Connecting the Dots in Data Science Education: Learning From Different Teaching Contexts

Districts and states recognize the ever-increasing role of data in making decisions in our everyday lives. To help address this, many are incorporating data science concepts into their curricula, both as stand-alone courses and as areas of emphasis in existing courses. Data science concepts allow additional opportunities for students to recognize the relevance and usefulness of mathematics and it is critical to be thoughtful in creating practices and policies. National Council of Teachers of Mathematics has recently released two position statements to provide guidance to the mathematics education community. This talk will give brief overviews of these as well as share updates on other work being done with incorporating data science into K-12. 

The Data Science & Society (DSS) initiative at Vassar College officially launched in 2022-23, in response to years of growing campus-wide interest in "data." A group of faculty representing 8 different departments and programs, including one DSS post-doctoral fellow, has led the initiative to cast a vision for DSS at Vassar. In its early stages, we have hosted focus groups on campus, visited peer institutions with data science programs, organized a colloquium series, offered technical workshops, and established a faculty-student small grant program. We are also developing courses and a curricular program that will meet the demand for education in DSS at Vassar, while maintaining the College's liberal arts mission. In this talk, we share more about each of these efforts. We also reflect on support received from our campus community, as well as challenges faced and lessons learned in this on-going and exciting work-in-progress.
 

Teaching data science, with its inherently interdisciplinary nature, presents educators with unique challenges in crafting content and instructional approaches. These challenges are further compounded by instructors holding terminal degrees in diverse data science fields, varying teaching experiences, and the diverse backgrounds of students. In this talk, I will share a range of examples from introductory data science instructors representing various North American colleges and universities. Drawing from these examples, we will explore the varied orientations educators adopt in teaching data science and the considerations influencing their content choices. Examining the challenges posed by different classroom sizes and students' diverse majors, our talk explores the nuances of curricular materials, teaching methods and assessment strategies. Emphasis will be placed on tailoring these elements to accommodate variations in student backgrounds, including factors such as prior knowledge, misconceptions and motivations to learn data science. By exploring these dimensions, my goal is to provide a glimpse into the practices of introductory data science courses across different contexts. 

With the field of data science continuing to grow, hands-on experience is a vital resource for students attending higher educational institutions. The SoCal Data Science Program is an NSF-funded cross-institutional program hosted at Cypress College (CC), California State University, Fullerton (CSUF), and the University of California, Irvine (UCI). The goal of this program is to foster a community of students and faculty from a range of institutions, including community college and teaching-focused and research universities, in data science through research opportunities. The program focuses on creating pathways including college to four-year college, college to industry, and college to graduate school. In this talk, I will share my perspective on navigating the pathway as a former CSUF fellow to a current UCI graduate student in statistics. I will highlight my current experience as a graduate student researcher in the program, mentoring students from all three institutions. By sharing my full-circle point of view, my goal is to shed light on the challenges and advantages of being an undergraduate student participant and mentoring undergraduate students in data science initiatives.