba | University of California, Irvine |
| Kseniya Usovich | University of California, Berkeley |
Continue the Conversation
We recognize that it’s hard to keep the momentum of a conference going once you’re back at your home institution. E-mails and social media posts get lost in the deluge, and by the time you have time to respond to them you’ve missed them entirely.
Our online Community of Data Science Learners web forum continues the conversations in a friendly, organized way. Post with the tag #DSC2025 to highlight the major insights you came away with, or reply to what others took away from the conference. No social media or app necessary! And if you can’t get to it until a week or a month after the conference, that’s totally fine - our community will still be reading and reacting.
Previous DSC Conferences
DSC 2024: New Programs & New Responsibilities
Project PIPE-LINE is a collaboration between California State University Fullerton, Riverside City College, Rio Hondo College, and University of California Irvine. Funded by California Learning Lab: Building Critical Mass for Data Science